881 results on '"Statistical relational learning"'
Search Results
2. Hybrid attention mechanism for few‐shot relational learning of knowledge graphs
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Fangqing Guo, Ruixin Ma, Liang Zhao, and Zeyang Li
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One shot ,business.industry ,Computer science ,knowledge graph reasoning ,Computer applications to medicine. Medical informatics ,Statistical relational learning ,R858-859.7 ,one‐shot ,few‐shot ,QA76.75-76.765 ,Knowledge graph ,Shot (pellet) ,Computer Vision and Pattern Recognition ,Artificial intelligence ,Computer software ,business ,attention mechanism ,Software ,Mechanism (sociology) - Abstract
Few‐shot knowledge graph (KG) reasoning is the main focus in the field of knowledge graph reasoning. In order to expand the application fields of the knowledge graph, a large number of studies are based on a large number of training samples. However, we have learnt that there are actually many missing relationships or entities in the knowledge graph, and in most cases, there are not many training instances when implementing new relationships. To tackle it, in this study, the authors aim to predict a new entity given few reference instances, even only one training instance. A few‐shot learning framework based on a hybrid attention mechanism is proposed. The framework employs traditional embedding models to extract knowledge, and uses an attenuated attention network and a self‐attention mechanism to obtain the hidden attributes of entities. Thus, it can learn a matching metric by considering both the learnt embeddings and one‐hop graph structures. The experimental results present that the model has achieved significant performance improvements on the NELL‐One and Wiki‐One datasets.
- Published
- 2021
3. Pruning strategies for the efficient traversal of the search space in PILP environments
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Inês Dutra, Ricardo Rocha, and Joana Côrte-Real
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Probabilistic inductive logic programming ,business.industry ,Computer science ,Statistical relational learning ,Probabilistic logic ,Space (commercial competition) ,Machine learning ,computer.software_genre ,Human-Computer Interaction ,Tree traversal ,Inductive logic programming ,Artificial Intelligence ,Hardware and Architecture ,Artificial intelligence ,Pruning (decision trees) ,business ,Representation (mathematics) ,computer ,Software ,Information Systems - Abstract
Probabilistic inductive logic programming (PILP) is a statistical relational learning technique which extends inductive logic programming by considering probabilistic data. The ability to use probabilities to represent uncertainty comes at the cost of an exponential evaluation time when composing theories to model the given problem. For this reason, PILP systems rely on various pruning strategies in order to reduce the search space. However, to the best of the authors’ knowledge, there has been no systematic analysis of the different pruning strategies, how they impact the search space and how they interact with one another. This work presents a unified representation for PILP pruning strategies which enables end-users to understand how these strategies work both individually and combined and to make an informed decision on which pruning strategies to select so as to best achieve their goals. The performance of pruning strategies is evaluated both time and quality-wise in two state-of-the-art PILP systems with datasets from three different domains. Besides analysing the performance of the pruning strategies, we also illustrate the utility of PILP in one of the application domains, which is a real-world application.
- Published
- 2021
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4. A comparison of statistical relational learning and graph neural networks for aggregate graph queries
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Lise Getoor, Varun Embar, and Sriram Srinivasan
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Reduction (recursion theory) ,Theoretical computer science ,Computer science ,Node (networking) ,Aggregate (data warehouse) ,Probabilistic logic ,Statistical relational learning ,Inference ,Cohesion (computer science) ,02 engineering and technology ,Artificial Intelligence ,Joint probability distribution ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Software - Abstract
Statistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complexaggregate graph queries(AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation, we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.
- Published
- 2021
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5. Learning Structured and Causal Probabilistic Models for Computational Science
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Sridhar, Dhanya
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Artificial intelligence ,Causality ,Computational biology ,Computational social science ,Probabilistic modeling ,Statistical relational learning - Abstract
The drive to understand human phenomena such as our behavior and biology guides scientific discovery in the social and biological sciences. Today's wealth of observational and experimental data presents both opportunities and challenges for machine learning methods to facilitate these discoveries around human behavior and biology. Social media sites provide observational data, capturing snapshots of how users feel towards current events, engage in discourse with one another, and reflect on behavioral factors that affect their mood. These rich textual data support socio-behavioral modeling and understanding. In biology, large-scale experimental datasets are available, coupled with extensive efforts to extract and curate scientific ontologies and knowledge bases. Such empirical data enables inferences in pharmaceutical sciences and genetics. While standard machine learning methods build probabilistic models using social media posts or gene expression levels, they fall short on handling three important challenges in these problems. First, in socio-behavioral and biological domains, inferences are interrelated and require collective reasoning. Second, prior knowledge from multiple sources such as textual or experiment evidence are abundant and probabilistic methods must fuse these signals of varying fidelity. Third, to advance discoveries in social and biological sciences, computational methods must go beyond predictive performance. In both domains, experts seek new insights and knowledge, requiring techniques to discover patterns and causal relationships directly from data.My dissertation addresses the challenges of computational science domains by developing a unified probabilistic framework that: 1) exploits useful structure in the domain to make collective inferences; 2) fuses several sources of signals; 2) discovers causal structure; 4) enables learning of complex, structured models directly from data. I validate this framework on important scientific modeling problems such as online debate and dialogue, mood and behavioral choices, interactions between drug treatments, and gene regulation. In this thesis, I first develop structural patterns for collective inference by evaluating several modeling choices for online debates. My findings illustrate the harms of naive collective reasoning while showing the benefits of jointly modeling debate interactions and users' stances. I extend these collective patterns to fuse several sources of biological information which lead to state-of-the-art performance in drug-drug interaction prediction. To go beyond predictive performance, I combine multiple statistical signals to infer causal networks of gene regulation from measurements of gene expression and estimate causal effects in dialogue. Finally, I develop algorithms that learn these modeling patterns directly from data, showing the benefits of discovering complex dependencies in the drug-drug interaction prediction domain. The technical contributions highlighted in my thesis lay the foundation for applying structured and causal models to computational science. I conclude by outlining promising areas of future research that stem from my work and further bolster probabilistic methods for scientific domains.
- Published
- 2018
6. Knowledge Graph Completion via Complex Tensor Factorization.
- Author
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Trouillon, Théo, Dance, Christopher R., Gaussier, Éric, Welbl, Johannes, Riedel, Sebastian, and Bouchard, Guillaume
- Subjects
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STATISTICAL learning , *BIG data , *MACHINE learning , *ARTIFICIAL intelligence , *MACHINE theory , *DATA science , *HERMITIAN forms - Abstract
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs|labeled directed graphs| and predicting missing relationships|labeled edges. State-of-the-art embedding models propose different trade-offs between modeling expressiveness, and time and space complexity. We reconcile both expressiveness and complexity through the use of complex-valued embeddings and explore the link between such complex-valued embeddings and unitary diagonalization. We corroborate our approach theoretically and show that all real square matrices|thus all possible relation/adjacency matrices|are the real part of some unitarily diagonalizable matrix. This results opens the door to a lot of other applications of square matrices factorization. Our approach based on complex embeddings is arguably simple, as it only involves a Hermitian dot product, the complex counterpart of the standard dot product between real vectors, whereas other methods resort to more and more complicated composition functions to increase their expressiveness. The proposed complex embeddings are scalable to large data sets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.¹ [ABSTRACT FROM AUTHOR]
- Published
- 2017
7. Modeling Content and Context with Deep Relational Learning
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Dan Goldwasser and Maria Leonor Pacheco
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FOS: Computer and information sciences ,Linguistics and Language ,Computer Science - Computation and Language ,Artificial neural network ,Computer Science - Artificial Intelligence ,Interface (Java) ,Computer science ,Communication ,Statistical relational learning ,Inference ,Context (language use) ,02 engineering and technology ,Computer Science Applications ,Variety (cybernetics) ,Human-Computer Interaction ,Artificial Intelligence (cs.AI) ,Artificial Intelligence ,Human–computer interaction ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Representation (mathematics) ,Computation and Language (cs.CL) ,Natural language - Abstract
Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning., TACL pre-MIT Press version
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- 2021
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8. Explainable and unexpectable recommendations using relational learning on multiple domains
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Masayuki Numao, Sirawit Sopchoke, and Ken-ichi Fukui
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Cognitive science ,Artificial Intelligence ,Computer science ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Statistical relational learning ,020201 artificial intelligence & image processing ,02 engineering and technology ,Computer Vision and Pattern Recognition ,Theoretical Computer Science - Abstract
In this research, we combine relational learning with multi-domain to develop a formal framework for a recommendation system. The design of our framework aims at: (i) constructing general rules for recommendations, (ii) providing suggested items with clear and understandable explanations, (iii) delivering a broad range of recommendations including novel and unexpected items. We use relational learning to find all possible relations, including novel relations, and to form the general rules for recommendations. Each rule is represented in relational logic, a formal language, associating with probability. The rules are used to suggest the items, in any domain, to the user whose preferences or other properties satisfy the conditions of the rule. The information described by the rule serves as an explanation for the suggested item. It states clearly why the items are chosen for the users. The explanation is in if-then logical format which is unambiguous, less redundant and more concise compared to a natural language used in other explanation recommendation systems. The explanation itself can help persuade the user to try out the suggested items, and the associated probability can drive the user to make a decision easier and faster with more confidence. Incorporating information or knowledge from multiple domains allows us to broaden our search space and provides us with more opportunities to discover items which are previously unseen or surprised to a user resulting in a wide range of recommendations. The experiment results show that our proposed algorithm is very promising. Although the quality of recommendations provided by our framework is moderate, our framework does produce interesting recommendations not found in the primitive single-domain based system and with simple and understandable explanations.
