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In the current landscape, the Internet of Things (IoT) finds its utility across diverse sectors, including finance, healthcare, and beyond. However, security emerges as the principal obstacle impeding the advancement of IoT. Given the intricate nature of IoT cybersecurity, traditional security protocols fall short when addressing the unique challenges within the IoT domain. Security strategies anchored in the cybersecurity knowledge graph present a robust solution to safeguard IoT ecosystems. The foundation of these strategies lies in the intricate networks of the cybersecurity knowledge graph, with Named Entity Recognition (NER) serving as a crucial initial step in its implementation. Conventional cybersecurity entity recognition approaches IoT grapple with the complexity of cybersecurity entities, characterized by their sophisticated structures and vague meanings. Additionally, these traditional models are inadequate at discerning all the interrelations between cybersecurity entities, rendering their direct application in IoT security impractical. This paper introduces an innovative Cybersecurity Entity Recognition Model, referred to as CERM, designed to pinpoint cybersecurity entities within IoT. CERM employs a hierarchical attention mechanism that proficiently maps the interdependencies among cybersecurity entities. Leveraging these mapped dependencies, CERM precisely identifies IoT cybersecurity entities. Comparative evaluation experiments illustrate CERM’s superior performance over the existing entity recognition models, marking a significant advancement in the field of IoT security. [ABSTRACT FROM AUTHOR]

Natural-language spatial relations between geographic entities (geoentities) reflect diverse perceptions influenced by factors like location, culture, and linguistic conventions. These relations play a crucial role in supporting geospatial tasks, such as question answering and cognitive reasoning. While prior studies focused on a limited set of human-selected spatial terms and geometric attributes, they often overlooked essential semantic attributes. To overcome this limitation, we developed a Spatial Relation-based Knowledge Graph Embedding framework, SR-KGE, with new KG fusion functions to predict spatial relation terms among distinct geoentities. This method not only considers graph structures and the diversity of natural language expressions in the embedding and learning process, but also incorporates geoentity types as a constraint to capture spatial and semantic relations more accurately. Our experiments on two knowledge graph datasets, one small-scale and one large-scale, have both shown its superior performance in spatial relation inference compared to popular KGE models, including TransE, RotatE, and HAKE. We hope our research will advance the classic study of natural language described spatial relations in a more automated and intelligent way. [ABSTRACT FROM AUTHOR]

Purpose: Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services. Design/methodology/approach: The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management. Findings: The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization. Originality/value: The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects. [ABSTRACT FROM AUTHOR]

Open-domain question answering (ODQA) stands at the forefront of advancing natural language understanding and information retrieval. Traditional ODQA systems, which predominantly utilize a two-step process of information retrieval followed by reading module, face significant challenges in aligning retrieved passages with the contextual nuances of user queries. This paper introduces a novel methodology that leverages a semi-structured knowledge graph to enhance both the accuracy and relevance of answers in ODQA systems. Our model employs a threefold approach: firstly, it extracts and ranks evidence from a textual knowledge graph, a semi-structured knowledge graph where the nodes are real-world entities and the edges are sentences that two entities co-occur in, based on the contextual relationships relevant to the question. Secondly, it utilizes this ranked evidence to re-rank initially retrieved passages, ensuring that they align more closely with the query's context. Thirdly, it integrates this evidence into a generative reading component to construct precise and context-rich answers. We compare our model, termed contextual evidence-based question answering (CEQA), against traditional and state-of-the-art ODQA systems across several datasets, including TriviaQA, Natural Questions, and SQuAD Open. Our extensive experiments and ablation studies show that CEQA significantly outperforms existing methods by improving both the relevance of retrieved passages and the accuracy of the generated answers, thereby establishing a new benchmark in ODQA. [ABSTRACT FROM AUTHOR]

