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Online learning has been greatly widespread since the explosion of COVID-19. However, due to the lack of interaction between teachers and students in online courses, it is very difficult for students to focus on course content and complete the learning. Therefore, we developed a novel intelligent multilevel knowledge graph to help students quickly and systematically grasp the framework and key content of video lectures. Specifically, our method introduced cues into the interactive knowledge mapping. This approach not only allows students to interact with the graphs but also displays different levels of knowledge based on its importance. In addition, a quasi-experiment was implemented to verify the effectiveness of our method. Specifically, we compare the effects of knowledge graphs in three weighting strategies for cues with traditional manual concept maps on students' learning effectiveness and learning perception. Our study shows that although static artificial concept maps do enhance student learning to a certain extent compared to traditional learning, its development efficiency and enhancement of student learning outcomes are not ideal. In contrast, interactive graphs presenting overlay cues can effectively help students to restructure and systematize their knowledge, direct their attention and thus improve their learning outcomes. [ABSTRACT FROM AUTHOR]

Background: Familiar cardiopathies are genetic disorders that affect the heart. Cardiologists face a significant problem when treating patients suffering from these disorders: most DNA variations are novel (i.e., they have not been classified before). To facilitate the analysis of novel variations, we present CardioGraph, a platform specially designed to support the analysis of novel variations and help determine whether they are relevant for diagnosis. To do this, CardioGraph identifies and annotates the consequence of variations and provides contextual information regarding which heart structures, pathways, and biological processes are potentially affected by those variations. Methods: We conducted our work through three steps. First, we define a data model to support the representation of the heterogeneous information. Second, we instantiate this data model to integrate and represent all the genomics knowledge available for familiar cardiopathies. In this step, we consider genomic data sources and the scientific literature. Third, the design and implementation of the CardioGraph platform. A three-tier structure was used: the database, the backend, and the frontend. Results: Three main results were obtained: the data model, the knowledge base generated with the instantiation of the data model, and the platform itself. The platform code has been included as supplemental material in this manuscript. Besides, an instance is publicly available in the following link: https://genomics-hub.pros.dsic.upv.es:3090. Conclusion: CardioGraph is a platform that supports the analysis of novel variations. Future work will expand the body of knowledge about familiar cardiopathies and include new information about hotspots, functional studies, and previously reported variations. [ABSTRACT FROM AUTHOR]

With the advent of robotics and artificial intelligence, the potential for automating tasks within human-centric environments has increased significantly. This is particularly relevant in the retail sector where the demand for efficient operations and the shortage of labor drive the need for rapid advancements in robot-based technologies. Densely packed retail shelves pose unique challenges for robotic manipulation and detection due to limited space and diverse object shapes. Vacuum-based grasping technologies offer a promising solution but face challenges with object shape adaptability. The study proposes a framework for robotic grasping in retail environments, an adaptive vacuum-based grasping solution, and a new evaluation metric—termed grasp shear force resilience—for measuring the effectiveness and stability of the grasp during manipulation. The metric provides insights into how retail objects behave under different manipulation scenarios, allowing for better assessment and optimization of robotic grasping performance. The study's findings demonstrate the adaptive suction cups' ability to successfully handle a wide range of object shapes and sizes, which, in some cases, overcome commercially available solutions, particularly in adaptability. Additionally, the grasp shear force resilience metric highlights the effects of the manipulation process, such as in shear force and shake, on the manipulated object. This offers insights into its interaction with different vacuum cup grasping solutions in retail picking and restocking scenarios. [ABSTRACT FROM AUTHOR]

Transformer-based large language models (LLMs) are very suited for biological sequence data, because of analogies to natural language. Complex relationships can be learned, because a concept of "words" can be generated through tokenization. Training the models with masked token prediction, they learn both token sequence identity and larger sequence context. We developed methodology to interrogate model learning, which is both relevant for the interpretability of the model and to evaluate its potential for specific tasks. We used DNABERT, a DNA language model trained on the human genome with overlapping k-mers as tokens. To gain insight into the model′s learning, we interrogated how the model performs predictions, extracted token embeddings, and defined a fine-tuning benchmarking task to predict the next tokens of different sizes without overlaps. This task evaluates foundation models without interrogating specific genome biology, it does not depend on tokenization strategies, vocabulary size, the dictionary, or the number of training parameters. Lastly, there is no leakage of information from token identity into the prediction task, which makes it particularly useful to evaluate the learning of sequence context. We discovered that the model with overlapping k-mers struggles to learn larger sequence context. Instead, the learned embeddings largely represent token sequence. Still, good performance is achieved for genome-biology-inspired fine-tuning tasks. Models with overlapping tokens may be used for tasks where a larger sequence context is of less relevance, but the token sequence directly represents the desired learning features. This emphasizes the need to interrogate knowledge representation in biological LLMs. [ABSTRACT FROM AUTHOR]

In the realm of artificial intelligence, knowledge graphs (KGs) serve as an essential structured framework, capturing intricate relationships between diverse entities and supporting a broad spectrum of AI applications. Despite their utility, the static characteristic of KGs poses challenges in dynamically evolving information landscapes. This has catalyzed the development of temporal knowledge graphs (TKGs), which introduce a temporal layer to KGs, facilitating the representation of knowledge progression through time. This study zeroes in on the critical task of TKG extrapolation, which is vital for forecasting future occurrences and offering foresight into emerging situations across a variety of fields. Most contemporary approaches to TKG extrapolation are predicated on the symmetrical encoder–decoder paradigm, wherein the processes of representation learning and reasoning are harmoniously intertwined. While the encoder often garners the most attention due to its role in capturing and encoding information, the pivotal role of the decoder, which is often overlooked, is essential for direct inference and the accurate projection of temporal dynamics. To this end, we present the Householder-transformation-based temporal knowledge graph extrapolation (HTKGE) method: a groundbreaking encoder–decoder framework that reimagines the decoder's contribution to TKG extrapolation. Our approach spotlights an adaptive decoder propelled by Householder transformations, which engage dynamically with the temporal encoding from the encoder. This interaction fosters a nuanced comprehension of the TKG's temporal trajectory. Our empirical evaluations across four benchmark TKG datasets substantiate HTKGE's consistent efficacy in TKG extrapolation tasks. [ABSTRACT FROM AUTHOR]

