Search

Driven by economic globalisation, various industries have developed a trend towards high specialisation and vertical division of labor, resulting in vast and intricate supply chain networks. However, unforeseen disasters can cause supply chain disruptions, subsequently impacting the regular production and operations of both upstream and downstream enterprises. To tackle this challenge, this study utilises Graph Neural Networks (GNNs) to synthesise graph structural data within the supply chain network, aiming to identify alternative suppliers to mitigate the impact of disruptions. We construct a large-scale knowledge graph to represent the realistic automotive supply chain network in China. Additionally, we propose a GNN-based framework that utilises information about interactions between buyers and suppliers to recommend alternative suppliers from the knowledge graph. Experimental results show that our approach significantly outperforms state-of-the-art GNN-based models, including Light-GCN and NGCF. Our research provides an intelligent and efficient perspective on supplier selection for the Chinese automobile industry. [ABSTRACT FROM AUTHOR]

The deployment of the Industrial Internet of Things (IIoT) in smart container terminals provides a foundation for sensing and recording all operational processes. However, little effort has been devoted to integrating the massive data regarding interoperability challenges, thus limiting the value of data in advancing the intelligent evolution of ports. In this research, we propose a graph-based approach to organise operational records semantically, thereby facilitating data-driven decision-making in container terminals. We first construct a knowledge graph for operational processes in container terminals, employing a tailored procedure for the automatic conversion of operational records into triples. By utilising the graph information, we propose a novel method that integrates reinforcement learning (RL) with a mathematical solver for optimising scheduling problems. The quay crane scheduling problem (QCSP) is illustrated as an example to elaborate on the technical details. Based on a dataset from a real-world container terminal, numerical studies demonstrate the superiority of the proposed framework in terms of information retrieval efficiency and solution quality compared with the traditional data organisation approach. [ABSTRACT FROM AUTHOR]

Modern supply chains are complex, interconnected systems that contain emergent, invisible dependencies. Lack of visibility often hinders effective risk planning and results in delayed discovery of supply chain problems, with examples ranging from product contamination, unsustainable production practices, or exposure to suppliers clustered in geographical areas prone to natural or man-made disasters. Initiatives that rely on manual collection of data often fail due to supply chain complexity and unwillingness of suppliers to share data. In this paper, we propose a neurosymbolic machine learning technique to proactively uncover hidden risks in supply chains and discover new information. Our method uses a combination of graph neural networks and knowledge graph reasoning. Unlike existing research our model is able to infer multiple types of hidden relationship risks, presenting a step change in automated supply chain surveillance. The approach has been tested on two empirical datasets from the automotive and energy industries, illustrating that it can provide inference in multiple types of links such as companies, products, production capabilities, certifications; thereby facilitating complex queries that go beyond who-supplies-whom. As such, additional risk insights can emerge from graph structure, providing practitioners with new knowledge. [ABSTRACT FROM AUTHOR]

The increasing complexity of industrial systems demands more effective and intelligent maintenance approaches to address manufacturing defects arising from faults in multiple asset modules. Traditional digital twin (DT) systems, however, face limitations in interoperability, knowledge sharing, and causal inference. As such, cognitive digital twins (CDTs) can add value by managing a collaborative web of interconnected systems, facilitating advanced cross-domain analysis and dynamic context considerations. This paper introduces a CDT system that leverages industrial knowledge graphs (iKGs) to support maintenance planning and operations. By employing a design structure matrix (DSM) to model dependencies and relationships, a semantic translation approach maps the knowledge into a graph-based representation for reasoning and analysis. An automatic solution generation mechanism, utilising graph sequencing with Louvain and PageRank algorithms, derives feasible solutions, which can be validated via simulation to minimise production disruption impacts. The CDT system can also identify potential disruptions in new product designs, thus enabling preventive actions to be taken. A case study featuring a print production manufacturing line illustrates the CDT system's capabilities in causal inference and solution explainability. The study concludes with a discussion of limitations and future directions, providing valuable guidelines for manufacturers aiming to enhance reactive and predictive maintenance strategies. [ABSTRACT FROM AUTHOR]

BackgroundOntologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts. We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.ResultsWe assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.ConclusionsThese findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

Micro, Small, and Medium-sized Enterprises (MSMEs) are essential for the growth and development of the country's economy, as they create jobs, generate income, and foster production and innovation. In recent years, credit risk assessment (CRA) has been an essential process used by financial institutions to evaluate the creditworthiness of MSMEs and determine the likelihood of default. Traditionally, CRA has relied on credit scores and financial statements, but with the advent of machine learning (ML) algorithms, lenders have a new tool at their disposal. By and large, ML algorithms are designed to classify borrowers based on their credit history and transactional data while leveraging the entity relationship involved in credit transactions. This study introduces an innovative knowledge graph-driven credit risk assessment model (RGCN-RF) based on the Relational Graph Convolutional Network (RGCN) and Random Forest (RF) algorithm. RGCN is employed to identify topological structures and relationships, which is currently nascent in traditional credit risk assessment methods. RF categorises MSMEs based on the enterprise embedding vector generated from RGCN. Extensive experimentation is conducted to assess model performance utilising the Indian MSMEs database. The balanced accuracy of 92% obtained using the RGCN-RF model demonstrates a considerable advancement over prior techniques in identifying risk-free enterprises. [ABSTRACT FROM AUTHOR]

Feature selection in Knowledge Graphs (KGs) is increasingly utilized in diverse domains, including biomedical research, Natural Language Processing (NLP), and personalized recommendation systems. This paper delves into the methodologies for feature selection (FS) within KGs, emphasizing their roles in enhancing machine learning (ML) model efficacy, hypothesis generation, and interpretability. Through this comprehensive review, we aim to catalyze further innovation in FS for KGs, paving the way for more insightful, efficient, and interpretable analytical models across various domains. Our exploration reveals the critical importance of scalability, accuracy, and interpretability in FS techniques, advocating for the integration of domain knowledge to refine the selection process. We highlight the burgeoning potential of multi-objective optimization and interdisciplinary collaboration in advancing KG FS, underscoring the transformative impact of such methodologies on precision medicine, among other fields. The paper concludes by charting future directions, including the development of scalable, dynamic FS algorithms and the integration of explainable AI principles to foster transparency and trust in KG-driven models.