Network embeddings, which map nodes to low-dimensional vectors, facilitate link prediction, a pivotal aspect of complex network research. However, existing methods often overlook the complexities of hypergraphs and potent structures for modeling intricate relationships among multiple entities. This paper delves into link prediction within hypergraph motifs and network embedding (HMNE), crucial for diverse fields like knowledge graphs and bioinformatics. HMNE employs motifs to perform network embedding, representing nodes as hyper-nodes. HMNE utilizes the skip-gram model to get the embedding vectors by analyzing the sequence generated using a local random walk technique. Additionally, we consider hyper-motifs as super-nodes to highlight structural similarities between nodes. To further refine our methodology, we use the depth and breadth motif random walk strategy on the embedded network with hyper-nodes. This innovative approach enriches our understanding of network dynamics and enhances the predictive power of our model. We have thoroughly experimented the proposed method on several real-world datasets, and the results consistently demonstrate its usefulness. [ABSTRACT FROM AUTHOR]

Objective: An updated summary of the research profile of nutrition for the last 30 years for decompensated cirrhosis is lacking. This study aimed to explore the literature on nutrition for decompensated cirrhosis, draw a visual network map to investigate the research trends, and provide suggestions for future research. The Web of Science database retrieves the literature on nutrition for decompensated cirrhosis between 1994 and 2024. Methods: We used the cooperative, co-occurrence, and co-citation networks in the CiteSpace knowledge graph analysis tool to explore and visualize the relevant countries, institutions, authors, co-cited journals, keywords, and co-cited references. Results: We identified 741 articles on nutrition for decompensated cirrhosis. The number of publications and research interests has generally increased. The USA contributed the largest number of publications and had the highest centrality. The University of London ranked first in the number of articles issued, followed by the University of Alberta and Mayo Clinic. TANDON P, a "core strength" researcher, is a central hub in the collaborative network. Of the cited journals, HEPATOLOGY had the highest output (540, 15.3%). Conclusions: Over the past three decades, the focus of research on nutrition in decompensated cirrhosis has shifted from "hepatic encephalopathy, intestinal failure, metabolic syndrome, and alcoholic hepatitis" to "sarcopenia and nutritional assessment." In the future, nutritional interventions for sarcopenia should be based on a multimodal approach to address various causative factors. Its targeted treatment is an emerging area that warrants further in-depth research. [ABSTRACT FROM AUTHOR]

Industrial prosperity is the foundation of rural revitalization, and reasonable optimization can promote industrial prosperity. We analyzed WOS and CNKI academic data through knowledge mapping, drawed visualized knowledge mapping of domestic and international rural industrial structure, discussed the current hot topics of rural industrial structure, and focused on the development trend of domestic rural industrial structure and the development and evolution path of foreign rural industrial structure according to the status quo. It was found out that through the exploration of the three phases of start-up period-prosperous period-stable period, the development mode of rural industrial structure was undergoing a great change globally. In the future, China's rural industrial structure will be guided by leisure agriculture, beautiful countryside and structural adjustment. Suggestions were put forward, such as the need for localized debugging for theoretical research related to rural industrial structure. [ABSTRACT FROM AUTHOR]

Precision medicine is defined by the U.S. Food & Drug Administration as "an innovative approach to tailoring disease prevention and treatment that considers differences in people's genes, environments, and lifestyles". To succeed in providing personalized medicine to patients, it will be necessary to integrate medical, biological and molecular data in order to identify all complex disease subtypes and understand their pathobiological mechanism. Since biomedical knowledge graphs (BKGs) are limited to the integration of prior knowledge data and do not integrate real-world data (RWD) that would allow for the incorporation of patient level information, we propose a first step towards using RWD, BKGs and graph machine learning (ML) to enable a fully integrated precision medicine strategy. In this study, we established a link between RWD and a BKG. Our methodology introduced a novel patient representation using graph ML applied to the BKG. This approach facilitated the interpretation and extension of ML findings, particularly in disease subtype identification with molecular data contained in the BKG. We applied our innovative methodology to deepen our understanding of atopic dermatitis, a condition with a complex underlying pathophysiological mechanism. Through our analysis, we identified seven subgroups of patients each characterized by clinical and genomic characteristics. [ABSTRACT FROM AUTHOR]