In recent years, machine learning models have been increasingly used to detect security vulnerabilities in software, due to their ability to achieve high performance and lower false positive rates compared to traditional program analysis tools. However, these models often lack the capability to provide a clear explanation for why a program has been flagged as vulnerable, leaving developers with little reasoning to work with. We present a new method which not only identifies the presence of vulnerabilities in a program, but also the specific type of error, considering the whole program rather than just individual functions. Our approach utilizes graph neural networks that employ inter-procedural value flow graphs, and instruction embedding from the LLVM Intermediate Representation, to predict a class. By mapping these classes to the Common Weakness Enumeration list, we provide a clear indication of the security issue found, saving developers valuable time which would otherwise be spent analyzing a binary vulnerable/non-vulnerable label. To evaluate our method's effectiveness, we used two datasets: one containing memory-related errors (out of bound array accesses), and the other a range of vulnerabilities from the Juliet Test Suite, including buffer and integer overflows, format strings, and invalid frees. Our model, implemented using PyTorch and the Gated Graph Sequence Neural Network from Torch-Geometric, achieved a precision of 96.35 and 91.59% on the two datasets, respectively. Compared to common static analysis tools, our method produced roughly half the number of false positives, while identifying approximately three times the number of vulnerable samples. Compared to recent machine learning systems, we achieve similar performance while offering the added benefit of differentiating between classes. Overall, our approach represents a meaningful improvement in software vulnerability detection, providing developers with valuable insights to better secure their code. [ABSTRACT FROM AUTHOR]

Phishing attacks are increasing every year, both in terms of number and technique. Using only human weaknesses, an attacker can easily obtain the victim's credentials or access their network. The problem persists despite many approaches offered by researchers, due to its dynamic nature, in which new phishing tactics are created every time. We, therefore, need more robust and effective methods to detect phishing emails. In this paper, we aim to detect phishing emails using the body text of the email with the hybrid approach combining case-based reasoning (CBR) and a deep learning model. Our proposed model, called DL-CBR, consists of a Bidirectional Long Short-Term Memory (Bi-LSTM) + Temporal Convolutional Network (TCN) network with an attention mechanism followed by a CBR classifier. The deep learning model is used for email representation, where it is trained using the N -pair loss function. To demonstrate the performance of DL-CBR, evaluation metrics, such as precision, accuracy, recall, and F-measure, were used, where we obtained an accuracy of 98.28%. The results show that our model outperformed other CBRs that utilize classical text representations like TF-IDF and Bag-of-Words. Additionally, while our model's performance is slightly below that of the state-of-the-art models, it offers several advantages inherent to CBR. For instance, it can learn from new cases and update their database accordingly. [ABSTRACT FROM AUTHOR]

In the fast-paced world of product design, businesses seek a competitive edge by swiftly addressing user requirements and developing precise solutions. Depending on diverse knowledge, conceptual design makes enterprises invest significantly in knowledge management. Following the philosophy of decision-based design, a knowledge graph-aided decision guidance method is proposed to streamline and enhance knowledge utilisation in product conceptual design. Firstly, the decision-making process in product conceptual design is modelled using the Concept-Decision-Knowledge (CDK) model, yielding the meta-model of CDK (mCDK). A data-augmented BERT-BiLSTM-CRF model is adopted to extract information from diverse data sources, forming a CDK knowledge graph (CDK-KG). A decision case for employing problem-solving knowledge configuration is generated to resolve specific design decision problems when new requirements arise. Validation of the method is demonstrated through a case study on the launch vehicle conceptual design of the first and second-stage separation system. The results indicate that the proposed method can automatically extract information from data resources to construct a knowledge graph. Furthermore, it can provide decision cases for design requirements through knowledge configuration, supporting decision-making. [ABSTRACT FROM AUTHOR]

The integration of quantitative and qualitative knowledge from parasitologists plays a pivotal role in refining the performance of deep learning (DL) models. Knowledge-integrated DL models can be trained using different learning strategies that make use of labeled and unlabeled data, such as supervised learning, semi-supervised learning, and self-supervised learning. When choosing a backbone network – such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), transformers, or graph neural networks (GNNs) – for parasitology research, it is essential to consider their advantages, disadvantages, and data characteristics. DL-based microscopy image analysis has been successfully applied in parasitology, including high-throughput parasite detection, quantitative analysis of host–pathogen interactions, and aberrated images correction. Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology. [ABSTRACT FROM AUTHOR]

Background: Globally, healthcare systems have always contended with well-known and seemingly intractable challenges like safety, quality, efficient and effective clinical and administrative patient-care services. To firmly confront these and other healthcare challenges, the World Health Organisation proposed a full adoption of artificial intelligence (AI) applications into patient care to stimulate efficiency and guarantee quality in patient management. Purpose: This review aimed to establish the extent and type of evidence of the positive effects of the use of AI tools in patient care. Thus, the review mapped evidence by using articles published between January 1, 2010, and October 31, 2023. Methods: Consistent with the protocol by Tricco et al., a comprehensive literature search was executed from Nature, PubMed, Scopus, ScienceDirect, Dimensions, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. Upholding the inclusion and exclusion standards, 95 peer-reviewed articles were included in this review. Findings: We report that the use of AI tools can significantly improve the accuracy of clinical diagnosis to guarantee better patient health outcomes. AI tools also have the ability to mitigate, if not eliminate, most of the factors that currently predict poor patient outcomes. Furthermore, AI tools are far more efficient in generating robust and accurate data in real time and can help ease and accelerate workflow at healthcare facilities. Conclusion: If properly integrated into the healthcare system, AI will help reduce patients' waiting time and accelerate the attainment of Sustainable Development Goals 3.4, 3.8, and 3.b. We propose that AI developers collaborate with public health practitioners and healthcare managers to develop AI applications that appreciate socio-cultural dimensions in patient care. [ABSTRACT FROM AUTHOR]

Traditional martial arts are treasures of humanity's knowledge and critical carriers of sociocultural memories throughout history. However, such treasured practices have encountered various challenges in knowledge transmission and now feature many entries on the UNESCO list of intangible cultural heritage. In tackling the urgency of knowledge preservation through digital means, this project employs an ontology-based approach to model the conceptual realm of traditional martial arts. Accordingly, it creates the Martial Art Ontology (MAon) , a comprehensive domain ontology with an annotated data resource incorporating entities and relations from embodied, epistemic, and sociocultural facets. MAon underlines the significance of embodied qualities and addresses relevant dimensions, such as kinesthetics, techniques, mnemonics, and tactics, along with stylistic, interpretative, and ideological components. It features scholarly terminology developed through literature analysis, interviews with masters, and expert validations. The instantiation of MAon is realized through annotating three archetypal Southern Chinese styles, offering exhaustive descriptions concerning techniques, forms, principles, and form sets, amongst others. In summary, the reported approach encodes the manifold of martial arts into a structured vocabulary and an interlinked data resource, accessible to both human-reading and machine-operating applications. By applying it to manifest a range of knowledge concepts, we demonstrate the potential of ontology-based datafication to create coherent representations for intangible cultural entities and to enable an interoperable data infrastructure across multimodal cultural archives. [ABSTRACT FROM AUTHOR]

Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for sharing and translational researches. A formal representation of PCa-associated knowledge will be essential to the diverse data standardization, data sharing and the future knowledge graph extraction, deep phenotyping and explainable artificial intelligence developing. In this study, we constructed an updated PCa ontology (PCAO2) based on the ontology development life cycle. An online information retrieval system was designed to ensure the usability of the ontology. The PCAO2 with a subclass-based taxonomic hierarchy covers the major biomedical concepts for PCa-associated genotypic, phenotypic and lifestyle data. The current version of the PCAO2 contains 633 concepts organized under three biomedical viewpoints, namely, epidemiology, diagnosis and treatment. These concepts are enriched by the addition of definition, synonym, relationship and reference. For the precision diagnosis and treatment, the PCa-associated genes and lifestyles are integrated in the viewpoint of epidemiological aspects of PCa. PCAO2 provides a standardized and systematized semantic framework for studying large amounts of heterogeneous PCa data and knowledge, which can be further, edited and enriched by the scientific community. The PCAO2 is freely available at https://bioportal.bioontology.org/ontologies/PCAO , http://pcaontology.net/ and http://pcaontology.net/mobile/. [ABSTRACT FROM AUTHOR]

Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults. [ABSTRACT FROM AUTHOR]

Representation learning serves as a crucial link between knowledge graphs and neural models. Knowledge graphs are typical symbolic models and require representation learning to obtain vector representations of entities and relationships. Existing efforts focus more on the structural characteristics of knowledge itself, such as the connections between entities and relationships under the same type of knowledge, neglecting the potential value of commonsense knowledge in guiding and constraining. In fact, commonsense knowledge implies the belonging tendency of entities in triplets. In this paper, we propose a novel knowledge representation learning model, CoolGust, which is guided and constrained by commonsense knowledge and can effectively utilize commonsense knowledge to seamlessly guide the belonging relationships of entities and enhance the model performance. Commonsense knowledge is essential for guiding humans to make wise decisions in unknown scenarios. We find that by utilizing commonsense knowledge as guides and constraints for entities, hidden knowledge entanglement structures can be formed in complex network applications, thereby constructing decisions consistent with situational logic. To verify the effectiveness of our model, we conducted experimental verification of two tasks, link prediction, and triple classification, on three public datasets. The experimental results demonstrate the effectiveness and advancedness of our proposed method. [ABSTRACT FROM AUTHOR]

AbstractPurposeMaterials and methodsResultsConclusion(s)\nIMPLICATIONS FOR REHABILITATIONTo examine (1) how much participation is represented in the benchmark Unified Medical Language System (UMLS) resource, and (2) to what extent that representation reflects the definition of child and youth participation and/or its related constructs per the family of Participation-Related Constructs framework.We searched and analysed UMLS concepts related to the term “participation.” Identified UMLS concepts were rated according to their representation of participation (i.e., attendance, involvement, both) as well as participation-related constructs using deductive content analysis.363 UMLS concepts were identified. Of those, 68 had at least one English definition, resulting in 81 definitions that were further analysed. Results revealed 2 definitions (2/81; 3%; 2/68 UMLS concepts) representing participation “attendance” and 18 definitions (18/81; 22%; 14/68 UMLS concepts) representing participation “involvement.” No UMLS concept definition represented both attendance and involvement (i.e., participation). Most of the definitions (11/20; 55%; 9/16 UMLS concepts) representing attendance or involvement also represent a participation-related construct.The representation of participation within the UMLS is limited and poorly aligned with the contemporary definition of child and youth participation. Expanding ontological resources to represent child and youth participation is needed to enable better data analytics that reflect contemporary paediatric rehabilitation practice.The representation of participation within the Unified Medical Language System (UMLS) is limited and poorly aligned with the contemporary definition of child and youth participation.From a contemporary paediatric rehabilitation perspective, using the current UMLS concepts for data analytics might result in misrepresentation of child and youth participation.There is need to expand ontological resources within the UMLS to fully and exclusively represent participation dimensions (attendance and involvement) in daily life activities to enable better data analytics that reflect contemporary paediatric rehabilitation practice.The representation of participation within the Unified Medical Language System (UMLS) is limited and poorly aligned with the contemporary definition of child and youth participation.From a contemporary paediatric rehabilitation perspective, using the current UMLS concepts for data analytics might result in misrepresentation of child and youth participation.There is need to expand ontological resources within the UMLS to fully and exclusively represent participation dimensions (attendance and involvement) in daily life activities to enable better data analytics that reflect contemporary paediatric rehabilitation practice. [ABSTRACT FROM AUTHOR]

The understanding of the external characteristics of objects that need to be grasped is crucial for enhancing the dexterity of a robotic hand. Utilizing ontology-based knowledge representation (KR) approaches in the field of grasping presents novel opportunities for designing effective object recognition modules. This research paper proposes the development of an ontology aimed at recognizing objects that have been grasped. The ontology, named OntOGrasp, encompasses various concepts, attributes, and relationships concerning the kinematics of the hand and the structural components of the object. To generate a dataset for grasp kinematics, a series of grasping experiments were conducted using carefully selected objects. A semantic feature selection approach was then introduced, leveraging the knowledge graph embedding technique, to identify practical features from the ontology. The grasp dataset was processed to extract relevant data pertaining to the selected features, which were subsequently employed to train classification algorithms for the task of object recognition. The integration of semantically suggested features resulted in a significant improvement in recognition accuracy for the classifiers, surpassing the performance achieved by using the complete feature set. The semantic features demonstrated an accuracy increase ranging from 1% to 9% across all classification algorithms, accompanied by a notable reduction in false positives and false negatives during object prediction. This enhanced object recognition accuracy substantiates the importance of knowledge modeling in incorporating practical aspects of a domain described through experimentation, thereby yielding plausible outcomes. [ABSTRACT FROM AUTHOR]

Cloud Computing has revolutionized the way applications are developed, deployed, and maintained. Over the past decade, we have observed dynamically growing interest in Cloud Computing. The benefits of the cloud approach caused the increasing popularity of Cloud-native applications. Cloud-native is an approach to developing and deploying applications according to the concepts of DevOps, Continuous Integration/Continuous Delivery (CI/CD), containers and microservices. The knowledge about Cloud Computing has become extensive and complex. Fortunately, before Cloud-native applications development, there was a great deal of effort to develop tools for effective knowledge representation. Ontologies are a convenient way to show the relations between domain-specific concepts. In this paper, we propose an ontology named CNOnt that describes the state-of-the-art of Cloud-native applications. CNOnt covers aspects from the clusterization perspective. First, this paper presents the engineering perspective of building the CNOnt ontology. Second, we demonstrate a use case of our ontology that proves the correctness of CNOnt development. This ontology is exhausted in CNOnt Broker. It is a system that applies the information in the OWL file into the Kubernetes cluster and in reverse. The knowledge representation makes Cloud-native applications understandable to third-party systems and increases interoperability between different microservices. [ABSTRACT FROM AUTHOR]