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- 2020
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9. Propositionalization and embeddings: two sides of the same coin
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Marko Robnik-Šikonja, Blaž Škrlj, and Nada Lavrač
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer science ,Data transformation ,Statistical relational learning ,Machine Learning (stat.ML) ,02 engineering and technology ,Machine learning ,computer.software_genre ,External Data Representation ,Data type ,Article ,Machine Learning (cs.LG) ,Relational learning ,Artificial Intelligence ,Statistics - Machine Learning ,020204 information systems ,Component (UML) ,Knowledge graphs ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,Propositionalization ,business.industry ,Inductive logic programming ,Embeddings ,020201 artificial intelligence & image processing ,Data pre-processing ,Artificial intelligence ,business ,computer ,Software - Abstract
Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into the format required by a given learning algorithm. This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches. While both approaches aim at transforming data into tabular data format, they use different terminology and task definitions, are perceived to address different goals, and are used in different contexts. This paper contributes a unifying framework that allows for improved understanding of these two data transformation techniques by presenting their unified definitions, and by explaining the similarities and differences between the two approaches as variants of a unified complex data transformation task. In addition to the unifying framework, the novelty of this paper is a unifying methodology combining propositionalization and embeddings, which benefits from the advantages of both in solving complex data transformation and learning tasks. We present two efficient implementations of the unifying methodology: an instance-based PropDRM approach, and a feature-based PropStar approach to data transformation and learning, together with their empirical evaluation on several relational problems. The results show that the new algorithms can outperform existing relational learners and can solve much larger problems., Accepted in MLJ
- Published
- 2022
10. Logic Tensor Networks
- Author
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Samy Badreddine, Artur S. d'Avila Garcez, Michael Spranger, and Luciano Serafini
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FOS: Computer and information sciences ,QA75 ,Linguistics and Language ,Computer Science - Machine Learning ,I.2.4 ,Artificial neural network ,business.industry ,Computer science ,Computer Science - Artificial Intelligence ,I.2.6 ,Deep learning ,Statistical relational learning ,Semantics ,Fuzzy logic ,Language and Linguistics ,Signature (logic) ,Machine Learning (cs.LG) ,Artificial Intelligence (cs.AI) ,Artificial Intelligence ,RC0321 ,Artificial intelligence ,Cluster analysis ,business ,Abstraction (linguistics) - Abstract
Artificial Intelligence agents are required to learn from their surroundings and to reason about the knowledge that has been learned in order to make decisions. While state-of-the-art learning from data typically uses sub-symbolic distributed representations, reasoning is normally useful at a higher level of abstraction with the use of a first-order logic language for knowledge representation. As a result, attempts at combining symbolic AI and neural computation into neural-symbolic systems have been on the increase. In this paper, we present Logic Tensor Networks (LTN), a neurosymbolic formalism and computational model that supports learning and reasoning through the introduction of a many-valued, end-to-end differentiable first-order logic called Real Logic as a representation language for deep learning. We show that LTN provides a uniform language for the specification and the computation of several AI tasks such as data clustering, multi-label classification, relational learning, query answering, semi-supervised learning, regression and embedding learning. We implement and illustrate each of the above tasks with a number of simple explanatory examples using TensorFlow 2. Keywords: Neurosymbolic AI, Deep Learning and Reasoning, Many-valued Logic., 68 pages, 28 figures, 6 tables
- Published
- 2022
11. Automatic Data Repairs with Statistical Relational Learning
- Author
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Weibang Li, Zhen Zhang, Lidong Zhu, Ling Li, and Chengjie Li
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business.industry ,Computer science ,Statistical relational learning ,Artificial intelligence ,computer.software_genre ,business ,computer ,Natural language processing - Published
- 2021
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12. Statistical Relational Learning: A State-of-the-Art Review
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Marenglen Biba and Muhamet Kastrati
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Matematik ,Probabilistic inductive logic programming ,business.industry ,Computer science ,Statistical relational learning ,State of the art review ,computer.software_genre ,Variety (cybernetics) ,Inductive logic programming ,Statistical relational learning,probabilistic graphical models,inductive logic programming,probabilistic inductive logic programming ,Modeling and Simulation ,Key (cryptography) ,Artificial intelligence ,Graphical model ,business ,computer ,Noisy data ,Mathematics ,Natural language processing - Abstract
The objective of this paper is to review the state-of-the-art of statistical relational learning (SRL) models developed to deal with machine learning and data mining in relational domains in presence of missing, partially observed, and/or noisy data. It starts by giving a general overview of conventional graphical models, first-order logic and inductive logic programming approaches as needed for background. The historical development of each SRL key model is critically reviewed. The study also focuses on the practical application of SRL techniques to a broad variety of areas and their limitations.
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- 2019
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13. Visual-Semantic Graph Reasoning for Pedestrian Attribute Recognition
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Xin Zhao, Kaiqi Huang, Qiaozhe Li, and Ran He
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021110 strategic, defence & security studies ,Computer science ,business.industry ,Image quality ,0211 other engineering and technologies ,Statistical relational learning ,02 engineering and technology ,General Medicine ,Pedestrian ,Machine learning ,computer.software_genre ,Graph ,Spatial relation ,0202 electrical engineering, electronic engineering, information engineering ,Embedding ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer - Abstract
Pedestrian attribute recognition in surveillance is a challenging task due to poor image quality, significant appearance variations and diverse spatial distribution of different attributes. This paper treats pedestrian attribute recognition as a sequential attribute prediction problem and proposes a novel visual-semantic graph reasoning framework to address this problem. Our framework contains a spatial graph and a directed semantic graph. By performing reasoning using the Graph Convolutional Network (GCN), one graph captures spatial relations between regions and the other learns potential semantic relations between attributes. An end-to-end architecture is presented to perform mutual embedding between these two graphs to guide the relational learning for each other. We verify the proposed framework on three large scale pedestrian attribute datasets including PETA, RAP, and PA100k. Experiments show superiority of the proposed method over state-of-the-art methods and effectiveness of our joint GCN structures for sequential attribute prediction.