Knowledge graph (KG) can effectively address the sparsity issue in recommendation systems. In recent years, graph neural networks (GNNs) have gained popularity in knowledge-aware recommendation (KGR) due to their powerful capability in modeling graph structures. However, existing GNN-based methods have the following limitations: (1) Primarily focusing on extracting collaborative signals between items from the user-item graph and neglecting the influence of preferences between different users. (2) The sparsity of user interaction data is a problem, and mining item knowledge associations on the KG with limited interaction data as a supervision signal will lead to limited performance improvement. In this paper, we propose an efficient cross-view contrastive learning method, KECL, to address the above challenges. Specifically, we first construct a user-user social graph based on the user-item graph to capture potential social connections among users. Then, we selectively utilize the entity information demanded by users and items to construct user-entity and item-entity graphs. Based on this, we design two contrastive loss tasks to perform contrastive learning on the above four graph views from both user and knowledge levels. This approach enables us to model the influence of preferences between users with social connections. It also allows us to efficiently mine item knowledge associations through a self-supervised paradigm, thereby learning high-quality node representations. Experimental results on three publicly available datasets demonstrate that our KECL outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objective: The existing research on the relationship between anxiety and non-suicidal self-injury (NSSI) is inconsistent, and there is no systematic review on this area. This study aims to explore the relationship between anxiety and NSSI, in order to provide evidence-based medicine evidence for the early identification of preventable occurrence factors of NSSI. Methods: The semantic query (i.e. SPARQL) method was used to retrieve the anxiety- related literature on the Knowledge graph of NSSI, which consist of the metadata and semantic annotation data of English literature related to non-suicidal self-injury in PubMed by June 2023. Two researchers strictly followed the inclusion and exclusion criteria for independent literature screening. After evaluating the quality of the included studies, the selected data was subjected to meta-analysis using RevMan5.3 software. Results: A total of 14 studies met the inclusion criteria of the meta-analysis, including 44064 subjects. The results showed that the proportion of anxiety in the NSSI group was significantly higher than that in the non-NSSI group, and the difference between the groups was statistically significant (OR=3.60, 95% Cl=2.08-6.22, p<0.01). Conclusion: There is a significant correlation between anxiety and NSSI, which is a possible risk factor for NSSI. However, due to limitations of the design type, quantity, and quality of the included study, further research is needed on the causal relationship between anxiety and NSSI. Furthermore, we show that using knowledge graphs is an effective approach to retrieve literature for meta-analysis. [ABSTRACT FROM AUTHOR]

Due to advances in big data technology, deep learning, and knowledge engineering, biological sequence visualization has been extensively explored. In the post-genome era, biological sequence visualization enables the visual representation of both structured and unstructured biological sequence data. However, a universal visualization method for all types of sequences has not been reported. Biological sequence data are rapidly expanding exponentially and the acquisition, extraction, fusion, and inference of knowledge from biological sequences are critical supporting technologies for visualization research. These areas are important and require in-depth exploration. This paper elaborates on a comprehensive overview of visualization methods for DNA sequences from four different perspectives—two-dimensional, three-dimensional, four-dimensional, and dynamic visualization approaches—and discusses the strengths and limitations of each method in detail. Furthermore, this paper proposes two potential future research directions for biological sequence visualization in response to the challenges of inefficient graphical feature extraction and knowledge association network generation in existing methods. The first direction is the construction of knowledge graphs for biological sequence big data, and the second direction is the cross-modal visualization of biological sequences using machine learning methods. This review is anticipated to provide valuable insights and contributions to computational biology, bioinformatics, genomic computing, genetic breeding, evolutionary analysis, and other related disciplines in the fields of biology, medicine, chemistry, statistics, and computing. It has an important reference value in biological sequence recommendation systems and knowledge question answering systems. [ABSTRACT FROM AUTHOR]

The fine-grained mining and construction of semantic associations within multimodal intangible cultural heritage (ICH) resources are crucial for deepening our understanding of their knowledge content and ensuring their systematic protection and transmission in the digital and intelligent era. This paper addresses the urgent need for the digital preservation and transmission of ICH resources. Following a review of current research on Qingyang sachets and ICH, the study introduces an ontology-based approach to constructing a semantic description model for the multimodal digital resources related to Qingyang sachets. By acquiring and processing multimodal resources concerning the craftsmanship and associated customs of Qingyang sachets, the study reorganizes the corresponding textual and visual knowledge. Utilizing knowledge graphs, the research explores multidimensional pathways for delivering knowledge services related to the multimodal digital resources of Qingyang sachets. Empirical research confirms the applicability and feasibility of the proposed semantic association scheme for multimodal ICH digital resources. The findings provide valuable insights for multidimensional organization and integration across scenarios, time periods, and resources within the ICH domain, offering a reference for digital solutions aimed at the systematic protection of ICH. [ABSTRACT FROM AUTHOR]

Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation. Fault diagnosis and identification of centrifugal compressors are crucial. To promptly monitor abnormal changes in compressor data and trace the causes leading to these data anomalies, this paper proposes a security monitoring and root cause tracing method for compressor data anomalies. Additionally, it presents an intelligent system design method for fault tracing in compressors and localization of faults from different sources. This method starts from petrochemical big data and consists of three parts: fault dynamic knowledge graph construction, instrument data sliding fault-tolerant filtering, and the fusion and reasoning of fault dynamic knowledge graph and instrument data variation monitoring. The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)—SPE (Square Prediction Error)—CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results. [ABSTRACT FROM AUTHOR]

Intelligent remanufacturing process planning is crucial for the efficient and high-quality remanufacturing of used parts with complex failure characteristics. However, due to the varied failure characteristics of used parts, the diversity of remanufacturing processes, and complex non-linear relationships among remanufacturing process elements, relying solely on mathematical programming or manual empirical is difficult to effectively model and optimise the remanufacturing process planning. To this end, a knowledge graph-based intelligent planning method for remanufacturing processes is proposed to enhance efficiency and quality by combining mathematical programming and knowledge reuse. Firstly, with failure characteristics as decision nodes, a full-element remanufacturing process ontology model is constructed, linking used parts, failure characteristics, and corresponding process plans. The BERT-BiLSTM-CRF model extracts remanufacturing process entities, and a remanufacturing process knowledge graph (RPKG) is constructed. Secondly, an intelligent decision-making model based on graph multi-node path retrieval is proposed. Aim to minimise carbon emissions, time, and cost, combining feature similarity calculations and nearest neighbour search (NNS) to efficiently retrieve the optimal process plan for each failure characteristic. Then, the optimal process plans are merged based on process constraints to create the complete plan. Finally, a concrete case is given to verify the effectiveness and advantages of this method. [ABSTRACT FROM AUTHOR]

Cyber Threat Intelligence (CTI) plays a crucial role in cybersecurity. However, traditional information extraction has low accuracy due to the specialization of CTI and the concealment of relations. To improve the performance of CTI relation extraction in the knowledge graph, we propose a relation extraction architecture called Adversarial Training for Cyber Threat Intelligence Relation Extraction (AT4CTIRE). Additionally, we developed a large-scale cybersecurity dataset for CTI analysis and evaluation called Cyber Threat Intelligence Analysis (CTIA). Inspired by Generative Adversarial Networks, we integrate contextual semantics to refine our study. Firstly, we use some wrong triples with incorrect relations to train the generator and produce high-quality generated triples as adversarial samples. Secondly, the discriminator used actual and generated samples as training data. Integrating the discriminator and the context-embedding module facilitates a deeper understanding of contextual CTI within threat triples. Finally, training a discriminator identified the relation between the threat entities. Experimentally, we set two CTI datasets and only one baseline that we could find to test the effect in the cybersecurity domain. We also took general knowledge graph completion tests. The results demonstrate that AT4CTIRE outperforms existing methods with improved extraction accuracy and a remarkable expedited training convergence rate. [ABSTRACT FROM AUTHOR]