Narrator disambiguation is a field within hadith science that studies unidentified narrators in hadith narration chains, also known as sanads. Sanads can be represented as graphs, with the nodes representing the narrators and the edges representing their relationships in the chain. The current methods for resolving the narrator disambiguation problem do not utilize the graph structure of the sanad, but by leveraging this structure, we can apply graph computational and deep learning techniques to identify narrators. This paper introduces a method that utilizes the sanad graph structure to identify all narrators in a given sanad. Our two-stage approach begins by generating a query embedding and identifying the top k narrator entities closest to the query embedding. We then use AraBERT to re-rank the top k narrators and make the final prediction. Our method achieves 94.6% accuracy on the validation set of AR-Sanad 280K dataset. Additionally, we present AR-Sanad 280K-v2, an updated dataset that represents real hadiths more accurately. [ABSTRACT FROM AUTHOR]

Summary: In sedimentology, the integration of intelligent engineering decision‐making with edge computing environments aims to furnish engineers and decision‐makers with precise, real‐time insights into sediment‐related issues. This approach markedly reduces data transfer time and response latency by harnessing the computational power of edge computing, thereby bolstering the decision‐making process. Concurrently, the establishment of a sediment knowledge graph serves as a pivotal conduit for disseminating sediment‐related knowledge in the realm of intelligent engineering decision‐making. Moreover, it facilitates a comprehensive exploration of the intricate evolutionary and transformative processes inherent in sediment materials. By unveiling the evolutionary trajectory of life on Earth, the sediment knowledge graph catalyzes a deeper understanding of our planet's history and dynamics. Relationship extraction, as a key step in knowledge graph construction, implements automatic extraction and establishment of associations between entities from a large amount of sedimentary literature data. However, sedimentological literature presents multi‐source heterogeneous features, which leads to a weak representation of hidden relationships, thus decreasing the accuracy of relationship extraction. In this article, we propose an attribute‐extended relation extraction approach (AERE), which is specifically designed for sedimentary relation extraction scenarios. First, context statements containing sediment entities are obtained from the literature. Then, a cohesive hierarchical clustering algorithm is used to extend the relationship attributes between sediments. Finally, mine the relationships between entities based on AERE. The experimental results show that the proposed model can effectively extract the hidden relations and exhibits strong robustness in dealing with redundant noise before and after sentences, which in turn improves the completeness of the relations between deposits. After the relationship extraction, a proprietary sediment knowledge graph is constructed with the extracted triads. [ABSTRACT FROM AUTHOR]

ABSTRACT The growing global interest in Geographic Information System/Science (GIS) programs has led to an increased demand for higher education in this field. However, students often struggle to identify suitable programs and faculty due to the overwhelming options and the lack of personalized guidance. This paper presents GISphere‐KG, an AI‐powered platform based on the GISphere project. It combines knowledge graph (KG) and large language models (LLMs) to enhance the search and recommendation of GIS‐related graduate programs. GISphere‐KG offers four key features: (1) interactive conversation that provides natural language responses to applicants' inquiries; (2) efficient information retrieval through semantic relationships built within the KG; (3) discovery of professors whose research interests align with those of the applicants, offering more choices within specific research fields; and (4) personalized program recommendations tailored to applicants' academic and career developments. Our platform aims to provide a user‐friendly tool that assists prospective students in achieving their career goals and enriching the geography community by attracting more talent and promoting global geography education. [ABSTRACT FROM AUTHOR]

ABSTRACT High‐precision, multi‐source landslide monitoring data are crucial for disaster prevention and management. These datasets provide explicit landslide descriptions. Integrating this data with landslide mechanisms, using knowledge graphs can enhance early monitoring and forecasting. However, traditional knowledge graphs often oversimplify landslide knowledge, failing to capture the complexity of geological environments and landslide evolution. Spatio‐temporal knowledge graphs face challenges in representing intricate relationships. A hypergraph (HG), where an edge connects multiple nodes, offers a better representation of these complexities. This paper proposes an HG‐based method for landslide knowledge representation, organizing multi‐source information and knowledge through binary or multiple relationships under specific temporal and spatial conditions. A case study of the Miaodian landslide, which experienced multiple sliding events, shows that the proposed landslide knowledge HG outperforms other knowledge graphs like YAGO, Geographic Knowledge Graph (GeoKG), and Geographic Evolutionary Knowledge Graph (GEKG) in completeness, accuracy, and redundancy, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

A majority of research in Computational Thinking (CT) mainly focuses on teaching coding to school students. However, CT involves more than just coding and includes other skills like algorithmic thinking. The current study developed an Online Inquiry-based Learning Platform for Computational Thinking (CT-ONLINQ) that follows Inquiry-Based Learning (IBL) pedagogy to support CT activities. IBL-based CT steps include algorithm design, analysis, and comparison of algorithms. Also, the platform allows students to explore multiple solutions to a problem and provides multiple-try feedback with hints as support during problem-solving activities. The hint generation strategy uses a Knowledge Graph that captures knowledge about the problem's solution in a machine-processible form. A six-week quasi-experimental study was conducted to determine the effectiveness of multiple-try feedback with hints on students' CT skills. The study included 79 high school students: 41 students as part of the experimental group (EG) were provided problem-specific hints, and 38 as part of the control group (CG) with CT-general hints. The results showed that the students in the EG group improved their CT skills significantly more than those in the CG group. In addition, the study also evaluates the effectiveness of intervention considering biases in gender and prior coding experience. Female students performed better than male students in both groups after the intervention. Furthermore, in EG group, observations showed that students without coding experience performed better than their counterparts with experience. The findings suggest that the IBL-based CT activity on CT-ONLINQ can be deployed to improve the CT skills of school students. [ABSTRACT FROM AUTHOR]

In the rapidly advancing information era, various human behaviors are being precisely recorded in the form of data, including identity information, criminal records, and communication data. Law enforcement agencies can effectively maintain social security and precisely combat criminal activities by analyzing the aforementioned data. In comparison to traditional data analysis methods, deep learning models, relying on the robust computational power in cloud centers, exhibit higher accuracy in extracting data features and inferring data. However, within the architecture of cloud centers, the transmission of data from end devices introduces significant latency, hindering real-time inference of data. Furthermore, low-latency edge computing architectures face limitations in direct deployment due to relatively weak computing and storage capacities of nodes. To address these challenges, a lightweight distributed knowledge graph completion architecture is proposed. Firstly, we introduce a lightweight distributed knowledge graph completion architecture that utilizes knowledge graph embedding for data analysis. Subsequently, to filter out substandard data, a personnel data quality assessment method named PDQA is proposed. Lastly, we present a model pruning algorithm that significantly reduces the model size while maximizing performance, enabling lightweight deployment. In experiments, we compare the effects of 11 advanced models on completing the knowledge graph of public security personnel information. The results indicate that the RotatE model outperforms other models significantly in knowledge graph completion, with the pruned model size reduced by 70%, and hits@10 reaching 86.97%. [ABSTRACT FROM AUTHOR]