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- 2019
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14. Relational social recommendation: Application to the academic domain
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Einat Minkov, Peter Brusilovsky, Tsvi Kuflik, Saeed Amal, and Chun-Hua Tsai
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0209 industrial biotechnology ,Social graph ,Information retrieval ,Social network ,Serendipity ,business.industry ,Computer science ,General Engineering ,Statistical relational learning ,02 engineering and technology ,Recommender system ,computer.software_genre ,Computer Science Applications ,Set (abstract data type) ,Information extraction ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Relevance (information retrieval) ,business ,computer - Abstract
This paper outlines RSR, a relational social recommendation approach applied to a social graph comprised of relational entity profiles. RSR uses information extraction and learning methods to obtain relational facts about persons of interest from the Web, and generates an associative entity-relation social network from their extracted personal profiles. As a case study, we consider the task of peer recommendation at scientific conferences. Given a social graph of scholars, RSR employs graph similarity measures to rank conference participants by their relatedness to a user. Unlike other recommender systems that perform social rankings, RSR provides the user with detailed supporting explanations in the form of relational connecting paths. In a set of user studies, we collected feedbacks from participants onsite of scientific conferences, pertaining to RSR quality of recommendations and explanations. The feedbacks indicate that users appreciate and benefit from RSR explainability features. The feedbacks further indicate on recommendation serendipity using RSR, having it recommend persons of interest who are not apriori known to the user, oftentimes exposing surprising inter-personal associations. Finally, we outline and assess potential gains in recommendation relevance and serendipity using path-based relational learning within RSR.
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- 2019
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15. Contrasting logical sequences in multi-relational learning
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João Gama, Carlos Abreu Ferreira, and Vítor Santos Costa
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Sequence ,Theoretical computer science ,Computer science ,Process (computing) ,Statistical relational learning ,Brute-force search ,Computational intelligence ,02 engineering and technology ,Predicate (mathematical logic) ,Discriminative model ,Artificial Intelligence ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Beam search ,020201 artificial intelligence & image processing - Abstract
In this paper, we present the BeamSouL sequence miner that finds sequences of logical atoms. This algorithm uses a levelwise hybrid search strategy to find a subset of contrasting logical sequences available in a SeqLog database. The hybrid search strategy runs an exhaustive search, in the first phase, followed by a beam search strategy. In the beam search phase, the algorithm uses the confidence metric to select the top k sequential patterns that will be specialized in the next level. Moreover, we develop a first-order logic classification framework that uses predicate invention technique to include the BeamSouL findings in the learning process. We evaluate the performance of our proposals using four multi-relational databases. The results are promising, and the BeamSouL algorithm can be more than one order of magnitude faster than the baseline and can find long and highly discriminative contrasting sequences.
- Published
- 2019
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16. Online probabilistic theory revision from examples with ProPPR
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Victor Guimaraes, Aline Paes, and Gerson Zaverucha
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Point (typography) ,Computer science ,Relational database ,business.industry ,Statistical relational learning ,Probabilistic logic ,02 engineering and technology ,Machine learning ,computer.software_genre ,Task (project management) ,Knowledge graph ,Artificial Intelligence ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Statistical theory ,business ,computer ,Software - Abstract
Handling relational data streams has become a crucial task, given the availability of pervasive sensors and Internet-produced content, such as social networks and knowledge graphs. In a relational environment, this is a particularly challenging task, since one cannot assure that the streams of examples are independent along the iterations. Thus, most relational learning systems are still designed to learn only from closed batches of data. Furthermore, in case there is a previously acquired model, these systems either would discard it or assuming it as correct. In this work, we propose an online relational learning algorithm that can handle continuous, open-ended streams of relational examples as they arrive. We employ techniques of theory revision to take advantage of the previously acquired model as a starting point, by finding where it should be modified to cope with the new examples, and automatically update it. We rely on the Hoeffding’s bound statistical theory to decide if the model must, in fact, be updated in accordance with the new examples. The proposed algorithm is built upon ProPPR statistical relational language, aiming at contemplating the uncertainty inherent to real data. Experimental results in social networks and entity co-reference datasets show the potential of the proposed approach compared to other relational learners.
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- 2019
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17. Semi-supervised online structure learning for composite event recognition
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Georgios Paliouras, Evangelos Michelioudakis, and Alexander Artikis
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Minimisation (psychology) ,business.industry ,Computer science ,Statistical relational learning ,02 engineering and technology ,Semi-supervised learning ,Machine learning ,computer.software_genre ,Task (project management) ,Activity recognition ,Artificial Intelligence ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Benchmark (computing) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Event calculus ,computer ,Software ,Hoeffding's inequality - Abstract
Online structure learning approaches, such as those stemming from statistical relational learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph-cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph-cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system.
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- 2019
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18. Software Vulnerabilities, Products and Exploits: A Statistical Relational Learning Approach
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Joao Gabriel Lopes, Caina Figueiredo, Leandro Pfleger de Aguiar, Gerson Zaverucha, Rodrigo Azevedo, and Daniel Sadoc Menasche
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Matching (statistics) ,Exploit ,Computer science ,business.industry ,National Vulnerability Database ,Statistical relational learning ,Industrial control system ,Machine learning ,computer.software_genre ,Knowledge-based systems ,Software ,Leverage (statistics) ,Artificial intelligence ,business ,computer - Abstract
Data on software vulnerabilities, products and exploits is typically collected from multiple non-structured sources. Valuable information, e.g., on which products are affected by which exploits, is conveyed by matching data from those sources, i.e., through their relations. In this paper, we leverage this simple albeit unexplored observation to introduce a statistical relational learning (SRL) approach for the analysis of vulnerabilities, products and exploits. In particular, we focus on the problem of determining the existence of an exploit for a given product, given information about the relations between products and vulnerabilities, and vulnerabilities and exploits, focusing on Industrial Control Systems (ICS), the National Vulnerability Database and ExploitDB. Using RDN-Boost, we were able to reach an AUC ROC of 0.83 and an AUC PR of 0.69 for the problem at hand. To reach that performance, we indicate that it is instrumental to include textual features, e.g., extracted from the description of vulnerabilities, as well as structured information, e.g., about product categories. In addition, using interpretable relational regression trees we report simple rules that shed insight on factors impacting the weaponization of ICS products.
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- 2021
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19. Prediction of COVID-19 Infection Based on Symptoms and Social Life Using Machine Learning Techniques
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Stefanos Zervoudakis, Emmanouil Marakakis, Haridimos Kondylakis, and Stefanos Goumas
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2019-20 coronavirus outbreak ,Exploit ,Coronavirus disease 2019 (COVID-19) ,business.industry ,Computer science ,media_common.quotation_subject ,Statistical relational learning ,Bayesian inference ,Machine learning ,computer.software_genre ,Social life ,Social media ,Quality (business) ,Artificial intelligence ,business ,computer ,media_common - Abstract
COVID-19 pandemic has affected nearly every aspect of life. Observing online the spread of the virus can offer a complementary view to the cases that are daily officially recorded and reported. In this article, we present an approach that exploits information available on social media to predict whether a patient has been infected with COVID-19. Our approach is based on a Bayesian model that is trained using data collected online. Then the trained model can be used for evaluating the possibility that new patients are infected with COVID-19. The experimental evaluation presented shows the high quality of our approach. In addition, our model can be incrementally retrained, so that it becomes more robust in an efficient way.
- Published
- 2021
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20. Scalable and Usable Relational Learning With Automatic Language Bias
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Sudhanshu Pathak, John D. Davis, Alan Fern, Praveen Ilango, Arash Termehchy, and Jose Picado
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Structure (mathematical logic) ,Computer science ,business.industry ,Relational database ,Statistical relational learning ,Probabilistic logic ,02 engineering and technology ,Trial and error ,Machine learning ,computer.software_genre ,Set (abstract data type) ,020204 information systems ,Scalability ,0202 electrical engineering, electronic engineering, information engineering ,Overhead (computing) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer - Abstract
A large body of machine learning and AI is focused on learning models composed of (probabilistic) logical rules, i.e., relational models, over relational databases and knowledge bases. To learn effective relational models over the huge space of possible ones efficiently, users of the current learning systems must restrict the structure of the candidate models using language bias. ML experts have to spend a long time inspecting the data and performing many rounds of trial and error to develop an effective language bias. We propose AutoBias, a system that leverages information in the underlying data to generate the language bias. As its induced language bias may not restrict the set of candidate models as tightly as the manually-written ones, learning may not scale to large datasets. Thus, we design novel and efficient methods to sample and learn effective relational models over large data. Our extensive empirical study shows that AutoBias delivers the same accuracy as using manually-written language bias by imposing only a slight overhead on the learning time.