A qualitative study was conducted in a secondary school to evaluate student learning processes and outcomes by examining their inquiry questions, journals, and maker artifacts in a curriculum-based maker learning environment supported by the Guided Inquiry Design (GID). Thirteen 8th-grade students in a suburban middle school in the southwest of the United States participated in the study. Inquiry questions, maker artifacts, and inquiry journals were collected and analyzed with rubrics that were developed based on a critical review of literature drawn from different bodies of literature. The objectives of this study were three-fold: (1) evaluate the quality of students' inquiry questions and maker artifacts, (2) assess students' internal knowledge representation (IKR) and external knowledge representation (EKR), and (3) develop a robust and valid assessment framework for maker learning considering students' expertise development over time. The findings revealed that the students progressed at different developmental levels; therefore, expertise development should be incorporated into the assessment framework for maker learning. This study also implied that scaffolding should be tailored to meet special needs of each student in a maker learning environment. The originality of this research is that the assessment framework takes into account individuals' development and progress towards expertise over time, instead of focusing on their learning outcomes at a specific time point. An additional value of this study is that the tools and supporting materials that serve as instructional scaffolds also serve as tools to collect evidence about student learning performance, processes, and outcomes. [ABSTRACT FROM AUTHOR]

The domains of computational social anthropology and computational ethnography refer to the computational processing or computational modelling of data for anthropological or ethnographic research. In this context, the article surveys the use of computational methods regarding the production and the representation of knowledge. The ultimate goal of the study is to highlight the significance of modelling ethnographic data and anthropological knowledge by harnessing the potential of the semantic web. The first objective was to review the use of computational methods in anthropological research focusing on the last 25 years, while the second objective was to explore the potential of the semantic web focusing on existing technologies for ontological representation. For these purposes, the study explores the use of computers in anthropology regarding data processing and data modelling for more effective data processing. The survey reveals that there is an ongoing transition from the instrumentalisation of computers as tools for calculations, to the implementation of information science methodologies for analysis, deduction, knowledge representation, and reasoning, as part of the research process in social anthropology. Finally, it is highlighted that the ecosystem of the semantic web does not subserve quantification and metrics but introduces a new conceptualisation for addressing and meeting research questions in anthropology. [ABSTRACT FROM AUTHOR]

The knowledge graph in the recommendation system plays a vital role in the recommendation effect of the system, and the knowledge representation in the graph becomes a key factor affecting the recommendation system, which has become one of the current research hotspots. This paper proposed a node2vec-based knowledge representation node2 vec-side based on the traditional node2vec model by adding relational representation and diversifing wandering strategy to the structural characteristics of the knowledge graph in recommendation system, which combined with the knowledge graph network structure of recommendation system to explore the potential association relationship between nodes of large-scale recommendation entities, reduced the complexity of the representation and improved interpretability. After time complexity analysis, it could be seen that the proposed knowledge representation is lower than Trans series and RGCN in terms of complexity. Link prediction experiments were conducted on the traditional knowledge graph datasets FB15K, WN18, and recommendation domain datasets MovieLens-1M, Book-Crossing, Last. FM respectively. The experimental results show that on the MovieLens-1M dataset, hits @ 10 improves 5.5%12.1% and MRR improves 0. 090. 24, respectively. On the Book-Crossing dataset, hits @ 10 improves 3.5%-20.6%, and MRR improves 0.04-0.24 on average, respectively. And on the Last. FM dataset, hits@1 improves 0.3%-8.5% and MRR improves 0.04-0. 16 on average. It is better than the existing algorithms and verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Potato diseases and pests have a serious impact on the quality and yield of potatoes, and timely prevention and control of potato diseases and pests is essential. A rich knowledge reserve of potato diseases and pests is one of the most important prevention and control measures; however, valuable knowledge is buried in the massive data of potato diseases and pests, making it difficult for potato growers and managers to obtain and use it in a timely manner and to develop the potential of knowledge. Therefore, this paper explores the construction method of a knowledge graph for automatic knowledge extraction, which extracts the knowledge of potato diseases and pests scattered in heterogeneous data from multiple sources, organises it into a semantically related knowledge base, and provides potato growers with professional knowledge and timely guidance to effectively prevent and control potato diseases and pests. In this paper, a data corpus on potato diseases and pests, called PotatoRE, is first constructed. Then, a model of ALBert-BiLSTM-Self_Att-CRF is designed to extract knowledge from the corpus to form a triplet structure, which is imported into the Neo4j graph database for storage and visualisation. Furthermore, the performance of the model constructed in this paper is compared and verified using the datasets PotatoRE and People's Daily. The results show that compared to the SOTA models of ALBert BiLSTM-CRF and ALBert BiGRU-CRF, the accuracy of our model has been improved by 2.92% and 3.12%, respectively, using PotatoRE. Compared to the Bert BiLSTM-CRF model on two datasets, our model not only improves the accuracy, recall, and F1 values, but also has a higher efficiency. The model in this paper solves the problem of the difficult recognition of nested entities. On this basis, through comparative experiments, the TransH model is used to effectively represent the constructed knowledge graph, which lays the foundation for achieving inference, extension, and automatic updating of the knowledge base. The achievements of the thesis have made certain contributions to the automatic construction of large-scale knowledge bases. [ABSTRACT FROM AUTHOR]

The paper introduces partial set-theoretic relations for metasets and investigates their properties. Metaset is a concept of imprecise set designed to represent vague notions in Artificial Intelligence, whose idea stems from Boolean-valued techniques in classical set theory. The basic set relations are extended to functions valued in a Boolean algebra or unit interval. Important classical set theory axioms: extensionality and comprehension are formulated for metasets. The latter enables formal definitions of collections with blurred boundaries by using set-theory formulae. This facilitates representing and reasoning about imprecise data. [ABSTRACT FROM AUTHOR]

Simple Summary: Ontology-based AI promises a more flexible and cost-effective approach to the validation of cancer registry data over traditional approaches and may allow a more straightforward means of federating centralised data-harmonisation processes. The advantage of using ontologies is that they unite the conceptual and logical aspects of a given domain and, therefore, provide an inherent means for expressing data validation rule sets. The idiosyncrasies of data validation, however, impose certain constraints on the ontology structure of AI-based reasoning and require design patterns that are not immediately obvious. The design patterns presented in this work are generic to other domains beyond cancer registries and serve to point out the types of issues that validation ontologies have to confront, with proposed solutions for tackling them. Data validation in cancer registration is a critical operation but is resource-intensive and has traditionally depended on proprietary software. Ontology-based AI is a novel approach utilising machine reasoning based on axioms formally described in description logic. This is a different approach from deep learning AI techniques but not exclusive of them. The advantage of the ontology approach lies in its ability to address a number of challenges concurrently. The disadvantages relate to computational costs, which increase with language expressivity and the size of data sets, and class containment restrictions imposed by description logics. Both these aspects would benefit from the availability of design patterns, which is the motivation behind this study. We modelled the European cancer registry data validation rules in description logic using a number of design patterns and showed the viability of the approach. Reasoning speeds are a limiting factor for large cancer registry data sets comprising many hundreds of thousands of records, but these can be offset to a certain extent by developing the ontology in a modular way. Data validation is also a highly parallelisable process. Important potential future work in this domain would be to identify and optimise reusable design patterns, paying particular attention to avoiding any unintended reasoning efficiency hotspots. [ABSTRACT FROM AUTHOR]

Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians' writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge. [ABSTRACT FROM AUTHOR]

Objective: This study used the looking-at-nothing phenomenon to explore situation awareness (SA) and the effects of working memory (WM) load in driving situations. Background: While driving, people develop a mental representation of the environment. Since errors in retrieving information from this representation can have fatal consequences, it is essential for road safety to investigate this process. During retrieval, people tend to fixate spatial positions of visually encoded information, even if it is no longer available at that location. Previous research has shown that this "looking-at-nothing" behavior can be used to trace retrieval processes. Method: In a video-based laboratory experiment with 2 (WM) x 3 (SA level) within-subjects design, participants (N = 33) viewed a reduced screen and evaluated auditory statements relating to different SA levels on previously seen dynamic traffic scenarios while eye movements were recorded. Results: When retrieving information, subjects more frequently fixated emptied spatial locations associated with the information relevant for the probed SA level. The retrieval of anticipations (SA level 3) in contrast to the other SA level information resulted in more frequent gaze transitions that corresponded to the spatial dynamics of future driving behavior. Conclusion: The results support the idea that people build a visual-spatial mental image of a driving situation. Different gaze patterns when retrieving level-specific information indicate divergent retrieval processes. Application: Potential applications include developing new methodologies to assess the mental representation and SA of drivers objectively. [ABSTRACT FROM AUTHOR]

Bird's nest defect is an important cause of transmission line faults. To achieve accurate detection of bird nest defects in complex scenarios, a bird nest defect detection model for transmission lines was proposed that combines domain knowledge and occlusion reasoning networks. On the one hand, the model utilized the domain knowledge of the location of the bird's nest, using edge detection to obtain tower area information to constrain the location of candidate frames. This helps to reduce the false detection caused by complex backgrounds. On the other hand, on the basis of analyzing the occlusion characteristics of bird nests, the model employed occlusion reasoning networks that randomly erase features at the feature level to simulate the occlusion of bird nests in real scenes and improve the model's detection capability for occluded targets. Additionally, a multi‐scale feature fusion algorithm was designed in this paper to adapt the model to the scale variations of bird nests in aerial images. Experimental results demonstrate that the model outperforms advanced target detection models and other bird nest defect detection methods, with an AP50 of 78.8% and an AR10 of 72.4% for defect detection. [ABSTRACT FROM AUTHOR]

The advancement of sciences and technologies, economic challenges, increasing expectations, and consumerism result in a radical transformation of health and social care around the globe, characterized by foundational organizational, methodological, and technological paradigm changes. The transformation of the health and social care ecosystems aims at ubiquitously providing personalized, preventive, predictive, participative precision (5P) medicine, considering and understanding the individual's health status in a comprehensive context from the elementary particle up to society. For designing and implementing such advanced ecosystems, an understanding and correct representation of the structure, function, and relations of their components is inevitable, thereby including the perspectives, principles, and methodologies of all included disciplines. To guarantee consistent and conformant processes and outcomes, the specifications and principles must be based on international standards. A core standard for representing transformed health ecosystems and managing the integration and interoperability of systems, components, specifications, and artifacts is ISO 23903:2021, therefore playing a central role in this publication. Consequently, ISO/TC 215 and CEN/TC 251, both representing the international standardization on health informatics, declared the deployment of ISO 23903:2021 mandatory for all their projects and standards addressing more than one domain. The paper summarizes and concludes the first author's leading engagement in the evolution of pHealth in Europe and beyond over the last 15 years, discussing the concepts, principles, and standards for designing, implementing, and managing 5P medicine ecosystems. It not only introduces the theoretical foundations of the approach but also exemplifies its deployment in practical projects and solutions regarding interoperability and integration in multi-domain ecosystems. The presented approach enables comprehensive and consistent integration of and interoperability between domains, systems, related actors, specifications, standards, and solutions. That way, it should help overcome the problems and limitations of data-centric approaches, which still dominate projects and products nowadays, and replace them with knowledge-centric, comprehensive, and consistent ones. [ABSTRACT FROM AUTHOR]

Intelligent answering technology, which enables computers to solve problems automatically, is often used to develop tutorial systems, and has a wide range of application prospects. However, due to the lack of linguistic analysis and understanding methods, there are few researches on intelligent algorithms for solving kinematics problems. Developing such an algorithm is challenging, because solving kinematics problems is a complex task that includes text understanding, problem analysis, and automatic solution. To understand all these complexities involved in kinematics problems requires background knowledge. And only when an automatic solver contains a powerful internal knowledge representation system can it perform these tasks. We, thus, develop KinRob, an tutorial system for solving kinematics problems by combining neural network and ontology. Firstly, we propose an ontology for KinRob, which defines the knowledge of kinematics, and can help the robot understand a kinematics problem. Secondly, to match the text in natural language with the ontology, we propose a novel tagging scheme based on the kinematic problem understanding model in named entity recognition (NER). Finally, extensive experiments are conducted, and the experimental results show that the performance of the proposed method on a dataset of kinematic problems from authoritative sources better than the baseline algorithms. [ABSTRACT FROM AUTHOR]

Lifelong reinforcement learning is able to continually accumulate shared knowledge by estimating the inter‐task relationships based on training data for the learned tasks in order to accelerate learning for new tasks by knowledge reuse. The existing methods employ a linear model to represent the inter‐task relationships by incorporating task features in order to accomplish a new task without any learning. But these methods may be ineffective for general scenarios, where linear models build inter‐task relationships from low‐dimensional task features to high‐dimensional policy parameters space. Also, the deficiency of calculating errors from objective function may arise in the lifelong reinforcement learning process when some errors of policy parameters restrain others due to inter‐parameter correlation. In this paper, we develop a policy generation network that nonlinearly models the inter‐task relationships by mapping low‐dimensional task features to the high‐dimensional policy parameters, in order to represent the shared knowledge more effectively. At the same time, we propose a novel objective function of lifelong reinforcement learning to relieve the deficiency of calculating errors by adding weight constraints for errors. We empirically demonstrate that our method improves the zero‐shot policy performance across a variety of dynamical systems. [ABSTRACT FROM AUTHOR]