Over the past ten years, large amounts of original research data related to Earth system science have been made available at a rapidly increasing rate. Such growing data stock helps researchers understand the human-Earth system across different fields. A substantial amount of this data is published by geoscientists as open-access in authoritative journals. If the information stored in this literature is properly extracted, there is significant potential to build a domain knowledge base. However, this potential remains largely unfulfilled in geoscience, with one of the biggest obstacles being the lack of publicly available related corpora and baselines. To fill this gap, the Earth Science Data Corpus (ESDC), an academic text corpus of 600 abstracts, was built from the international journal Earth System Science Data (ESSD). To the best of our knowledge, ESDC is the first corpus with the needed detail to provide a professional training dataset for knowledge extraction and construction of domain-specific knowledge graphs from massive amounts of literature. The production process of ESDC incorporates both the contextual features of spatiotemporal entities and the linguistic characteristics of academic literature. Furthermore, annotation guidelines and procedures tailored for Earth science data are formulated to ensure reliability. ChatGPT with zero- and few-shot prompting, BARTNER generative, and W2NER discriminative models were trained on ESDC to evaluate the performance of the name entity recognition task and showed increasing performance metrics, with the highest achieved by BARTNER. Performance metrics for various entity types output by each model were also assessed. We utilized the trained BARTNER model to perform model inference on a larger unlabeled literature corpus, aiming to automatically extract a broader and richer set of entity information. Subsequently, the extracted entity information was mapped and associated with the Earth science data knowledge graph. Around this knowledge graph, this paper validates multiple downstream applications, including hot topic research analysis, scientometric analysis, and knowledge-enhanced large language model question-answering systems. These applications have demonstrated that the ESDC can provide scientists from different disciplines with information on Earth science data, help them better understand and obtain data, and promote further exploration in their respective professional fields. [ABSTRACT FROM AUTHOR]

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users' potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users' potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. [ABSTRACT FROM AUTHOR]

Research in Natural Language Processing (NLP) is increasing rapidly; as a result, a large number of research papers are being published. It is challenging to find the contributions of the research paper in any specific domain from the huge amount of unstructured data. There is a need for structuring the relevant contributions in Knowledge Graph (KG). In this paper, we describe our work to accomplish four tasks toward building the Scientific Knowledge Graph (SKG). We propose a pipelined system that performs contribution sentence identification, phrase extraction from contribution sentences, Information Units (IUs) classification, and organize phrases into triplets (subject, predicate, object) from the NLP scholarly publications. We develop a multitasking system (ContriSci) for contribution sentence identification with two supporting tasks, viz.Section Identification and Citance Classification. We use the Bidirectional Encoder Representations from Transformers (BERT)—Conditional Random Field (CRF) model for the phrase extraction and train with two additional datasets: SciERC and SciClaim. To classify the contribution sentences into IUs, we use a BERT-based model. For the triplet extraction, we categorize the triplets into five categories and classify the triplets with the BERT-based classifier. Our proposed approach yields the F1 score values of 64.21%, 77.47%, 84.52%, and 62.71% for the contribution sentence identification, phrase extraction, IUs classification, and triplet extraction, respectively, for non-end-to-end setting. The relative improvement for contribution sentence identification, IUs classification, and triplet extraction is 8.08, 2.46, and 2.31 in terms of F1 score for the NLPContributionGraph (NCG) dataset. Our system achieves the best performance (57.54% F1 score) in the end-to-end pipeline with all four sub-tasks combined. We make our codes available at: https://github.com/92Komal/pipeline_triplet_extraction. [ABSTRACT FROM AUTHOR]

Detecting texts that contain semantic-level new information is not straightforward. The problem becomes more challenging for research articles. Over the years, many datasets and techniques have been developed to attempt automatic novelty detection. However, the majority of the existing textual novelty detection investigations are targeted toward general domains like newswire. A comprehensive dataset for scientific novelty detection is not available in the literature. In this paper, we present a new triplet-based corpus (SciND) for scientific novelty detection from research articles via knowledge graphs. The proposed dataset consists of three types of triples (i) triplet for the knowledge graph, (ii) novel triplets, and (iii) non-novel triplets. We build a scientific knowledge graph for research articles using triplets across several natural language processing (NLP) domains and extract novel triplets from the paper published in the year 2021. For the non-novel articles, we use blog post summaries of the research articles. Our knowledge graph is domain-specific. We build the knowledge graph for seven NLP domains. We further use a feature-based novelty detection scheme from the research articles as a baseline. Moreover, we show the applicability of our proposed dataset using our baseline novelty detection algorithm. Our algorithm yields a baseline F1 score of 72%. We show analysis and discuss the future scope using our proposed dataset. To the best of our knowledge, this is the very first dataset for scientific novelty detection via a knowledge graph. We make our codes and dataset publicly available at https://github.com/92Komal/Scientific_Novelty_Detection. [ABSTRACT FROM AUTHOR]

Understanding complex biological pathways, including gene–gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the literature. Large‐scale language models (LLMs) trained on extensive text corpora contain rich biological information, and they can be mined as a biological knowledge graph. This study assesses 21 LLMs, including both application programming interface (API)‐based models and open‐source models in their capacities of retrieving biological knowledge. The evaluation focuses on predicting gene regulatory relations (activation, inhibition, and phosphorylation) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway components. Results indicated a significant disparity in model performance. API‐based models GPT‐4 and Claude‐Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open‐source models lagged behind their API‐based counterparts, whereas Falcon‐180b and llama2‐7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations, respectively. The KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon‐180b and 0.2207 for llama2‐7b. Our study suggests that LLMs are informative in gene network analysis and pathway mapping, but their effectiveness varies, necessitating careful model selection. This work also provides a case study and insight into using LLMs das knowledge graphs. Our code is publicly available at the website of GitHub (Muh‐aza). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southeast University (English Edition) is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multi-hop reasoning provides a means for inferring indirect relationships and missing information from knowledge graphs (KGs). Reinforcement learning (RL) was recently employed for multi-hop reasoning. Although RL-based methods provide explainability, they face challenges such as sparse rewards, spurious paths, large action spaces, and long training and running times. In this study, we present a novel approach that combines KG embeddings and RL strategies for multi-hop reasoning called path-based multi-hop reasoning (PMHR). We address the issues of sparse rewards and spurious paths by incorporating a well-designed reward function that combines soft rewards with rule-based rewards. The rewards are adjusted based on the target entity and the path to it. Furthermore, we perform action filtering and utilize the vectors of entities and relations acquired through KG embeddings to initialize the environment, thereby significantly reducing the runtime. Experiments involving a comprehensive performance evaluation, efficiency analysis, ablation studies, and a case study were performed. The experimental results on benchmark datasets demonstrate the effectiveness of PMHR in improving KG reasoning accuracy while preserving interpretability. Compared to existing state-of-the-art models, PMHR achieved Hit@1 improvements of 0.63%, 2.02%, and 3.17% on the UMLS, Kinship, and NELL-995 datasets, respectively. PMHR provides not only improved reasoning accuracy and explainability but also optimized computational efficiency, thereby offering a robust solution for multi-hop reasoning. [ABSTRACT FROM AUTHOR]