- Published
- 2021
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21. Asymmetric learning facilitates human inference of transitive relations
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Charley M. Wu, Simon Ciranka, Clara Wicharz, Ivan Padezhki, Juan Linde-Domingo, and Bernhard Spitzer
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Transitive relation ,Empirical research ,Computer science ,business.industry ,Simple (abstract algebra) ,Statistical relational learning ,Psychophysics ,Reinforcement learning ,Inference ,Cognition ,Artificial intelligence ,business - Abstract
Humans and other animals are capable of inferring never-experienced relations (e.g., A>C) from other relational observations (e.g., A>B and B>C). The processes behind such transitive inference are subject to intense research. Here, we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be accomplished through simple reinforcement learning mechanisms. We show in simulations that inference of novel relations benefits from an asymmetric learning policy, where observers update only their belief about the winner (or loser) in a pair. Across 4 experiments (n=145), we find substantial empirical support for such asymmetries in inferential learning. The learning policy favoured by our simulations and experiments gives rise to a compression of values which is routinely observed in psychophysics and behavioural economics. In other words, a seemingly biased learning strategy that yields well-known cognitive distortions can be beneficial for transitive inferential judgments.
- Published
- 2021
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22. Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit
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Ariful Azad, Benjamin S. Glicksberg, Ying Ding, Jessica K De Freitas, Akhil Vaid, Sulaiman Somani, Tingyi Wanyan, Riccardo Miotto, and Girish N. Nadkarni
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0301 basic medicine ,Information Systems and Management ,Computer science ,relational learning ,Statistical relational learning ,010501 environmental sciences ,Machine learning ,computer.software_genre ,01 natural sciences ,Article ,Data modeling ,03 medical and health sciences ,Similarity (psychology) ,Electronic health records ,Representation (mathematics) ,0105 earth and related environmental sciences ,heterogeneous graph model ,Modalities ,Receiver operating characteristic ,business.industry ,Deep learning ,COVID-19 ,deep learning ,mortality ,030104 developmental biology ,machine learning ,Softmax function ,ICU ,Artificial intelligence ,business ,LSTM ,computer ,embeddings ,Information Systems - Abstract
Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COVID-19) outcomes using Electronic Health Record (EHR) data partially due to not effectively capturing the inter-connectivity patterns between various data modalities. In this work, we propose a novel framework that utilizes relational learning based on a heterogeneous graph model (HGM) for predicting mortality at different time windows in COVID-19 patients within the intensive care unit (ICU). We utilize the EHRs of one of the largest and most diverse patient populations across five hospitals in major health system in New York City. In our model, we use an LSTM for processing time varying patient data and apply our proposed relational learning strategy in the final output layer along with other static features. Here, we replace the traditional softmax layer with a Skip-Gram relational learning strategy to compare the similarity between a patient and outcome embedding representation. We demonstrate that the construction of a HGM can robustly learn the patterns classifying patient representations of outcomes through leveraging patterns within the embeddings of similar patients. Our experimental results show that our relational learning-based HGM model achieves higher area under the receiver operating characteristic curve (auROC) than both comparator models in all prediction time windows, with dramatic improvements to recall.
- Published
- 2021
23. Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
- Author
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Jian Sun, Hao Wang, Fei Huang, Luo Si, Chengguang Tang, Qiao Liu, Jian Dai, Ruiying Geng, Guanglin Niu, and Yang Li
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FOS: Computer and information sciences ,Computer Science - Computation and Language ,Meta learning (computer science) ,Relation (database) ,business.industry ,Computer science ,Computer Science - Artificial Intelligence ,Statistical relational learning ,Semantics ,computer.software_genre ,Machine learning ,News aggregator ,Artificial Intelligence (cs.AI) ,Metric (mathematics) ,Benchmark (computing) ,Noise (video) ,Artificial intelligence ,business ,computer ,Computation and Language (cs.CL) - Abstract
Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot knowledge graph completion (FKGC) has recently gained more research interests. Some existing models employ a few-shot relation's multi-hop neighbor information to enhance its semantic representation. However, noise neighbor information might be amplified when the neighborhood is excessively sparse and no neighbor is available to represent the few-shot relation. Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances. Thus, inferring complex relations in the few-shot scenario is difficult for FKGC models due to limited training instances. In this paper, we propose a few-shot relational learning with global-local framework to address the above issues. At the global stage, a novel gated and attentive neighbor aggregator is built for accurately integrating the semantics of a few-shot relation's neighborhood, which helps filtering the noise neighbors even if a KG contains extremely sparse neighborhoods. For the local stage, a meta-learning based TransH (MTransH) method is designed to model complex relations and train our model in a few-shot learning fashion. Extensive experiments show that our model outperforms the state-of-the-art FKGC approaches on the frequently-used benchmark datasets NELL-One and Wiki-One. Compared with the strong baseline model MetaR, our model achieves 5-shot FKGC performance improvements of 8.0% on NELL-One and 2.8% on Wiki-One by the metric Hits@10., Comment: The full version of a paper accepted to SIGIR 2021
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- 2021
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24. Learning to Solve NLP Tasks in an Incremental Number of Languages
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Giuseppe Castellucci, Simone Filice, Roberto Basili, and Danilo Croce
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Forgetting ,business.industry ,Computer science ,Statistical relational learning ,Retraining ,computer.software_genre ,Sequence labeling ,Constructed language ,Quality constraint ,Artificial intelligence ,business ,Set (psychology) ,computer ,Sentence ,Natural language processing - Abstract
In real scenarios, a multilingual model trained to solve NLP tasks on a set of languages can be required to support new languages over time. Unfortunately, the straightforward retraining on a dataset containing annotated examples for all the languages is both expensive and time-consuming, especially when the number of target languages grows. Moreover, the original annotated material may no longer be available due to storage or business constraints. Re-training only with the new language data will inevitably result in Catastrophic Forgetting of previously acquired knowledge. We propose a Continual Learning strategy that updates a model to support new languages over time, while maintaining consistent results on previously learned languages. We define a Teacher-Student framework where the existing model “teaches” to a student model its knowledge about the languages it supports, while the student is also trained on a new language. We report an experimental evaluation in several tasks including Sentence Classification, Relational Learning and Sequence Labeling.
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- 2021
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25. Propositionalization of Relational Data
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Vid Podpečan, Nada Lavrač, and Marko Robnik-Šikonja
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Computer science ,Relational database ,business.industry ,Statistical relational learning ,Representation (systemics) ,Learning models ,computer.software_genre ,Task (project management) ,Inductive logic programming ,Research community ,Artificial intelligence ,business ,computer ,Natural language processing - Abstract
Relational learning addresses the task of learning models or patterns from relational data. Complementary to relational learning approaches that learn directly from relational data, developed in the Inductive Logic Programming research community, this chapter addresses the propositionalization approach of first transforming a relational database into a single-table representation, followed by a model or pattern construction step using a standard machine learning algorithm.