Knowledge graphs, as a novel knowledge representation method, originated from the fields of artificial intelligence and the semantic web. Its development can be traced back to the early 1990s. At that time, the rapid growth of the Internet led to an influx of massive information, but there lacked structured and semantic connections, making information retrieval and comprehension challenging. To address this issue, researchers began exploring a new representation method that could express the relationships between knowledge, thus introducing the concept of knowledge graphs. With the advancement of artificial intelligence technologies, particularly in natural language processing, graph databases, and machine learning, the application of knowledge graphs has expanded significantly. They are not only used for structured knowledge expression and querying but also extensively applied in areas such as search engine optimization, intelligent recommendation systems, and natural language understanding. Moreover, knowledge graphs have achieved remarkable success in industries such as healthcare, finance, and education. In the field of fashion, knowledge graph application has also shown immense potential and promising prospects. We firstly introduced the fundamental concepts, key technologies, and construction methods of knowledge graphs, along with their extensive applications in other fields. Subsequently, we focused on elucidating the necessity of researching knowledge graphs in the domain of fashion. With the rapid development of the fashion industry, intelligence and automation have become increasingly crucial. Knowledge graphs, as a powerful knowledge management and expression tool, are expected to further drive the digitization process in fashion design, production, and marketing. By structurally integrating data and knowledge from various aspects, the fashion industry can better grasp fashion trends, optimize the supply chain, promote sustainability, and offer personalized custom products and services. Currently, there have been several examples of applying knowledge graphs in the fashion industry. In the domain of fashion design, designers can utilize knowledge graphs to organize and manage elements from different cultures, historical periods, and popular trends, drawing inspiration to create more innovative and appealing clothing designs. In terms of fashion production, knowledge graphs can optimize the supply chain, enable material traceability, expedite the production process, reduce costs, and ensure product quality. Additionally, in fashion marketing, leveraging knowledge graphs to analyze user preferences, purchasing behavior, and social media activities enables businesses to offer personalized recommendations, thus enhancing customer satisfaction and loyalty. In addition, the knowledge graph can be used for the visual analysis of literature in the field of clothing, and the use of CiteSpace software for keyword cooccurrence to analyze the research status and trend of the clothing field. The current fashion knowledge graphs still face several challenges. Data quality is a significant issue, especially when dealing with unstructured data, ensuring the accuracy and completeness of the data becomes crucial. Additionally, the integration of knowledge from different data sources is a complex and critical task, necessitating solutions for data standardization and consistency. Moreover, constructing large-scale fashion knowledge graphs demands substantial human resources and time investment. With the continuous development of technologies like artificial intelligence, the application of knowledge graphs is set to become more intelligent and automated, presenting immense opportunities and advantages for the fashion industry. They can foster the collaborative development of the entire industry chain, accelerate the digital transformation of the fashion sector, and enhance the competitiveness and market share of enterprises. Moreover, the combination of knowledge graphs and artificial intelligence holds the potential to pioneer more intelligent applications in fashion design, pattern making, production, and marketing, providing consumers with personalized and high-quality clothing products and services. In conclusion, knowledge graphs possess significant development potential in the fashion domain. Integrating them with technologies such as artificial intelligence can propel the fashion industry towards greater intelligence and automation, injecting new vitality into innovation and development. While challenges exist presently, the continuous progress of technology and deepening application efforts lead us to believe that fashion knowledge graphs will embrace even broader prospects in the future, bringing profound impact and transformation to the entire industry. [ABSTRACT FROM AUTHOR]

A model for consciousness is proposed based on Nisargadatta Maharaj ‘s interpretation of Jnana Yoga/Advaita Vedanta. Concepts from modern physics, neuroscience, and information science are integrated into that framework. A novel understanding of information in the context of a system with consciousness is proposed. Information generated in the neurophysiology of a brain’s unique “wiring structure” is hypothesized to become dissociated from the neurophysiology and transformed into a universal format within a nonphysical domain in which consciousness is assumed to occur. Sharing features with Bohm’s Implicate Order, the nonphysical domain has nonlocal properties which support instant correlation of information throughout. The energy of a carrier is not needed to transport information in the nonphysical domain. This allows for low-energy neurological systems to transfer information associated with thought across large distances, as observed in telepathic psi phenomena. An experiment to test for the existence of a nonphysical dimension that supports instant telepathic information transfer is discussed. It is argued that three is the minimum number of aspects necessary to model a practical framework for consciousness. [ABSTRACT FROM AUTHOR]

Typhoons are destructive weather events. News media reports contain large amounts of typhoon information. Transforming this information into useful knowledge to provide a basis for mining typhoon knowledge and supporting disaster prevention and relief is urgently required to solve this problem. Knowledge representation can be used to address this problem, although it presents several challenges. These challenges lie in expressing the static and dynamic characteristics of typhoons and formalizing the knowledge representation method and making it suitable for machine processing. Moreover, the general Chinese time and space representation method is overly cumbersome for use in ontologies. The present study proposes an ontology-based typhoon event representation method that solves the representation problems of the typhoon static concept and dynamic features. Furthermore, it summarizes the fixed patterns of time and space in Chinese news and designs a time and space model suitable for typhoon event ontologies. From the ontology population, typhoon event ontology instances are created, and the typhoon event ontology model is applied to the analysis of typhoon processes, verifying the effectiveness of the typhoon event ontology model. [ABSTRACT FROM AUTHOR]

In a 2003-article, Sven Ove Hansson discusses the justificatory structure of a belief base, by highlighting that some beliefs of the belief base are held only because they are (deductively) justified by some other beliefs. He concludes that the relation between the justificatory structure of a belief base and the vulnerability of its beliefs (which in turn reflects their resistance to change) remains an open issue, both on a conceptual and on a technical level. Motivated by Hanssons' remarks, we introduce in this article a new interesting type of change-operation, called deductive belief change (contraction and revision), and abbreviated as DBC. DBC associates in a natural manner the deductive justification that the logical sentences of the language have, in the context of a belief base B, with their vulnerability relative to B. According to DBC, the more explicit B-beliefs imply a sentence φ, the more resistant to change φ is, with respect to B. We characterize DBC both axiomatically, in terms of natural postulates, and constructively, in terms of kernel belief change, illustrating its simple and intuitive structure. Interestingly enough, as we prove, kernel belief change (and its central specialization partial-meet belief change) already encodes a strong coupling between justificatory structure and vulnerability, as it implements DBC. Furthermore, we show that deductive belief revision, properly adapted to the belief-sets realm, is indistinguishable from Parikh's relevance-sensitive revision, a fundamental type of revision which, due to its favourable properties, constitutes a promising candidate for a variety of real-world applications. As a last contribution, we study relevance in the context of belief bases, and prove that kernel belief change respects Parikh's notion of relevance. [ABSTRACT FROM AUTHOR]

Background: Clinical decision support systems have been widely deployed to guide healthcare decisions on patient diagnosis, treatment choices, and patient management through evidence-based recommendations. These recommendations are typically derived from clinical practice guidelines created by clinical specialties or healthcare organizations. Although there have been many different technical approaches to encoding guideline recommendations into decision support systems, much of the previous work has not focused on enabling system generated recommendations through the formalization of changes in a guideline, the provenance of a recommendation, and applicability of the evidence. Prior work indicates that healthcare providers may not find that guideline-derived recommendations always meet their needs for reasons such as lack of relevance, transparency, time pressure, and applicability to their clinical practice. Results: We introduce several semantic techniques that model diseases based on clinical practice guidelines, provenance of the guidelines, and the study cohorts they are based on to enhance the capabilities of clinical decision support systems. We have explored ways to enable clinical decision support systems with semantic technologies that can represent and link to details in related items from the scientific literature and quickly adapt to changing information from the guidelines, identifying gaps, and supporting personalized explanations. Previous semantics-driven clinical decision systems have limited support in all these aspects, and we present the ontologies and semantic web based software tools in three distinct areas that are unified using a standard set of ontologies and a custom-built knowledge graph framework: (i) guideline modeling to characterize diseases, (ii) guideline provenance to attach evidence to treatment decisions from authoritative sources, and (iii) study cohort modeling to identify relevant research publications for complicated patients. Conclusions: We have enhanced existing, evidence-based knowledge by developing ontologies and software that enables clinicians to conveniently access updates to and provenance of guidelines, as well as gather additional information from research studies applicable to their patients' unique circumstances. Our software solutions leverage many well-used existing biomedical ontologies and build upon decades of knowledge representation and reasoning work, leading to explainable results. [ABSTRACT FROM AUTHOR]