Automating the job hazard analysis (JHA) process is an urgent requirement in the construction safety management field due to limitations of the conventional process. The manual nature of conducting the JHA and the dynamic environment of construction sites make it necessary to perform the analysis before commencing the job and to then regularly update it in accordance with changes in the construction plans. With this in mind, this research aims to develop an automated approach to support safety personnel during the JHA process. In seeking to automate the JHA process, the nature of construction accidents, hazards and risk assessment needs to be studied in light of the theoretical knowledge on accident causation. Thus, this research was designed according to the constructive research approach to develop a job hazard analysis knowledge graph (JHAKG) to automate the JHA process. The JHAKG incorporated an ontology (O-JHAKG) built according to the systematic ontology development method, METHONTOLOGY, which formalises both explicit and implicit knowledge inherent in the JHA process. The data were imported to the JHAKG from an incident database using rule-based natural language processing (NLP) which helped to extract implicit information not evident in the traditional JHA document. The validation of the JHAKG was conducted in two stages: the first stage validated the information extraction process by calculating performance metrics, while the second stage validated the data population process and the JHAKG's reasoning capability. The overall research resulted in a comprehensive JHAKG with advanced inferencing capabilities which can assist safety personnel in effectively executing the JHA process. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence models for data driven decision making has a lot of potential for the manufacturing sector. Nevertheless, applications in industry are currently limited to the actionable insights one can discover from the available data and knowledge of a manufacturing system. We call the process to obtain such insights "ad hoc analysis". Ad hoc analysis at system level is very complex in an industrial setting due to the inherent heterogeneity of data and existence of data silos, the lack of information and knowledge formalization, and the inability to meaningfully and efficiently reason about the data, information and knowledge. In this paper, we provide and outline a framework for ad hoc analysis in manufacturing based on knowledge graphs and influenced by the metamodelling paradigm. We derive its requirements and key elements from an analysis of several industry application cases. We show how manufacturing data, information and knowledge can be combined and made actionable using this framework. The framework supports workflows and tools for the data consumer (i.e., data scientist), and for the knowledge engineer. Furthermore, we show how the framework is integrated with existing data sources. Then, we discuss how we applied the framework to several application cases. We discuss how the framework contributes when applied, and what challenges still remain. [ABSTRACT FROM AUTHOR]

Prospectivity mapping based on deep learning typically requires substantial amounts of geological feature information from known mineral deposits. Due to the limited spatial distribution of ore deposits, the training of predictive models is often hampered by insufficient positive samples. Meanwhile, data-driven mineral prospectivity mapping often overlooks domain knowledge and expert experience, leading to poor interpretability of predictive results. To address this problem, we employed the Gaussian mixture model (GMM) for spatial feature classification to expand the number of positive samples. The approach integrated the embedding of geological map knowledge graphs with geological exploration data to enhance the knowledge constraints of the prospecting model, which enabled the integration of knowledge with data. Considering the complex spatial structure of geological elements, a bi-branch utilizing the 1-dimensional convolutional neural network (CNN1D) and graph convolutional network (GCN) was used to extract geological spatial features for model training and prediction. To validate the effectiveness of the method, a gold mineralization prediction study was conducted in the Wulonggou area (Qinghai province, western China). The results indicate that, when the number of GMM spatial feature classifications was 17, the positive-to-negative sample ratio was optimal, and the embedding of the knowledge graph controlled the prediction area distribution effectively, which demonstrated strong consistency between the prospecting area and the known mineral deposits. Compared with the predictions by CNN1D, the fused prediction model of CNN1D and GCN yielded higher accuracy. Our model identified 11 classes of mineralization potential areas and provides geological interpretations for different prediction categories. [ABSTRACT FROM AUTHOR]

An intelligent tutoring system is a computer-based educational tool designed to provide adaptive learning environment to learners, mimicking the role of a human tutor. Its most typical areas of application are language learning, mathematics education, programming courses and medical training. Intelligent Tutoring Systems are based on the knowledge-module that is holding the system's knowledge in a well-structured format. Considering the current state of the art knowledge-module representations, a model that can represent evolving information is lacking. Representing evolving information is needed for those tutoring systems that are working with dynamically changing domains, e.g., software science. In this paper a new combined model is presented that is based on the ontology model and the fundamentals of knowledge space theory. The proposed model introduces the term of abstract time to be able to formulate an evolving knowledge graph. This paper introduces the term of evoking-hooks that makes it possible to realize connections between external domain elements and the nodes of the proposed model. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features. Design/methodology/approach: This study represents user-generated images "cognitive" in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery. Findings: This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation. Practical implications: This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques. Social implications: This study advances the understanding of user-generated images' impact on hotel selection, helping users make better decisions and enabling marketers to understand users' preferences and trends. Originality/value: This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model. [ABSTRACT FROM AUTHOR]