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- 2021
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26. DVAMN: Dual Visual Attention Matching Network for Zero-Shot Action Recognition
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Zhiyong Feng, Cheng Qi, Meng Xing, and Yong Su
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Matching (statistics) ,Action (philosophy) ,Computer science ,business.industry ,Association (object-oriented programming) ,Visual space ,Key (cryptography) ,Statistical relational learning ,Pattern recognition ,Artificial intelligence ,DUAL (cognitive architecture) ,business ,Domain (software engineering) - Abstract
Zero-Shot Action Recognition (ZSAR) aims to transfer knowledge from a source domain to a target domain so that the unlabelled action can be inferred and recognized. However, previous methods often fail to highlight information about the salient factors of the video sequence. In the process of cross-modal search, information redundancy will weaken the association of key information among different modes. In this paper, we propose Dual Visual Attention Matching Network (DVAMN) to distill sparse saliency information from action video. We utilize dual visual attention mechanism and spatiotemporal Gated Recurrent Units (GRU) to establish irredundant and sparse visual space, which can boost the performance of the cross-modal recognition. Relational learning strategy is employed for final classification. Moreover, the whole network is trained in an end-to-end manner. Experiments on both the HMDB51 and the UCF101 datasets show that the proposed architecture achieves state-of-the-art results among methods using only spatial and temporal video features in zero-shot action recognition.
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- 2021
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27. A relational-learning perspective to multi-label chest x-ray classification
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Jens Kleesiek, Brandon Malone, Anjany Sekuboyina, and Daniel Oñoro-Rubio
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Dependency (UML) ,Computer Science - Artificial Intelligence ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Statistical relational learning ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Medizin ,Machine Learning (cs.LG) ,030218 nuclear medicine & medical imaging ,Image (mathematics) ,Domain (software engineering) ,03 medical and health sciences ,0302 clinical medicine ,Discriminative model ,business.industry ,Perspective (graphical) ,Pattern recognition ,Construct (python library) ,Artificial Intelligence (cs.AI) ,ComputingMethodologies_PATTERNRECOGNITION ,Artificial intelligence ,business ,Encoder ,030217 neurology & neurosurgery - Abstract
Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i.e. learning to map an image directly to its binary labels. Such approaches make it challenging to incorporate auxiliary information such as annotation uncertainty or a dependency among the labels. Building towards this, we propose a novel knowledge graph reformulation of multi-label classification, which not only readily increases predictive performance of an encoder but also serves as a general framework for introducing new domain knowledge. Specifically, we construct a multi-modal knowledge graph out of the chest X-ray images and its labels and pose multi-label classification as a link prediction problem. Incorporating auxiliary information can then simply be achieved by adding additional nodes and relations among them. When tested on a publicly-available radiograph dataset (CheXpert), our relational-reformulation using a naive knowledge graph outperforms the state-of-art by achieving an area-under-ROC curve of 83.5%, an improvement of "sim 1" over a purely discriminative approach.
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- 2021
28. Machine Learning Background
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Nada Lavrač, Marko Robnik-Šikonja, and Vid Podpečan
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business.industry ,Relational database ,Computer science ,Section (typography) ,Statistical relational learning ,Machine learning ,computer.software_genre ,Data type ,Sketch ,Terminology ,Software ,Categorization ,Artificial intelligence ,business ,computer - Abstract
This chapter provides an introduction to standard machine learning approaches that learn from tabular data representations, followed by an outline of approaches using various other data types addressed in this monograph: texts, relational databases, and networks (graphs, knowledge graphs, and ontologies). We first briefly sketch the historical outline of the research area, establish the basic terminology, and categorize learning tasks in Sect. 2.1. Section 2.2 provides a short introduction to text mining. Section 2.3 introduces relational learning techniques, followed by a brief introduction to network analysis, including semantic data mining, in Sect. 2.4. The means for evaluating the performance of machine learning algorithms, when used for prediction and rule quality estimation, are outlined in Sect. 2.5. We outline selected data mining techniques and platforms in Sect. 2.6. Finally, Sect. 2.7 presents the implemented software that allows for running selected methods on illustrative examples.
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- 2021
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29. Inductive logic programming at 30
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Andrew Cropper, Stephen Muggleton, Richard Evans, and Sebastijan Dumancic
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer science ,Programming language ,Statistical relational learning ,Predicate (mathematical logic) ,computer.software_genre ,Focus (linguistics) ,Machine Learning (cs.LG) ,TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES ,Artificial Intelligence (cs.AI) ,Inductive logic programming ,Artificial Intelligence ,Logic program ,computer ,Software ,Program synthesis - Abstract
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research., Comment: Extension of IJCAI20 survey paper. Accepted for the MLJ. arXiv admin note: substantial text overlap with arXiv:2002.11002, arXiv:2008.07912
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- 2021
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30. Inferring Semantic Object Affordances from Videos
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Rupam Bhattacharyya, Shyamanta M. Hazarika, and Zubin Bhuyan
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0209 industrial biotechnology ,business.industry ,Computer science ,Statistical relational learning ,Learning object ,02 engineering and technology ,Ontology (information science) ,Object (computer science) ,Qualitative reasoning ,020901 industrial engineering & automation ,Human–computer interaction ,0202 electrical engineering, electronic engineering, information engineering ,Robot ,020201 artificial intelligence & image processing ,Artificial intelligence ,Markov logic network ,business ,Affordance - Abstract
Lately there has been an increasing interest in learning object affordances. This is particularly to address human activity understanding and intention recognition for household robots. However, most existing approaches do not concentrate on imbibing new perceptual models of inferring affordances from visual input. Such models are key for object usage by household robots. Towards this goal, this paper introduces a knowledge based approach to inferring semantic object affordances. This is achieved by integrating ontology and qualitative reasoning within a statistical relational learning scheme. Encouraging results are obtained for the CAD-120 dataset consisting of indoor household activity videos involving human-object interactions.
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- 2021
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31. Few-Shot Induction of Generalized Logical Concepts via Human Guidance
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Sriraam Natarajan, Nandini Ramanan, Janardhan Rao Doppa, and Mayukh Das
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Robotics and AI ,Cognitive systems ,Theoretical computer science ,Knowledge representation and reasoning ,Computer science ,relational learning ,Small number ,lcsh:Mechanical engineering and machinery ,Statistical relational learning ,lcsh:QA75.5-76.95 ,Computer Science Applications ,Inductive logic programming ,Artificial Intelligence ,Leverage (statistics) ,knowledge representation and reasoning ,lcsh:TJ1-1570 ,cognitive systems ,lcsh:Electronic computers. Computer science ,logics for knowledge representation ,human in the loop (HITL) ,Original Research - Abstract
We consider the problem of learning generalized first-order representations of concepts from a small number of examples. We augment an inductive logic programming learner with two novel contributions. First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization. Second, we leverage richer human inputs in the form of advice to improve the sample-efficiency of learning. We prove that the proposed distance measure is semantically valid and use that to derive a PAC bound. Our experiments on diverse learning tasks demonstrate both the effectiveness and efficiency of our approach.
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- 2020
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32. Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning.
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McGovern, Amy, Gagne, David, Williams, John, Brown, Rodger, and Basara, Jeffrey
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WEATHER forecasting ,THUNDERSTORM forecasting ,ARTIFICIAL intelligence ,MACHINE learning ,SPATIOTEMPORAL processes ,EMBEDDED computer systems ,ARCHITECTURE Analysis & Design Language - Abstract
Severe weather, including tornadoes, thunderstorms, wind, and hail annually cause significant loss of life and property. We are developing spatiotemporal machine learning techniques that will enable meteorologists to improve the prediction of these events by improving their understanding of the fundamental causes of the phenomena and by building skillful empirical predictive models. In this paper, we present significant enhancements of our Spatiotemporal Relational Probability Trees that enable autonomous discovery of spatiotemporal relationships as well as learning with arbitrary shapes. We focus our evaluation on two real-world case studies using our technique: predicting tornadoes in Oklahoma and predicting aircraft turbulence in the United States. We also discuss how to evaluate success for a machine learning algorithm in the severe weather domain, which will enable new methods such as ours to transfer from research to operations, provide a set of lessons learned for embedded machine learning applications, and discuss how to field our technique. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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33. Link classification with probabilistic graphs.