Development inequality occurs in many parts of the world and is a global problem. Each country has made many efforts to reduce development inequality, especially developing countries. They are grouping the development inequality between regions to be used for future development decisions. However, the group often does not provide a complete representation. Many techniques are commonly used to classify development inequalities, such as the Williamson Index, Klassen typology, and shift-share analysis. But all three of them are not sufficiently able to visualize the proximity of inequality within and between regions. On the other hand, the development of computational science, such as data mining, can be used for data grouping tasks. This research intends to provide another alternative to grouping development inequalities using clustering tasks. The hierarchical agglomerative clustering (HAC) approach was used to group the GRDP data for 41 regions in the western part of Java Island, Indonesia. The 41 regions are spread out over three provinces: West Java, the Special Capital Region of Jakarta, and Banten. In this research, we use HAC techniques, namely single, average, complete, and ward linkage. The results showed that the single and average linkage techniques performed better than the other two. Single and average linkage performance can be seen from the silhouette coefficient of each cluster member, which is closer to +1. The cluster results can also show where each region stands in terms of development inequality. We divided cluster results into three groups to facilitate knowledge representation. Based on the average linkage technique, we have 35 regions that are members of the C1 cluster group; 3 regions are members of the C2 cluster group; and the other 3 regions are C3. [ABSTRACT FROM AUTHOR]

Framing effects concern the having of different attitudes towards logically or necessarily equivalent contents. Framing is of crucial importance for cognitive science, behavioral economics, decision theory, and the social sciences at large. We model a typical kind of framing, grounded in (i) the structural distinction between beliefs activated in working memory and beliefs left inactive in long term memory, and (ii) the topic- or subject matter-sensitivity of belief: a feature of propositional attitudes which is attracting growing research attention. We introduce a class of models featuring (i) and (ii) to represent, and reason about, agents whose belief states can be subject to framing effects. We axiomatize a logic which we prove to be sound and complete with respect to the class. [ABSTRACT FROM AUTHOR]

Traffic lights optimization is one of the principal components to lessen the traffic flow and travel time in an urban area. The present article seeks to introduce a novel procedure to design the traffic lights in a city using evolutionary-based optimization algorithms in combination with an ontology-based driving behavior simulation framework. Accordingly, an ontology-based knowledge base is introduced to provide a machine-understandable knowledge of roads and intersections, traffic rules, and driving behaviors. Then, a simulation environment is developed to inspect car behavior in real time. To optimize the traffic lights, a sine-based equation was defined for each traffic light, and the total travel time of the vehicles was considered as the cost function in the optimization algorithm. The optimization was performed with 5, 10, 15, 20, 25, and 30 vehicles in the urban areas. Based on the results, in contrast to uncontrolled intersections without traffic lights, optimized traffic lights can significantly contribute to total travel time-saving. To conclude, due to an escalation in the number of vehicles, the significance of optimized traffic lights has encountered an increase, and unoptimized traffic lights could increase total travel time even more than a city deprived of any traffic light. [ABSTRACT FROM AUTHOR]

The long-term maintenance of good condition for equipment is the basis of carrying out combat missions under the high technology and fast pace of modern war. However, the knowledge in the health management field at present has the characteristics of distribution, multi-source, heterogeneity and uncertainty, which seriously affects the efficiency of knowledge sharing and reuse. In order to improve the utilization of health management knowledge, an ontology-based knowledge representation method is proposed to describe knowledge in a unified and standardized way, and the classical ontology is extended to express the uncertain knowledge in the field of health management. In addition, to improve the maintenance and knowledge updating efficiency, a global ontology model and a hierarchy, time and activity (HTA) ontology model are constructed. This paper takes the guidance subsystem of a missile as an example to illustrate the process of knowledge modeling. The results show this method realizes knowledge sharing in the health management field and can provide decision support for health management of equipment. [ABSTRACT FROM AUTHOR]

Novel design creations often exist in the form of patents. It is well acknowledged that patents are a great source of design inspiration, therefore designers are encouraged to engage patents early in the design process. Studies of patent analysis have been carried out to benefit engineering design activities such as patent classification, technology forecasting, idea generation and emerging design-prior art comparison. However, the design intent behind inventions has received little attention in patent analysis. Designers can gain better design insight by looking at a patent from a systematic perspective starting from design intent to principal solutions. In this paper, an approach is proposed to extract and link the knowledge conveyed within patent descriptive sections to typical early engineering design stages, namely Motivation, Specification and Structure. This knowledge is then conceptualised into TRIZ engineering parameters and reconciled functional basis to enable cross-patent analysis. When compared to expert analysis, the proposed approach achieves an average of 63% accuracy with respect to Motivation, 56% with respect to Specification function and 44% with respect to Specification flow. Potential applications of such linking for early design use are then demonstrated through a prototype knowledge network. [ABSTRACT FROM AUTHOR]

In this internet-based world where a large amount of information exists on the World Wide Web (WWW), the information retrieval systems are mainly centred on topic-based classification. Numerous efforts and time have been consumed for relevant information extraction through major search engines if the data is not organised correctly. Ontologies proved to be an effective technique for representing and retrieving information, which is the key idea in semantic web applications. Ontologies not only help in efficient knowledge representation and information retrieval, but it also helps in mapping the hidden knowledge about a subject. This paper discusses the process and method of building ontology on the "Energy access" domain. The methodology is based on the tools used in developing the ontology. Several tools are used to create an ontology. Protégé is a most popular tool for ontology editing and for developing ontology.1 In this paper, various aspects like super class and subclass hierarchy, creating a subclass, instances for class illustration, query retrieval process, visualisation, and graph views have been demonstrated by using protégé software. [ABSTRACT FROM AUTHOR]