Background: Timely and accurate prediction of disease progress is crucial for facilitating early intervention and treatment for various chronic diseases. However, due to the complicated and longitudinal nature of disease progression, the capacity and completeness of clinical data required for training deep learning models remains a significant challenge. This study aims to explore a new method that reduces data dependency and achieves predictive performance comparable to existing research. Methods: This study proposed DAPNet, a deep learning-based disease progression prediction model that solely utilizes the comorbidity duration (without relying on multi-modal data or comprehensive medical records) and disease associations from biomedical knowledge graphs to deliver high-performance prediction. DAPNet is the first to apply multi-view graph contrastive learning to disease progression prediction tasks. Compared with other studies on comorbidities, DAPNet innovatively integrates molecular-level disease association information, combines disease co-occurrence and ICD10, and fully explores the associations between diseases; Results: This study validated DAPNet using a de-identified clinical dataset derived from medical claims, which includes 2,714 patients and 10,856 visits. Meanwhile, a kidney dataset (606 patients) based on MIMIC-IV has also been constructed to fully validate its performance. The results showed that DAPNet achieved state-of-the-art performance on the severe pneumonia dataset (F1=0.84, with an improvement of 8.7%), and outperformed the six baseline models on the kidney disease dataset (F1=0.80, with an improvement of 21.3%). Through case analysis, we elucidated the clinical and molecular associations identified by the DAPNet model, which facilitated a better understanding and explanation of potential disease association, thereby providing interpretability for the model. Conclusions: The proposed DAPNet, for the first time, utilizes comorbidity duration and disease associations network, enabling more accurate disease progression prediction based on a multi-view graph contrastive learning, which provides valuable insights for early diagnosis and treatment of patients. Based on disease association networks, our research has enhanced the interpretability of disease progression predictions. [ABSTRACT FROM AUTHOR]

Introduction: Tomato leaf pests and diseases pose a significant threat to the yield and quality of Q6 tomatoes, highlighting the necessity for comprehensive studies on effective control methods. Methods: Current control measures predominantly rely on experience and manual observation, hindering the integration of multi-source data. To address this, we integrated information resources related to tomato leaf pests and diseases from agricultural standards documents, knowledge websites, and relevant literature. Guided by domain experts, we preprocessed this data to construct a sample set. Results: We utilized the Named Entity Recognition (NER) model ALBERT-BiLSTM-CRF to conduct end-to-end knowledge extraction experiments, which outperformed traditional models such as 1DCNN-CRF and BiLSTM-CRF, achieving a recall rate of 95.03%. The extracted knowledge was then stored in the Neo4j graph database, effectively visualizing the internal structure of the knowledge graph. Discussion: We developed a digital diagnostic system for tomato leaf pests and diseases based on the knowledge graph, enabling graphical management and visualization of pest and disease knowledge. The constructed knowledge graph offers insights for controlling tomato leaf pests and diseases and provides new research directions for pest control in other crops. [ABSTRACT FROM AUTHOR]

ABSTRACT Current geographic knowledge graph research mainly focuses on static knowledge coupling through analysis of web text and maps, but its knowledge expression model fails to incorporate knowledge mechanisms such as process evolution and interactions. The geo‐scene is a synthesis of various geographic elements which prescribes spatial, temporal, semantic, attribute, and interrelationships. It has demonstrated its utility in facilitating geocomputation, spatiotemporal simulation and reasoning related to geo‐process. Therefore, developing a geo‐scene‐based knowledge graph is significant to improve the ability of spatiotemporal knowledge representation and service. In this study, we first analyze the basic characteristics of geo‐scenes and try to emphasize the evolution of geo‐scenes through redefining the ontologies ‘event’, ‘process’ and ‘state’. To describe the dynamic nested structure of geo‐scenes, we propose a geo‐scene knowledge representation framework, and illustrate the representation of a geo‐scene and its inner relationships. Moreover, to keep the integrity, interaction and dynamic of geo‐scenes, we develop a event‐process centered geo‐scene knowledge organization method. Furthermore, we have tested the utility of our proposed representation by the Beijing 731 heavy rainstorm. The case study represents heavy rainstorm to demonstrate the complicated structure of the disaster scene, and the support for precipitation process simulation, early warning information organization, and disaster situation information query. The proposed representation provides an idea for the construction of geo‐dynamic knowledge graphs, encodes both static and dynamic geographic knowledge, makes explicit the evolution and interaction knowledge in geo‐scenes, and expands the application service of geo‐scene knowledge graph in the era of GeoAI. [ABSTRACT FROM AUTHOR]

Background and aims Design and setting Participants and Measurements Findings Conclusions Amphetamine‐type stimulants are the second‐most used illicit drugs globally, yet there are no US Food and Drug Administration (FDA)‐approved treatments for amphetamine‐type stimulant use disorders (ATSUD). The aim of this study was to utilize a drug discovery framework that integrates artificial intelligence (AI)‐based drug prediction, clinical corroboration and mechanism of action analysis to identify FDA‐approved drugs that can be repurposed for treating ATSUD.An AI‐based knowledge graph model was first utilized to prioritize FDA‐approved drugs in their potential efficacy for treating ATSUD. Among the top 10 ranked candidate drugs, ketamine represented a novel candidate with few studies examining its effects on ATSUD. We therefore conducted a retrospective cohort study to assess the association between ketamine and ATSUD remission using US electronic health record (EHR) data. Finally, we analyzed the potential mechanisms of action of ketamine in the context of ATSUD.ATSUD patients who received anesthesia (n = 3663) or were diagnosed with depression (n = 4328) between January 2019 and June 2022. The outcome measure was the diagnosis of ATSUD remission within one year of the drug prescription.Ketamine for anesthesia in ATSUD patients was associated with greater ATSUD remission compared with other anesthetics: hazard ratio (HR) = 1.58, 95% confidence interval (CI) = 1.15–2.17. Similar results were found for ATSUD patients with depression when comparing ketamine with antidepressants and bupropion/mirtazapine with HRs of 1.51 (95% CI = 1.14–2.01) and 1.68 (95% CI = 1.18–2.38), respectively. Functional analyses demonstrated that ketamine targets several ATSUD‐associated pathways including neuroactive ligand‐receptor interaction and amphetamine addiction.There appears to be an association between clinician‐prescribed ketamine and higher remission rates in patients with amphetamine‐type stimulant use disorders. [ABSTRACT FROM AUTHOR]

Knowledge graph link prediction is crucial for constructing triples in knowledge graphs, which aim to infer whether there is a relation between the entities. Recently, graph neural networks and contrastive learning have demonstrated superior performance compared with traditional translation-based models; they successfully extracted common features through explicit linking between entities. However, the implicit associations between entities without a linking relationship are ignored, which impedes the model from capturing distant but semantically rich entities. In addition, directly applying contrastive learning based on random node dropout to link prediction tasks, or limiting it to triplet-level, leads to constrained model performance. To address these challenges, we design an implicit feature extraction module that utilizes the clustering characteristics of latent vector space to find entities with potential associations and enrich entity representations by mining similar semantic features from the conceptual level. Meanwhile, the subgraph mechanism is introduced to preserve the structural information of explicitly connected entities. Implicit semantic features and explicit structural features serve as complementary information to provide high-quality self-supervised signals. Experiments are conducted on three benchmark knowledge graph datasets. The results validate that our model outperforms the state-of-the-art baselines in link prediction tasks. [ABSTRACT FROM AUTHOR]