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Di Mauro, Nicola, Taranto, Claudio, and Esposito, Floriana
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PROBABILITY theory ,GRAPH theory ,MACHINE learning ,ARTIFICIAL intelligence ,REPRESENTATION theory ,LOGISTIC regression analysis - Abstract
The need to deal with the inherent uncertainty in real-world relational or networked data leads to the proposal of new probabilistic models, such as probabilistic graphs. Every edge in a probabilistic graph is associated with a probability whose value represents the likelihood of its existence, or the strength of the relation between the entities it connects. The aim of this paper is to propose two machine learning techniques for the link classification problem in relational data exploiting the probabilistic graph representation. Both the proposed methods will exploit a language-constrained reachability method to infer the probability of possible hidden relationships that may exists between two nodes in a probabilistic graph. Each hidden relationships between two nodes may be viewed as a feature (or a factor), and its corresponding probability as its weight, while an observed relationship is considered as a positive instance for its corresponding link label. Given a training set of observed links, the first learning approach is to use a propositionalization technique adopting a L2-regularized Logistic Regression to learn a model able to predict unobserved link labels. Since in some cases the edges' probability may be not known in advance or they could not be precisely defined for a classification task, the second xposed approach is to exploit the inference method and to use a mean squared technique to learn the edges' probabilities. Both the proposed methods have been evaluated on real world data sets and the corresponding results proved their validity. [ABSTRACT FROM AUTHOR]
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- 2014
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34. HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification
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Zhihua Zhu, Xiaokai Chu, Jingping Bi, and Xinxin Fan
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Hierarchy ,Theoretical computer science ,Computer science ,business.industry ,Deep learning ,Statistical relational learning ,02 engineering and technology ,Graph ,Categorization ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,Kripke semantics ,Artificial intelligence ,business - Abstract
Collective classification, as an important technique to study networked data, aims to exploit the label autocorrelation for a group of inter-connected entities with complex dependencies. As the emergence of various heterogeneous information networks (HINs), collective classification at present is confronting several severe challenges stemming from the heterogeneity of HINs, such as complex relational hierarchy, potential incompatible semantics and node-context relational semantics. To address the challenges, in this paper, we propose a novel heterogeneous graph convolutional network-based deep learning model, called HGCN, to collectively categorize the entities in HINs. Our work involves three primary contributions: i) HGCN not only learns the latent relations from the relation-sophisticated HINs via multi-layer heterogeneous convolutions, but also captures the semantic incompatibility among relations with properly-learned edge-level filter parameters; ii) to preserve the fine-grained relational semantics of different-type nodes, we propose a heterogeneous graph convolution to directly tackle the original HINs without any in advance transforming the network from heterogeneity to homogeneity; iii) we perform extensive experiments using four real-world datasets to validate our proposed HGCN, the multi-facet results show that our proposed HGCN can significantly improve the performance of collective classification compared with the state-of-the-art baseline methods.
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- 2020
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35. Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning
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Wentao Bao, Qi Yu, and Yu Kong
- Subjects
FOS: Computer and information sciences ,Computer science ,business.industry ,Computer Vision and Pattern Recognition (cs.CV) ,Statistical relational learning ,Computer Science - Computer Vision and Pattern Recognition ,Crash ,Machine learning ,computer.software_genre ,Ranking ,Anticipation (artificial intelligence) ,Graph (abstract data type) ,Leverage (statistics) ,Artificial intelligence ,business ,Feature learning ,computer ,Sensory cue - Abstract
Traffic accident anticipation aims to predict accidents from dashcam videos as early as possible, which is critical to safety-guaranteed self-driving systems. With cluttered traffic scenes and limited visual cues, it is of great challenge to predict how long there will be an accident from early observed frames. Most existing approaches are developed to learn features of accident-relevant agents for accident anticipation, while ignoring the features of their spatial and temporal relations. Besides, current deterministic deep neural networks could be overconfident in false predictions, leading to high risk of traffic accidents caused by self-driving systems. In this paper, we propose an uncertainty-based accident anticipation model with spatio-temporal relational learning. It sequentially predicts the probability of traffic accident occurrence with dashcam videos. Specifically, we propose to take advantage of graph convolution and recurrent networks for relational feature learning, and leverage Bayesian neural networks to address the intrinsic variability of latent relational representations. The derived uncertainty-based ranking loss is found to significantly boost model performance by improving the quality of relational features. In addition, we collect a new Car Crash Dataset (CCD) for traffic accident anticipation which contains environmental attributes and accident reasons annotations. Experimental results on both public and the newly-compiled datasets show state-of-the-art performance of our model. Our code and CCD dataset are available at https://github.com/Cogito2012/UString., Accepted by ACM MM 2020
- Published
- 2020
36. Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring
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Pedro Zuidberg Dos Martires, Amy Loutfi, Luc De Raedt, Andreas Persson, and Nitesh Kumar
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FOS: Computer and information sciences ,0209 industrial biotechnology ,perceptual anchoring ,Computer Science - Artificial Intelligence ,Computer science ,statistical relational learning ,lcsh:Mechanical engineering and machinery ,relational particle filtering ,Statistical relational learning ,Inference ,02 engineering and technology ,lcsh:QA75.5-76.95 ,020901 industrial engineering & automation ,Artificial Intelligence ,semantic world modeling ,0202 electrical engineering, electronic engineering, information engineering ,lcsh:TJ1-1570 ,Representation (mathematics) ,Set (psychology) ,object tracking ,Original Research ,Robotics and AI ,business.industry ,Probabilistic logic ,probabilistic anchoring ,Semantic reasoner ,Object (computer science) ,Computer Science Applications ,Artificial Intelligence (cs.AI) ,probabilistic rule learning ,020201 artificial intelligence & image processing ,lcsh:Electronic computers. Computer science ,Artificial intelligence ,probabilistic logic programming ,business ,Symbol (formal) - Abstract
Robotic agents should be able to learn from sub-symbolic sensor data and, at the same time, be able to reason about objects and communicate with humans on a symbolic level. This raises the question of how to overcome the gap between symbolic and sub-symbolic artificial intelligence. We propose a semantic world modeling approach based on bottom-up object anchoring using an object-centered representation of the world. Perceptual anchoring processes continuous perceptual sensor data and maintains a correspondence to a symbolic representation. We extend the definitions of anchoring to handle multi-modal probability distributions and we couple the resulting symbol anchoring system to a probabilistic logic reasoner for performing inference. Furthermore, we use statistical relational learning to enable the anchoring framework to learn symbolic knowledge in the form of a set of probabilistic logic rules of the world from noisy and sub-symbolic sensor input. The resulting framework, which combines perceptual anchoring and statistical relational learning, is able to maintain a semantic world model of all the objects that have been perceived over time, while still exploiting the expressiveness of logical rules to reason about the state of objects which are not directly observed through sensory input data. To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations. The learned logical rules are, subsequently, used to assess our proposed probabilistic anchoring procedure. We demonstrate our system in a setting involving object interactions where object occlusions arise and where probabilistic inference is needed to correctly anchor objects. ispartof: Frontiers in Robotics and AI vol:7 ispartof: location:Switzerland status: published
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- 2020
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37. Fuzzy MLNs and QSTAGs for Activity Recognition and Modelling with RUSH
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Liam Mellor and Van Nguyen
- Subjects
Situation awareness ,Markov chain ,Event (computing) ,Computer science ,business.industry ,Fuzzy set ,Probabilistic logic ,Statistical relational learning ,Machine learning ,computer.software_genre ,Semantics ,Fuzzy logic ,Activity recognition ,Artificial intelligence ,business ,computer - Abstract
Event and activity modelling and recognition is at the centre of situational awareness (SA). Being able to detect and extract useful semantic information from diverse data sources in the form of events and activities, an SA system may assist human operators with cognitive analysis, situation monitoring and to inform decision making. Recognition of realworld activities with complex spatial and temporal interactions in the presence of uncertainty has been a major line of research. In this respect, Statistical Relational Learning methods such as Markov Logic Networks (MLNs) have been shown to provide a powerful and promising framework, and efficiently leveraging their representational power for event and activity recognition are active research and development activities. In this paper we propose a complementary method to existing approaches, FQSTAG-MLN, that combines Fuzzy MLNs with Fuzzy Qualitative Spatio-Temporal Activity Graphs (FQSTAGs) toward achieving efficient and explainable activity modelling and recognition. With FQSTAG-MLNs, the complexity of real-world activities is addressed by combining the representational richness of high-level activity specification with compact local spatio-temporal narratives for low-level events; while the uncertainty of activity and world models is calibrated using probabilistic methods and the vagueness of observations is calibrated using fuzzy set theory. We describe its current implementation within RUSH, which aims to be a flexible reasoning and learning platform for situational awareness. We present a use case illustration and early results.