Knowledge representation and reasoning (KRR) in complex systems (CSs) usually require facts from multiple experts having complementary backgrounds to fuse together. Consequently, such KRR methods should provide universal modeling languages close to human reasoning, with increased expressiveness and efficient capabilities to describe uncertainties. In this context, this paper introduces a new modeling formalism entitled Hybrid Logic-Algebraic Relational Modeling which is based on combining logic, probabilities, numerical information and network representations. The behavior, facts and workflows in a CS can be described using an environment of interconnected models enclosing sets of logical rules with attached probabilistic trust factors and links regarding logical attributes and numerical parameters. The logical and probabilistic inference applied to the modeling environment gives valuable knowledge to designers and decision-makers so that they can develop procedures or take actions in managing the CS. In this article, the proposed approach is completely formalized, from concept to definition and proofs and up to implementation, while its usage is illustrated within a complex economic, logistical, economical and technical scenario. • Describe and analyze workflows and reason decisions in complex systems. • Fuse together knowledge from actors with different fields of expertise. • Build intuition and identify global emergent particularities and constraints. • Fuse together logic rules, trust factors and numerical parameters. • Build networks of models using specific domain propagation mechanisms. [ABSTRACT FROM AUTHOR]

Cognitive scientists have a long-standing interest in quantifying the structure of semantic memory. Here, we investigate whether a commonly used paradigm to study the structure of semantic memory, the semantic fluency task, as well as computational methods from network science could be leveraged to explore the underlying knowledge structures of academic disciplines such as psychology or biology. To compare the knowledge representations of individuals with relatively different levels of expertise in academic subjects, undergraduate students (i.e., experts) and preuniversity high school students (i.e., novices) completed a semantic fluency task with cue words corresponding to general semantic categories (i.e., animals, fruits) and specific academic domains (e.g., psychology, biology). Network analyses of their fluency networks found that both domain-general and domain-specific semantic networks of undergraduates were more efficiently connected and less modular than the semantic networks of high school students. Our results provide an initial proof-of-concept that the semantic fluency task could be used by educators and cognitive scientists to study the representation of more specific domains of knowledge, potentially providing new ways of quantifying the nature of expert cognitive representations. [ABSTRACT FROM AUTHOR]

Deep neural networks have proven to be able to learn rich internal representations, including for features that can also be used for different purposes than those the networks are originally developed for. In this paper, we are interested in exploring such ability and, to this aim, we propose a novel approach for investigating the internal behavior of networks trained for source code processing tasks. Using a simple autoencoder trained in the reconstruction of vectors representing programs (i.e., program embeddings), we first analyze the performance of the internal neurons in classifying programs according to different labeling policies inspired by real programming issues, showing that some neurons can actually detect different program properties. We then study the dynamics of the network from an information-theoretic standpoint, namely by considering the neurons as signaling systems and by computing the corresponding entropy. Further, we define a way to distinguish neurons according to their behavior, to consider them as formally associated with different abstract concepts, and through the application of nonparametric statistical tests to pairs of neurons, we look for neurons with unique (or almost unique) associated concepts, showing that the entropy value of a neuron is related to the rareness of its concept. Finally, we discuss how the proposed approaches for ranking the neurons can be generalized to different domains and applied to more sophisticated and specialized networks so as to help the research in the growing field of explainable artificial intelligence. [ABSTRACT FROM AUTHOR]

Extreme climate events disrupt the stable, established interactions among the components of the socio-economic and environmental systems. The disruptions affect reaching the targets defined by the United Nations Sustainable Development Goals 1, 2, 6, and 7 that aim at ending poverty and hunger and ensuring access to clean water, sanitation, and affordable energy. We have semantically modeled the interactions of climate change events with the components of the food, water, and energy systems in the 'Sustainable Development and Climate' (SDC) ontology. The domain ontology formalizes the impacts of the events on the implementations of the actions, plans, strategies, and policies that are described by the targets of the selected goals. To ensure interoperability with other ontologies, our ontology extends classes and properties of the top-level Basic Formal Ontology, mid-level Common Core Ontologies, and other upper-level ontologies. The publicly-available SDC ontology facilitates automated discovery, management, integration, and reasoning of the indicator data of the selected goals by reusing the extensive resources of the Common Core Ontologies for data and information modeling. The SDC knowledge model allows government agencies to identify the consequences of their planned actions on people, resources, and the environment in their country, and assess the impacts of the current climate change on the successful implementation of the requirements for goals 1, 2, 6, and 7. [ABSTRACT FROM AUTHOR]

Natural hazards are increasingly threatening our communities; hence it is imperative to provide communities with reliable information on possible impacts of such disasters, and on resilience measures that can be adopted to recover from disasters. To increase the engagement of various stakeholders in decision-making processes related to resilience to natural hazards, problem-specific information needs to be presented to them in a language understandable to non-experts in the field. To this end, this paper illustrates experimentation with low-code platforms for fast digitalization of resilience reports, incorporating the perspectives of various stakeholders in the analysis, thus making informed decision-making practicable. We present a co-creation-based approach to develop GIS-based user-friendly dashboards in support to the identification of resilience strategies against natural hazards; this approach has been developed within the framework of the European project ARCH. Urban areas are regarded as complex social-ecological systems whose various dimensions should be considered in this resilience endeavor, during all phases of the Disaster Risk Reduction and Climate Change Adaptation cycle. The work presented in this paper specifically targets the possible impacts and risks that might affect the cultural heritage subsystems of our cities, generally underrepresented in the international literature related to urban resilience assessment. We describe how we applied our approach to the Camerino municipality, a historic Italian town exposed to seismic risk, which was struck by a severe earthquake sequence in 2016–2017 and discuss the results of our experience. [ABSTRACT FROM AUTHOR]

In this article, we presents an effective semantic grounding scheme to long-term mobile robots for dynamic object search in open/dynamic home environments. The challenge of this strategy lies in dealing with situations where the robot suffers the home environment with an unknown layout of the object and the target object is moved without the robot’s knowledge. Therefore, a general probabilistic model and a semantic model are established by investigating typical spatial location relations between object and room type, in order to guide the robot to prioritize the search effort for spaces that are most promising to find the target object. Furthermore, a semantic grounding solution is proposed to address the mutual independence between objects, endowing the robot with inference capability for dynamic object search. Also, our proposal can be shared with other robots that have not explored the environment in order to perform object search tasks. The presented scheme is fully evaluated by extensive experiments in the real-world environment. The results demonstrate the validity of our approach by comparing it with the other three schemes in terms of search time and trajectory length, and show that the proposed method allows the mobile robot to efficiently and robustly find the dynamic object while achieving the human-like performance. [ABSTRACT FROM AUTHOR]

Eye movements have been examined as an index of attention and comprehension during reading in the literature for over 30 years. Although eye-movement measurements are acknowledged as reliable indicators of readers' comprehension skill, few studies have analyzed eye-movement patterns using network science. In this study, we offer a new approach to analyze eye-movement data. Specifically, we recorded visual scanpaths when participants were reading expository science text, and used these to construct scanpath networks that reflect readers' processing of the text. Results showed that low ability and high ability readers' scanpath networks exhibited distinctive properties, which are reflected in different network metrics including density, centrality, small-worldness, transitivity, and global efficiency. Such patterns provide a new way to show how skilled readers, as compared with less skilled readers, process information more efficiently. Implications of our analyses are discussed in light of current theories of reading comprehension. [ABSTRACT FROM AUTHOR]