In the realm of online learning, where resources are abundant, it is essential to customize recommendations and plans to meet individual learning needs. This involves not only identifying and addressing areas of weakness but also aligning the learning journey with each learner's cognitive preferences. However, existing methods for suggesting and structuring learning paths have notable limitations. To address these challenges, this paper introduces a knowledge graph-based personalized learning path planning method (KG-PLPPM). By leveraging a knowledge graph and refining cognitive diagnosis models, the proposed method tailors learning paths to individual needs. It evaluates knowledge concept similarity and learner mastery, and employs an algorithm for path planning. In the experiments, two metrics—the concept sequence degree and learning efficiency—are used to assess our work. Experimental results demonstrate that the method presented enhances the coherence and relevance of recommended learning paths, and achieves a higher concept sequence degree, indicating that knowledge concepts are arranged in a manner consistent with the learning sequence, which aligns more closely with learners' cognitive preferences. Moreover, across various learning progresses and path lengths, it effectively addresses weak knowledge areas, significantly enhancing learning efficiency. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) are widely used in recommendation systems to improve prediction performance, especially in scenarios with diverse behaviors and complex user interactions within supply chains. However, while existing models have achieved certain success in capturing the temporal and dynamic aspects of supply chain behaviors, challenges remain in effectively addressing the time-sensitive fluctuations of market demands and user preferences. Motivated by these challenges, we propose SC-TKGR, a supply chain recommendation framework based on temporal knowledge graphs. It employs enhanced time-sensitive graph embedding methods to model behavioral temporal characteristics, incorporates external factors to capture market dynamics, and utilizes contrastive learning to handle sparse information efficiently. Additionally, static feature knowledge graph embeddings are incorporated to complement temporal modeling by capturing complex retailer–product relationships. Experiments on real-world electrical equipment industry datasets demonstrate that SC-TKGR achieves superior performance in NDCG and Recall metrics, offering a robust approach for capturing trend-level demand shifts and market dynamics in supply chain recommendations, thereby aiding strategic planning at a monthly scale and operational adjustments. [ABSTRACT FROM AUTHOR]

Prefabricated construction (PC) plays a critical role in advancing the sustainable and high-quality transformation of the construction industry. Nevertheless, the fragmented and variable nature of technological innovations in PC complicates their acquisition, comprehension, and practical application, thereby hindering the process of innovation transformation. In response to these challenges, this study applies knowledge graph techniques to aggregate, correlate, and store knowledge pertaining to PC technological innovations. Specifically, using patent data from the past five years, and grounded in knowledge management and complex network theories, this study employs text mining, topic modeling, and association rule algorithms to perform clustering, evolutionary, and association analyses. The extracted entities and relationships obtained from the analyses are then stored in a Neo4j graph database for the construction and interactive visualization of a knowledge graph for PC technological innovation. According to the knowledge graph, a question-and-answer system framework is further proposed, providing practical application guidance. This research provides a comprehensive overview of the technological landscape, key nodes, and development trends in PC. It makes a meaningful contribution to knowledge management theory and complex network theory, advancing innovative applications in PC technology. [ABSTRACT FROM AUTHOR]

Intent detection and slot filling tasks share common semantic features and are interdependent. The abundance of professional terminology in specific domains, which poses difficulties for entity recognition, subsequently impacts the performance of intent detection. To address this issue, this paper proposes a co-interactive model based on a knowledge graph (CIMKG) for intent detection and slot filling. The CIMKG model comprises three key components: (1) a knowledge graph-based shared encoder module that injects domain-specific expertise to enhance its semantic representation and solve the problem of entity recognition difficulties caused by professional terminology and then encodes short utterances; (2) a co-interactive module that explicitly establishes the relationship between intent detection and slot filling to address the inter-dependency of these processes; (3) two decoders that decode the intent detection and slot filling. The proposed CIMKG model has been validated using question–answer corpora from both the medical and architectural safety fields. The experimental results demonstrate that the proposed CIMKG model outperforms benchmark models. [ABSTRACT FROM AUTHOR]

The Homo sapiens Chromosomal Location Ontology (HSCLO) is designed to facilitate the integration of human genomic features into biomedical knowledge graphs from releases GRCh37 and GRCh38 at multiple resolutions. HSCLO comprises two distinct versions, HSCLO37 and HSCLO38, each tailored to its respective human genome release. This ontology supports the efficient integration and analysis of human genomic data across scales ranging from entire chromosomes to individual base pairs, thereby enhancing data retrieval and interoperability within large-scale biomedical datasets. Unlike existing ontologies that primarily focus on genomic feature identification or annotation, HSCLO is specifically engineered to optimize the interoperability and scalability of genomic data within biomedical knowledge graphs. The utility and performance of HSCLO are demonstrated through a case study involving the integration of high-resolution chromatin interaction data, which reveals significant improvements in query efficiency and data linkage. HSCLO represents a valuable resource for advancing research in disease genetics, personalized medicine, and other domains that require complex genomic data integration. [ABSTRACT FROM AUTHOR]