To address the issue of efficiently reusing the massive amount of unstructured knowledge generated during the handling of track circuit equipment faults and to automate the construction of knowledge graphs in the railway maintenance domain, it is crucial to leverage knowledge extraction techniques to efficiently extract relational triplets from fault maintenance text data. Given the current lag in joint extraction technology within the railway domain and the inefficiency in resource utilization, this paper proposes a joint extraction model for track circuit entities and relations, integrating Global Pointer and tensor learning. Taking into account the associative characteristics of semantic relations, the nesting of domain-specific terms in the railway sector, and semantic diversity, this research views the relation extraction task as a tensor learning process and the entity recognition task as a span-based Global Pointer search process. First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). Finally, comparative experiments with existing mainstream joint extraction models were conducted, and the proposed model's excellent performance was validated on the English public datasets NYT and WebNLG, the Chinese public dataset DuIE, and a private track circuit dataset. The F1 scores on the NYT, WebNLG, and DuIE public datasets reached 92.1%, 92.7%, and 78.2%, respectively. [ABSTRACT FROM AUTHOR]

Decoding gene regulatory networks is essential for understanding the mechanisms underlying many complex diseases. GENET is developed, an automated system designed to extract and visualize extensive molecular relationships from published biomedical literature. Using natural language processing, entities and relations are identified from a randomly selected set of 1788 scientific articles, and visualized in a filterable knowledge graph. The performance of GENET is evaluated and compared with existing methods. The named entity recognition model has achieved an overall precision of 94.23% (4835/5131; 93.56–94.84%), recall of 97.72% (4835/4948; 97.27–98.10%), and an F1 score of 95.94%. The relation extraction model has demonstrated an overall precision of 91.63% (2593/2830; 90.55–92.59%), recall of 89.17% (2593/2908; 87.99–90.25%), and an F1 score of 90.38%. GENET significantly outperforms existing methods in extracting molecular relationships (P < 0.001). Additionally, GENET has successfully predicted WNT family member 4 regulates insulin‐like growth factor 2 via signal transducer and activator of transcription 3 in colon cancer. With RNA sequencing data and multiple immunofluorescence, the authenticity of this prediction is validated, supporting the promising feasibility of GENET. [ABSTRACT FROM AUTHOR]

The traditional model training approach based on negative sampling randomly samples a portion of negative samples for training, which can easily overlook important negative samples and adversely affect the training of knowledge graph embedding models. Some researchers have explored non-sampling model training frameworks that use all unobserved triples as negative samples to improve model training performance. However, both training methods inevitably introduce false negative samples and easy-to-separate negative samples that are far from the model's decision boundary, and they do not consider the adverse effects of long-tail entities and relations during training, thus limiting the improvement of model training performance. To address this issue, we propose a universal knowledge graph embedding framework based on high-quality negative sampling and weighting, called HNSW-KGE. First, we conduct pre-training based on the NS-KGE non-sampling training framework to quickly obtain an initial set of relatively high-quality embedding vector representations for all entities and relations. Second, we design a candidate negative sample set construction strategy that samples a certain number of negative samples that are neither false negatives nor easy-to-separate negatives for all positive triples, based on the embedding vectors obtained from pre-training. This ensures the provision of high-quality negative samples for model training. Finally, we apply weighting to the loss function based on the frequency of the entities and relations appearing in the triples to mitigate the adverse effects of long-tail entities and relations on model training. Experiments conducted on benchmark datasets FB15K237 and WN18RR using various knowledge graph embedding models demonstrate that our proposed framework HNSW-KGE, based on high-quality negative sampling and weighting, achieves better training performance and exhibits versatility, making it applicable to various types of knowledge embedding models. [ABSTRACT FROM AUTHOR]

The proliferation of the Internet and mobile devices has made it increasingly easy to propagate rumors on social media. Widespread rumors can incite public panic and have detrimental effects on individuals. In recent years, researchers have found that both the spatial structure of rumor diffusion and the temporal features of propagation can be effective in identifying rumors. However, existing methods tend to focus on either spatial structure or temporal information in isolation, and few models can effectively capture both types of information. Additionally, most existing methods treat continuously changing temporal information as static snapshots, neglecting the precise timing of propagation. Moreover, as users repost and comment, background knowledge associated with the posts also evolves dynamically, which is often ignored. To address these limitations, we propose CGNKP (Continuous-time Dynamic Graph Networks integrated with Knowledge Propagation), a model that jointly captures the spatial structure and continuous-time features of post propagation to fully understand the dynamics of background knowledge. Specifically, we introduce a novel method for encoding continuous-time dynamic graphs, modeling the propagation process through two dynamic graphs: a temporal propagation graph (for posts diffusion) and a temporal knowledge graph (for knowledge diffusion). Extensive experiments on real-world datasets demonstrate that CGNKP significantly outperforms multiple strong baselines, achieving accuracies of 0.861 on the Twitter15 dataset and 0.903 on the Twitter16 dataset. [ABSTRACT FROM AUTHOR]

Background: Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches. Results: We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development. Conclusion: This study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5's HydraScreen and Strateos' automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs. Scientific contribution: We present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery. [ABSTRACT FROM AUTHOR]

Research suggests that individuals who experience prolonged exposure to stress may be at higher risk for developing psychological stress disorders. Currently, psychological stress is primarily evaluated by professional physicians using rating scales, which may be prone to subjective biases and limitations of the scales. Therefore, it is imperative to explore more objective, accurate, and efficient biomarkers for evaluating the level of psychological stress in an individual. In this study, we utilized 4D data-independent acquisition (4D-DIA) proteomics for quantitative protein analysis, and then employed support vector machine (SVM) combined with SHAP interpretation algorithm to identify potential biomarkers for psychological stress levels. Biomarkers validation was subsequently achieved through machine learning classification and a substantial amount of a priori knowledge derived from the knowledge graph. We performed cross-validation of the biomarkers using two batches of data, and the results showed that the combination of Glyceraldehyde-3-phosphate dehydrogenase and Fibronectin yielded an average area under the curve (AUC) of 92%, an average accuracy of 86%, an average F1 score of 79%, and an average sensitivity of 83%. Therefore, this combination may represent a potential approach for detecting stress levels to prevent psychological stress disorders. [ABSTRACT FROM AUTHOR]