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- 2020
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38. Transfer Learning by Mapping and Revising Boosted Relational Dependency Networks
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Aline Paes, Rodrigo Azevedo Santos, and Gerson Zaverucha
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Vocabulary ,Dependency (UML) ,Uncertain data ,Point (typography) ,Computer science ,business.industry ,Relational database ,media_common.quotation_subject ,Statistical relational learning ,Machine learning ,computer.software_genre ,Domain (software engineering) ,Artificial Intelligence ,Artificial intelligence ,Transfer of learning ,business ,computer ,Software ,media_common - Abstract
Statistical machine learning algorithms usually assume that there is considerably-size data to train the models. However, they would fail in addressing domains where data is difficult or expensive to obtain. Transfer learning has emerged to address this problem of learning from scarce data by relying on a model learned in a source domain where data is easy to obtain to be a starting point for the target domain. On the other hand, real-world data contains objects and their relations, usually gathered from noisy environment. Finding patterns through such uncertain relational data has been the focus of the Statistical Relational Learning (SRL) area. Thus, to address domains with scarce, relational, and uncertain data, in this paper, we propose TreeBoostler, an algorithm that transfers the SRL state-of-the-art Boosted Relational Dependency Networks learned in a source domain to the target domain. TreeBoostler first finds a mapping between pairs of predicates to accommodate the additive trees into the target vocabulary. After, it employs two theory revision operators devised to handle incorrect relational regression trees aiming at improving the performance of the mapped trees. In the experiments presented in this paper, TreeBoostler has successfully transferred knowledge among several distinct domains. Moreover, it performs comparably or better than learning from scratch methods in terms of accuracy and outperforms a transfer learning approach in terms of accuracy and runtime.
- Published
- 2020
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39. HyperCard to Artificial Intelligence for Relational Learning
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Jazlin Ebenezer
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business.industry ,Computer science ,Statistical relational learning ,Artificial intelligence ,HyperCard ,business ,computer ,computer.programming_language - Published
- 2020
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40. An Approach to Automatically Selecting Tolerance Types Using Relational Learning for Knowledge Graphs
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Jia Jia, Mohamed Saad, and Yingzhong Zhang
- Subjects
Knowledge graph ,Computer science ,business.industry ,Statistical relational learning ,Artificial intelligence ,computer.software_genre ,business ,computer ,Natural language processing - Published
- 2020
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41. Improving Classification of Interlinked Entities Using Only the Network Structure
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Cristina Pérez-Solà and Jordi Herrera-Joancomartí
- Subjects
0209 industrial biotechnology ,Computer Networks and Communications ,Computer science ,business.industry ,Statistical relational learning ,Network structure ,02 engineering and technology ,Machine learning ,computer.software_genre ,Computer Graphics and Computer-Aided Design ,Support vector machine ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Architecture ,business ,Classifier (UML) ,computer ,Software - Abstract
This paper presents a classifier architecture that is able to deal with classification of interlinked entities when the only information available is the existing relationships between these entities, i.e. no semantic content is known for either the entities or their relationships. After proposing a classifier to deal with this problem, we provide extensive experimental evaluation showing that our proposed method is sound and that it is able to achieve high accuracy, in most cases much higher than other already existing algorithms configured to tackle this very same problem. The contributions of this paper are twofold: first, it presents a classifier for interlinked entities that outperforms most of the existing algorithms when the only information available is the relationships between these entities; second, it reveals the power of using label independent (LI) features extracted from network structural properties in the bootstrapping phases of relational classification.
- Published
- 2019
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42. Improving statistical relational learning with graph embeddings for socio-economic data retrieval
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Alexander Kalinin, Danila Vaganov, and Klavdiya Bochenina
- Subjects
Social graph ,Data collection ,Computer science ,business.industry ,Statistical relational learning ,Recommender system ,Machine learning ,computer.software_genre ,Graph ,Vertex (geometry) ,Personalized search ,Interpersonal ties ,General Earth and Planetary Sciences ,Graph (abstract data type) ,Social media ,Artificial intelligence ,business ,computer ,General Environmental Science - Abstract
Social media data is useful for personalized search engines, recommender systems, and targeted online marketing. Sometimes values of attributes are missing due to security reasons or problematic data collection process. In this case, the information about connections between vertices become more important since it explicitly allows for using the structure of a social graph for inferring missing attributes. One of the general and effective approaches of inferring missing attributes on graph structures are statistical relational learning. For machine learning tasks, the graph embeddings represent topological properties better, but they are not aimed at the attributes prediction. In this study, we introduce a method combining graph embeddings and the statistical relational learning. We consider different their combinations, as there are possible different hidden connections between social ties and considered attributes. We compare the performance using real data from the social network with different missing attributes and assortative patterns: gender, age, and economic status. As a result, the inclusion of graph embeddings in statistical relational learning improves accuracy and significantly decreases the number of iterations.
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- 2019
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43. Relational Learning Between Multiple Pulmonary Nodules via Deep Set Attention Transformers
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Xiaoyang Huang, Haoran Deng, Bingbing Ni, Jiancheng Yang, and Yi Xu
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Statistical relational learning ,02 engineering and technology ,Machine learning ,computer.software_genre ,Malignancy ,Machine Learning (cs.LG) ,03 medical and health sciences ,0302 clinical medicine ,Voxel ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Transformer (machine learning model) ,Multiple Pulmonary Nodules ,business.industry ,Image and Video Processing (eess.IV) ,Electrical Engineering and Systems Science - Image and Video Processing ,medicine.disease ,030220 oncology & carcinogenesis ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer - Abstract
Diagnosis and treatment of multiple pulmonary nodules are clinically important but challenging. Prior studies on nodule characterization use solitary-nodule approaches on multiple nodular patients, which ignores the relations between nodules. In this study, we propose a multiple instance learning (MIL) approach and empirically prove the benefit to learn the relations between multiple nodules. By treating the multiple nodules from a same patient as a whole, critical relational information between solitary-nodule voxels is extracted. To our knowledge, it is the first study to learn the relations between multiple pulmonary nodules. Inspired by recent advances in natural language processing (NLP) domain, we introduce a self-attention transformer equipped with 3D CNN, named {NoduleSAT}, to replace typical pooling-based aggregation in multiple instance learning. Extensive experiments on lung nodule false positive reduction on LUNA16 database, and malignancy classification on LIDC-IDRI database, validate the effectiveness of the proposed method., 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)
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- 2020
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44. Collective behavior learning by differentiating personal preference from peer influence
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Zan Zhang, Jiuyong Li, Hao Wang, Daning Hu, René Algesheimer, Markus Meierer, Jiaqi Yan, Lin Liu, Zhang, Zan, Liu, Lin, Wang, Hao, Li, Jiuyong, Hu, Daning, Yan, Jiaqi, Algesheimer, Rene, and Meierer, Markus
- Subjects
classification with networked data ,Estimation ,Collective behavior ,Information Systems and Management ,Social network ,business.industry ,Computer science ,Statistical relational learning ,causal analysis ,02 engineering and technology ,Preference ,Management Information Systems ,collective behavior ,Artificial Intelligence ,020204 information systems ,Propensity score matching ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Social media ,business ,Classifier (UML) ,propensity score ,Software ,Cognitive psychology - Abstract
Networked data, generated by social media, presents opportunities and challenges to the study of collective behaviors in a social networking environment. In this paper, we focus on multi-label classification on networked data, for which behaviors are represented as labels and an individual can have multiple labels. Existing relational learning methods exploit the connectivity of individuals and they have shown better performance than traditional multi-label classification methods. However, an individual's behavior may be influenced by other factors, particularly personal preference. Hence, we propose a novel approach that integrates causal analysis into multi-label classification to learn collective behaviors. We employ propensity score matching and causal effect estimation to distinguish the contributions of peer influence and personal preference to collective behaviors and incorporate the findings into the design of the classifier. We further study behavior heterogeneity across subgroups in social networks, as people with different demographic features may behave differently due to different impacts of peer influence and personal preference. We estimate conditional average causal effects to analyze the impacts of peer influence and personal preference in different subgroups in social networks. Experiments on real-world datasets demonstrate that our proposed methods improve classification performance over existing methods. Refereed/Peer-reviewed
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- 2018
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45. Interactive Visual Graph Mining and Learning
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Rong Zhou, Ryan A. Rossi, Nesreen K. Ahmed, and Hoda Eldardiry
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Dynamic network analysis ,Computer science ,business.industry ,Statistical relational learning ,020207 software engineering ,02 engineering and technology ,Machine learning ,computer.software_genre ,Theoretical Computer Science ,Weighting ,Visualization ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Block model ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer ,Interactive visualization ,Network analysis - Abstract
This article presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a particular task (e.g., classification, link prediction, discovering the roles of nodes, and finding influential nodes). Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. In particular, we propose techniques for interactive relational learning (e.g., node/link classification), interactive link prediction and weighting, role discovery and community detection, higher-order network analysis (via graphlets, network motifs), among others. GraphVis also allows for the refinement and tuning of graph mining and relational learning methods for specific application domains and constraints via an end-to-end interactive visual analytic pipeline that learns, infers, and provides rapid interactive visualization with immediate feedback at each change/prediction in real-time. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators (including new block model approaches), and a variety of multi-level network analysis techniques.