Due to growing population and technological advances, global electricity consumption is increasing. Although CO2 emissions are projected to plateau or slightly decrease by 2025 due to the adoption of clean energy sources, they are still not decreasing enough to mitigate climate change. The residential sector makes up 25% of global electricity consumption and has potential to improve efficiency and reduce CO2 footprint without sacrificing comfort. However, a lack of uniform consumption data at the household level spanning multiple regions hinders large-scale studies and robust multi-region model development. This paper introduces a multi-region dataset compiled from publicly available sources and presented in a uniform format. This data enables machine learning tasks such as disaggregation, demand forecasting, appliance ON/OFF classification, etc. Furthermore, we develop an RDF knowledge graph that characterizes the electricity consumption of the households and contextualizes it with household-related properties enabling semantic queries and interoperability with other open knowledge bases like Wikidata and DBpedia. This structured data can be utilized to inform various stakeholders towards data-driven policy and business development. [ABSTRACT FROM AUTHOR]

Purpose: Effective knowledge management in large academic institutions is crucial for fostering innovation and improving educational practices. However, these institutions often face challenges, such as data fragmentation, siloed information systems and the complexity of integrating different data sources from various departments with complex hierarchical structures. To address these problems, the authors proposed a data fabric strategic framework that improves and enhances knowledge management by leveraging ontologies and knowledge graphs. This study aims to investigate the potential of knowledge graphs, ontological knowledge modelling and knowledge representation to improve knowledge management in large academic institutions. It also describes how technology can enhance knowledge accessibility and exchanges and improve decision-making processes based on insights from complex educational systems. Design/methodology/approach: This study uses coordination theory as a foundational framework to analyse intricate data systems in preparation for constructing, the Wizard of Oz method to facilitate the systematic organisation and management of information and the execution of an ontology-based data fabric framework and knowledge graphs. The authors propose a data fabric strategic framework aimed at improving knowledge management by leveraging ontologies and knowledge graphs. Findings: The final evaluation demonstrates that this approach effectively breaks down data silos, promotes research collaboration and improves decision-making processes in large academic settings, offering solution-oriented data fabric technologies applicable to universities and university federations globally. Practical implications: The proposed system provides a more efficient way of managing and connecting fragmented academic resources, improving accessibility for both learners and educators. By interconnecting and streaming knowledge management process, the system can reduce not only operational costs but also expenses on doing scientific research. Originality/value: Academic institutions prioritise time efficiency when acquiring vital data for improved scientific results. This emphasis extends beyond data governance to focus on how collective intelligence might improve organisational performance. The academic community has enhanced data utilisation through the implementation of data fabric technologies to improve data accessibility and data line tracking. [ABSTRACT FROM AUTHOR]

We assessed whether constructing a mathematical knowledge graph for a knowledge question-answering system or a course recommendation system, Named Entity Recognition (NER), is indispensable. The accuracy of its recognition directly affects the actual performance of these subsequent tasks. In order to improve the accuracy of mathematical knowledge entity recognition and provide effective support for subsequent functionalities, this paper adopts the latest pre-trained language model, LERT, combined with a Bidirectional Gated Recurrent Unit (BiGRU), Iterated Dilated Convolutional Neural Networks (IDCNNs), and Conditional Random Fields (CRFs), to construct the LERT-BiGRU-IDCNN-CRF model. First, LERT provides context-related word vectors, and then the BiGRU captures both long-distance and short-distance information, the IDCNN retrieves local information, and finally the CRF is decoded to output the corresponding labels. Experimental results show that the accuracy of this model when recognizing mathematical concepts and theorem entities is 97.22%, the recall score is 97.47%, and the F1 score is 97.34%. This model can accurately recognize the required entities, and, through comparison, this method outperforms the current state-of-the-art entity recognition models. [ABSTRACT FROM AUTHOR]