Multimodal sentiment analysis is an important and active research field. Most methods construct fusion modules based on unimodal representations generated by pretrained models, which lack the deep interaction of multimodal information, especially the rich semantic-emotional information embedded in text. In addition, previous studies have focused on capturing modal coherence information and ignored differentiated information. We propose a text-enhanced multi-interactive attention and multitask learning network (TEMM). First, syntactic dependency graphs and sentiment graphs of the text are constructed, and additional graph embedding representations of the text are obtained using graph convolutional networks and graph attention networks. Then, self-attention and cross-modal attention are applied to explore intramodal and intermodal dynamic interactions, using text as the main cue. Finally, a multitask learning framework is constructed to exert control over the information flow by monitoring the mutual information between the unimodal and multimodal representations and exploiting the classification properties of the unimodal modality to achieve a more comprehensive focus on modal information. The experimental results on the CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets show that the proposed model outperforms state-of-the-art models. [ABSTRACT FROM AUTHOR]

Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs), which plays an important role in knowledge engineering. Recently, EA with dangling entities has been proposed as a more realistic setting, which assumes that not all entities have corresponding equivalent entities. In this paper, we focus on this setting. Some work has explored this problem by leveraging translation API, pre-trained word embeddings, and other off-the-shelf tools. However, these approaches over-rely on the side information (e.g., entity names) and fail to work when the side information is absent. On the contrary, they still insufficiently exploit the most fundamental graph structure information in KG. To improve the exploitation of the structural information, we propose a novel entity alignment framework called Structure-aware Wasserstein Graph Contrastive Learning (SWGCL), which is refined on three dimensions: (i) Model. We propose a novel Gated Graph Attention Network to capture local and global graph structure attention. (ii) Training. Two learning objectives: contrastive learning and optimal transport learning, are designed to obtain distinguishable entity representations. (iii) Inference. In the inference phase, a PageRank-based method HOSS (Higher-Order Structural Similarity) is proposed to calculate higher-order graph structural similarity. Extensive experiments on two dangling benchmarks demonstrate that our SWGCL outperforms the current state-of-the-art methods with pure structural information in both traditional (relaxed) and dangling (consolidated) settings. [ABSTRACT FROM AUTHOR]

One-shot knowledge graph completion (KGC) aims to infer unseen facts when only one support entity pair is available for a particular relationship. Prior studies learn reference representations from one support pair for matching query pairs. This strategy can be challenging, particularly when dealing with multiple relationships between identical support pairs, resulting in indistinguishable reference representations. To this end, we propose a disentangled representation learning framework for one-shot KGC. Specifically, to learn sufficient representations, we construct an entity encoder with a fine-grained attention mechanism to explicitly model the input and output neighbors. We adopt an orthogonal regularizer to promote the independence of learned factors in entity representation, enabling the matching processor with max pooling to adaptively identify the semantic roles associated with a particular relation. Subsequently, the one-shot KGC is accomplished by seamlessly integrating the aforementioned modules in an end-to-end learning manner. Extensive experiments on real-world datasets demonstrate the outperformance of the proposed framework. [ABSTRACT FROM AUTHOR]

Geospatial knowledge graphs provide critical technology for integrating geographic information and semantic knowledge, which are very useful for geographic data analysis. As the scale of geospatial knowledge graphs continues to grow, the distributed management of geospatial knowledge graphs is becoming an inevitable requirement. Geospatial knowledge graph partitioning is the core technology for the distributed management of geospatial knowledge graphs. To support geographic data analysis, spatial relationships between entities should be considered in the application of geospatial knowledge graphs. However, existing knowledge graph partitioning methods overlook the spatial relationships between entities, resulting in the low efficiency of spatial queries. To address this issue, this study proposes a geospatial knowledge graph partitioning method based on dual clustering which performs two different clustering methods step by step. First, the density peak clustering method (DPC) is used to cluster geographic nodes. The nodes within each cluster are merged into a super-node. Then, we use an efficient graph clustering method (i.e., Leiden) to identify the community structure of the graph. Nodes belonging to the same community are further merged to reduce the size of the graph. Finally, partitioning operations are performed on the compressed graph based on the idea of the Linear-Weighted Deterministic Greedy Policy (LDG). We construct a geospatial knowledge graph based on YAGO3 to evaluate the performance of the proposed graph partitioning method. The experimental results show that the proposed method outperforms ten comparison methods in terms of graph partitioning quality and spatial query efficiency. [ABSTRACT FROM AUTHOR]

The objective of knowledge embedding (KE) is to represent entities and relations in a knowledge graph (KG) in a continuous low-dimensional vector space, thereby facilitating the integration of the KG into various downstream applications. Existing KE methods can be classified into two categories: closed-world knowledge embedding methods (CWKEs) and open-world knowledge embedding methods (OWKEs). CWKEs necessitate the re-training of the entire model upon the addition of new entities to the KG. In contrast, OWKEs offer greater practical application value as they do not require re-training due to their capacity to generate vector representations for new entities. The current major OWKEs employ the description text of new entities to generate vector representations for new entities. However, these methods are highly dependent on the richness of entity relationships and attribute information present in the text, and they are ineffective when there are only a few textual resources, such as entity names. In reality, most KGs have only a limited number of textual resources. For this reason, we propose the first open-world Knowledge Embedding method (LTKE) in a low textual resource environment. LTKE is designed to efficiently generate vector representations for new entities when KGs have only a few textual resources. By efficiently serializing the local structure of entities, LTKE can obtain information-rich text when an entity has only the entity name. Additionally, we develop a new scoring function to enhance model efficiency. Extensive experiments on real-world KG datasets (Freebase, WordNet) have demonstrated the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Additive Manufacturing (AM) technology offers remarkable flexibility in fabricating products with intricate geometries, presenting unprecedented advantages in material efficiency and speed. The process planning of AM plays a pivotal role in ensuring overall quality and time-efficiency of printed products. This drives engineers and researchers to explore various approaches to achieve optimal AM process solutions. However, numerous challenges persist, particularly in logical relationship reasoning and information representation for complex manufacturing tasks and design requirements. In this study, a novel AM process reasoning method based on relation mining is proposed, leveraging knowledge graph representation and graph neural networks (GNN). An AM knowledge graph is constructed comprising essential process information, followed by implementing RED-GNN to accomplish graph reasoning tasks for parameter recommendation. We then focus on the process planning scenario of lattice structures, a common geometry used for designing products with weight-relief requirements and high sensitivity to process parameters. A series of lattice structure parts are designed and tested using our proposed method, demonstrating strong performance and unveiling new potentials and opportunities in advancing knowledge-based engineering and intelligent manufacturing. [ABSTRACT FROM AUTHOR]