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- 2018
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46. A Decision-Support Tool for Renal Mass Classification
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Bhushan Desai, Bino Varghese, Vinay Duddalwar, Priya Ganapathy, Inderbir S. Gill, Manju Aron, Gautam Kunapuli, and Steven Cen
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Decision support system ,Computer science ,Clinical Decision-Making ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,030232 urology & nephrology ,Statistical relational learning ,Contrast Media ,Computed tomography ,Kidney ,Clinical decision support system ,Article ,Decision Support Techniques ,030218 nuclear medicine & medical imaging ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Radiomics ,Renal mass ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Retrospective Studies ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,business.industry ,Reproducibility of Results ,Pattern recognition ,Kidney Neoplasms ,Computer Science Applications ,Radiographic Image Enhancement ,Gradient boosting ,Artificial intelligence ,Signal intensity ,Tomography, X-Ray Computed ,business ,Algorithms - Abstract
We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification. Experimental results demonstrate that RFGB outperforms many standard machine learning approaches as well as the current diagnostic gold standard of visual qualification by radiologists.
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- 2018
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47. Joint regression and classification via relational regularization for Parkinson’s disease diagnosis
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Baiying Lei, Haijun Lei, Tao Han, Gang Liu, Ye Cai, Qiuming Luo, and Zhongwei Huang
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Male ,multi-modality ,Parkinson's disease ,Computer science ,Biomedical Engineering ,Biophysics ,Statistical relational learning ,Neuroimaging ,Health Informatics ,Bioengineering ,Feature selection ,02 engineering and technology ,Disease ,Machine learning ,computer.software_genre ,Pattern Recognition, Automated ,Biomaterials ,03 medical and health sciences ,feature selection ,0302 clinical medicine ,Discriminative model ,Image Interpretation, Computer-Assisted ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Humans ,Aged ,business.industry ,Parkinson Disease ,Cognition ,Middle Aged ,medicine.disease ,Regression ,classification ,Positron-Emission Tomography ,Disease Progression ,Parkinson’s disease ,Female ,Joints ,020201 artificial intelligence & image processing ,Artificial intelligence ,score prediction ,business ,computer ,030217 neurology & neurosurgery ,Research Article ,Information Systems - Abstract
It is known that the symptoms of Parkinson’s disease (PD) progress successively, early and accurate diagnosis of the disease is of great importance, which slows the disease deterioration further and alleviates mental and physical suffering. In this paper, we propose a joint regression and classification scheme for PD diagnosis using baseline multi-modal neuroimaging data. Specifically, we devise a new feature selection method via relational learning in a unified multi-task feature selection model. Three kinds of relationships (e.g., relationships among features, responses, and subjects) are integrated to represent the similarities among features, responses, and subjects. Our proposed method exploits five regression variables (depression, sleep, olfaction, cognition scores and a clinical label) to jointly select the most discriminative features for clinical scores prediction and class label identification. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method on the Parkinson’s Progression Markers Initiative (PPMI) dataset. Our experimental results demonstrate that multi-modal data can effectively enhance the performance in class label identification compared with single modal data. Our proposed method can greatly improve the performance in clinical scores prediction and outperforms the state-of-art methods as well. The identified brain regions can be recognized for further medical analysis and diagnosis.
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- 2018
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48. On relational learning and discovery in social networks: a survey
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Xiaohui Tao, Ji Zhang, and Leonard Tan
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Computer science ,business.industry ,Statistical relational learning ,Social complexity ,02 engineering and technology ,Data science ,Field (computer science) ,law.invention ,Knowledge extraction ,Artificial Intelligence ,law ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,CLARITY ,020201 artificial intelligence & image processing ,The Internet ,Social media ,Computer Vision and Pattern Recognition ,business ,Affective computing ,Software - Abstract
The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements.
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- 2018
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49. Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures
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Vojtech Aschenbrenner, Gustav Sourek, Filip Zelezny, Steven Schockaert, and Ondrej Kuzelka
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Structure (mathematical logic) ,Artificial neural network ,business.industry ,Computer science ,Statistical relational learning ,Neural network learning ,02 engineering and technology ,Variety (cybernetics) ,Data set ,03 medical and health sciences ,0302 clinical medicine ,Stochastic gradient descent ,Artificial Intelligence ,030221 ophthalmology & optometry ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Interpretability - Abstract
We propose a method to combine the interpretability and expressive power of firstorder logic with the effectiveness of neural network learning. In particular, we introduce a lifted framework in which first-order rules are used to describe the structure of a given problem setting. These rules are then used as a template for constructing a number of neural networks, one for each training and testing example. As the different networks corresponding to different examples share their weights, these weights can be efficiently learned using stochastic gradient descent. Our framework provides a flexible way for implementing and combining a wide variety of modelling constructs. In particular, the use of first-order logic allows for a declarative specification of latent relational structures, which can then be efficiently discovered in a given data set using neural network learning. Experiments on 78 relational learning benchmarks clearly demonstrate the effectiveness of the framework.
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- 2018
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50. Stochastic relational processes: Efficient inference and applications.
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Thon, Ingo, Landwehr, Niels, and De Raedt, Luc
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MARKOV processes ,ARTIFICIAL intelligence ,LEARNING strategies ,DISTRIBUTION (Probability theory) ,LEARNING problems ,BOOLEAN algebra - Abstract
One of the goals of artificial intelligence is to develop agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. While standard probabilistic sequence models provide efficient inference and learning techniques for sequential data, they typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient to cope with complex sequential data. In this paper, we introduce a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic (Causal Probabilistic Logic), an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions and employing a Markov assumption, inference and learning become more tractable and effective. Specifically, we show how to solve part of the inference and learning problems directly at the first-order level, while transforming the remaining part into the problem of computing all satisfying assignments for a Boolean formula in a binary decision diagram. We experimentally validate that the resulting technique is able to handle probabilistic relational domains with a substantial number of objects and relations. [ABSTRACT FROM AUTHOR]
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- 2011
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