Event causality identification is an upstream operation for many tasks, including knowledge graphs and intelligent question-and-answer systems. The latest models introduce external knowledge and then use deep learning for causality prediction. However, event causality recognition still faces problems such as data imbalance and insufficient event content richness. Additionally, previous frameworks have utilized a single model, but these frequently produce unsatisfactory outcomes such as lower precision rates and lower recall rates. We propose the concept of ensemble learning, which combines multiple models to achieve frameworks that perform as well as or better than the latest models. This framework combines the advantages of Mamba, a temporal convolutional network, and graph computation to identify event causality more effectively and accurately. After comparing our framework to standard datasets, our F1-scores (measures of model accuracy) are essentially the same as those of the state-of-the-art (SOTA) methods on one dataset. [ABSTRACT FROM AUTHOR]

Hyper-relational knowledge graphs can enhance the intelligence, efficiency, and reliability of industrial production by enabling equipment collaboration and optimizing supply chains. However, the construction of knowledge graphs in industrial fields faces significant challenges due to the complexity of hyper-relational data, the sparsity of industrial datasets, and limitations in existing link prediction methods, which struggle to capture the nuanced relationships and qualifiers often present in industrial scenarios. This paper proposes the HyLinker model, designed to improve the representation of entities and relations through modular components, including an entity neighbor aggregator, a relation qualifier aggregator, MoE-LSTM (Mixture of Experts Long Short-Term Memory), and a convolutional bidirectional interaction module. Experimental results demonstrate that the proposed method performs well on both public datasets and a self-constructed hoisting machine dataset. In the Mine Hoist Super-Relationship Dataset (MHSD-100), HyLinker outperforms the latest models, with improvements of 0.142 in MRR (Mean Reciprocal Rank) and 0.156 in Hit@1 (Hit Rate at Rank 1), effectively addressing the knowledge graph completion problem for hoisting machines and providing more accurate information for equipment maintenance and fault prediction. These results demonstrate the potential of HyLinker in overcoming current challenges and advancing the application of hyper-relational knowledge graphs in industrial contexts. [ABSTRACT FROM AUTHOR]

Knowledge graphs organize and represent entity relationships through graph structures, providing a foundation for machine understanding and reasoning, but their reasoning capabilities are limited by coverage and manual rules. Large language models demonstrate strong semantic understanding and generation abilities but lack effective utilization of symbolic knowledge and interpretability. To combine the strengths of both technologies, academic and industrial communities have devoted significant effort in recent years to exploring the integration of knowledge graphs and large language models, aiming to build more powerful and interpretable AI systems. Firstly, this paper reviews the current state of research on the fusion of knowledge graphs and large language models, with a focus on the key achievements in enhancing fact representation and logical reasoning. These achievements include pre-trained language models based on knowledge graphs, knowledge graph representation learning based on large language models, and reasoning methods that leverage the fusion of the two approaches. Furthermore, the paper outlines the mainstream technical approaches and application scenarios of graph-model integration in the industry. Finally, future development directions of graph-model intgeration are discussed, and it is posited that the integration of these two technologies represents a crucial trend in the advancement of artificial intelligence. [ABSTRACT FROM AUTHOR]

As the forms of cyber threats become increasingly severe, cybersecurity knowledge graphs (KGs) have become essential tools for understanding and mitigating these threats. However, the quality of the KG is critical to its effectiveness in cybersecurity applications. In this paper, we propose a spurious-negative sample augmentation-based quality evaluation method for cybersecurity KGs (SNAQE) that includes two key modules: the multi-scale spurious-negative triple detection module and the adaptive mixup based on the attention mechanism module. The multi-scale spurious-negative triple detection module classifies the sampled negative triples into spurious-negative and true-negative triples. Subsequently, the attention mechanism-based adaptive mixup module selects appropriate mixup targets for each spurious-negative triple, constructing partially correct triples and achieving more precise sample generation in the entity embedding space to assist in training the KG quality evaluation models. Through extensive experimental validation, the SNAQE model not only performs excellently in general-domain KG quality evaluation but also achieves outstanding outcomes in the cybersecurity KGs, significantly enhancing the accuracy and F1 score of the model, with the best F1 score of 0.969 achieved on the FB15K dataset. [ABSTRACT FROM AUTHOR]