Knowledge Graph Embedding aims to encode both entities and relations into a continuous low-dimensional vector space, which is crucial for knowledge-driven application scenarios. As abstract entities in knowledge graphs, concepts inherently possess unique hierarchical structures and encompass rich semantic information. Although existing methods for jointly embedding concepts and instances achieve promising performance, they still face two issues: (1) They fail to explicitly reconstruct the hierarchical tree structure of concepts in the embedding space; (2) They ignore disjoint concept pairs and overlapping concept pairs derived from concepts. In this paper, we propose a novel concept representation approach, called Hyper Spherical Cone Concept Embedding (HCCE), to explicitly model the hierarchical tree structure of concepts in the embedding space. Specifically, HCCE represents each concept as a hyperspherical cone and each instance as a vector, maintaining the anisotropy of concept embeddings. We propose two variant methods to explore the impact of embedding concepts and instances in the same or different spaces. Moreover, we design score functions for disjoint concept pairs and overlapping concept pairs, using relative position relations to incorporate them seamlessly into our geometric models. Experimental results on three benchmark datasets show that HCCE outperforms most existing state-of-the-art methods on concept-related triples and achieves competitive results on instance-related triples. The visualization of embedding results intuitively shows the hierarchical tree structure of concepts in the embedding space. [ABSTRACT FROM AUTHOR]

The normal operation of automated equipment is essential for power grid regulation, making the accurate identification and diagnosis of defects in this equipment highly significant. Constructing a knowledge graph for automated equipment defects offers an effective solution to challenges such as delayed reporting, low efficiency, and data omissions in manually recorded defects. To address this, we developed a framework for constructing an automated equipment defect knowledge graph by designing appropriate patterns and data layers. For knowledge extraction, we introduced two models: RoBERTa-BiLSTM for named entity recognition (NER) and ALBERT-BiGRU for relation extraction (RE), both of which demonstrated improved performance in their respective tasks. Additionally, we applied the KBGAT model for knowledge graph completion. Finally, Neo4j was used for storing, visualizing, and analyzing the knowledge graph, highlighting its significance in the operation of power grids and the advancement of digital power systems. [ABSTRACT FROM AUTHOR]

The rapid advancement of artificial intelligence has significantly expanded the role of service robots in everyday life. This expansion necessitates the accurate recognition and prediction of human intentions to provide timely and appropriate services. However, existing methods often struggle to perform effectively in complex and unstructured environments. To address this challenge, we propose the Large language model and Knowledge graph based Intention Recognition Framework (LKIRF), which combines large language model (LLM) with knowledge graphs (KG) to enhance the intention recognition capabilities of service robots. Our approach constructs an offline KG from human motion and environmental data and builds an online reasoning graph through real-time interaction, utilizing LLM for interpretation. Experimental results indicate that compared to traditional methods, LKIRF not only improves prediction accuracy across various scenarios but also enhances the transparency and interpretability of the intention reasoning process. [ABSTRACT FROM AUTHOR]

Background: Migraine is a widespread, recurrent primary headache disorder primarily characterized by severe pulsatile headache, typically on one or both sides. It is often accompanied by nausea, vomiting, and hypersensitivity to sound and light. Despite the availability of multiple drugs for migraine management, the condition often becomes chronic due to untimely or irrational drug use, significantly distressing patients and increasing the burden on families and society. Over the past two decades, numerous clinical studies on migraine have been published. This study aimed to provide a comprehensive summary of the current status and trends of migraine clinical trials through bibliometric analysis. Methods: We used visual network tools such as CiteSpace and VOSviewer to perform a knowledge graph analysis of publications related to migraine clinical trials extracted from the WoSCC. Results: This study analyzed 1,129 articles published in 389 journals from 61 countries. The number of publications on migraine clinical trials has steadily increased from 2004 to 2023. The United States and Albert Einstein College of Medicine are the leading countries and institutions in this field, respectively. Richard B. Lipton is the most prolific author, making significant contributions to the research. The journal Headache has the highest number of publications and citations in this area. Keywords such as "efficacy," "RCT," "CGRP," "prophylaxis," "disability," "depression," "questionnaire," and "real-world effectiveness" received significant attention. Conclusion: This study identified reliable research hotspots and provided directions for clinicians. The treatment of migraine continues to be challenging. Future trends may include continued growth in migraine classification, risk factor analysis, and comorbidity studies. Research on CGRP and epigenetics will advance the progress of precision medicine in the migraine field. [ABSTRACT FROM AUTHOR]

Integrating Large Language Models (LLMs) with Knowledge Graphs (KGs) enhances the interpretability and performance of AI systems. This research comprehensively analyzes this integration, classifying approaches into three fundamental paradigms: KG-augmented LLMs, LLM-augmented KGs, and synergized frameworks. The evaluation examines each paradigm's methodology, strengths, drawbacks, and practical applications in real-life scenarios. The findings highlight the substantial impact of these integrations in fundamentally improving real-time data analysis, efficient decision-making, and promoting innovation across various domains. In this paper, we also describe essential evaluation metrics and benchmarks for assessing the performance of these integrations, addressing challenges like scalability and computational overhead, and providing potential solutions. This comprehensive analysis underscores the profound impact of these integrations on improving real-time data analysis, enhancing decision-making efficiency, and fostering innovation across various domains. [ABSTRACT FROM AUTHOR]

The advent of the Internet has led to the emergence of multi-source heterogeneous knowledge graphs, which have become a crucial means of storing and disseminating knowledge. Node importance estimation is a technique that is employed extensively in a number of fields, including recommender systems, intelligent search and resource allocation. This study introduces a Multi-perspective attention mechanism Fusion Algorithm for the mapping of multi-perspective features of knowledge graphs to Node Importance Estimation (MFA-NIE). First, structural embedding features, relational predicate features, and attribute features (both textual and quantitative) are established for the nodes. Subsequently, an enhanced attention mechanism is employed to extract, compress, and fuse these features. The fused hidden layer vector is employed in the design of a key-based attention mechanism, which enables the propagation of messages from neighboring nodes to the source node. This process results in the iterative updating of the source node's hidden features. Finally, TOPSIS centrality, based on the topology of the source node, is employed to dynamically adjust the mapping between the fused features and node importance. Experiments were conducted on real-world, large-scale, multi-source, heterogeneous knowledge graphs, comparing the MFA-NIE algorithm with traditional and advanced baseline algorithms, including PR, PPR, GENI, RGTN, CLINE, MCRL, and others. The results demonstrate that the MFA-NIE algorithm significantly improves effectiveness and accuracy, showcasing its superiority, versatility, and practical application value. [ABSTRACT FROM AUTHOR]