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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]

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]

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]

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]

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]

Legal knowledge involves multidimensional heterogeneous knowledge such as legal provisions, judicial interpretations, judicial cases, and defenses, which requires extremely high relevance and accuracy of knowledge. Meanwhile, the construction of a legal knowledge reasoning system also faces challenges in obtaining, processing, and sharing multisource heterogeneous knowledge. The knowledge graph technology, which is a knowledge organization form with triples as the basic unit, is able to efficiently transform multisource heterogeneous information into a knowledge representation form close to human cognition. Taking the automated construction of the Chinese legal knowledge graph (CLKG) as a case scenario, this paper presents a joint knowledge enhancement model (JKEM), where prior knowledge is embedded into a large language model (LLM), and the LLM is fine-tuned through the prefix of the prior knowledge data. Under the condition of freezing most parameters of the LLM, this fine-tuning scheme adds continuous deep prompts as prefix tokens to the input sequences of different layers, which can significantly improve the accuracy of knowledge extraction. The results show that the knowledge extraction accuracy of the JKEM in this paper reaches 90.92 % . Based on the superior performance of this model, the CLKG is further constructed, which contains 3480 knowledge triples composed of 9 entities and 2 relationships, providing strong support for an in-depth understanding of the complex relationships in the legal field. [ABSTRACT FROM AUTHOR]

In this age of big data and natural language processing, to what extent can we leverage new technologies and new tools to make progress in organizing disparate biomedical data sources? Imagine a system in which one could bring together sequencing data with phenotypes, gene expression data, and clinical information all under the same conceptual heading where applicable. Bio-ontologies seek to carry this out by organizing the relations between concepts and attaching the data to their corresponding concept. However, to accomplish this, we need considerable time and human input. Instead of resorting to human input alone, we describe a novel approach to obtaining the foundation for bio-ontologies: obtaining propositions (links between concepts) from biomedical text so as to fill the ontology. The heart of our approach is applying logic rules from Aristotelian logic and natural logic to biomedical information to derive propositions so that we can have material to organize knowledge bases (ontologies) for biomedical research. We demonstrate this approach by constructing a proof-of-principle bio-ontology for COVID-19 and related diseases. [ABSTRACT FROM AUTHOR]

A novel framework called TravelRAG is introduced in this paper, which is built upon a large language model (LLM) and integrates Retrieval-Augmented Generation (RAG) with knowledge graphs to create a retrieval system framework designed for the tourism domain. This framework seeks to address the challenges LLMs face in providing precise and contextually appropriate responses to domain-specific queries in the tourism field. TravelRAG extracts information related to tourist attractions from User-Generated Content (UGC) on social media platforms and organizes it into a multi-layer knowledge graph. The travel knowledge graph serves as the core retrieval source for the LLM, enhancing the accuracy of information retrieval and significantly reducing the generation of erroneous or fabricated responses, often termed as "hallucinations". As a result, the accuracy of the LLM's output is enhanced. Comparative analyses with traditional RAG pipelines indicate that TravelRAG significantly boosts both the retrieval efficiency and accuracy, while also greatly reducing the computational cost of model fine-tuning. The experimental results show that TravelRAG not only outperforms traditional methods in terms of retrieval accuracy but also better meets user needs for content generation. [ABSTRACT FROM AUTHOR]

The efficient analysis and interpretation of biological sequence data remain major challenges in bioinformatics. Graphical representation, as an emerging and effective visualization technique, offers a more intuitive method for analyzing DNA sequences. However, many visualization approaches are dispersed across research databases, requiring urgent organization, integration, and analysis. Additionally, no single visualization method excels in all aspects. To advance these methods, knowledge graphs and advanced machine learning techniques have become key areas of exploration. This paper reviews the current 2D and 3D DNA sequence visualization methods and proposes a new research direction focused on constructing knowledge graphs for biological sequence visualization, explaining the relevant theories, techniques, and models involved. Additionally, we summarize machine learning techniques applicable to sequence visualization, such as graph embedding methods and the use of convolutional neural networks (CNNs) for processing graphical representations. These machine learning techniques and knowledge graphs aim to provide valuable insights into computational biology, bioinformatics, genomic computing, and evolutionary analysis. The study serves as an important reference for improving intelligent search systems, enriching knowledge bases, and enhancing query systems related to biological sequence visualization, offering a comprehensive framework for future research. [ABSTRACT FROM AUTHOR]

The rapid growth of biomedical literature makes it challenging for researchers to stay current. Integrating knowledge from various sources is crucial for studying complex biological systems. Traditional text-mining methods often have limited accuracy because they don't capture semantic and contextual nuances. Deep-learning models can be computationally expensive and typically have low interpretability, though efforts in explainable AI aim to mitigate this. Furthermore, transformer-based models have a tendency to produce false or made-up information—a problem known as hallucination—which is especially prevalent in large language models (LLMs). This study proposes a hybrid approach combining text-mining techniques with graph neural networks (GNNs) and fine-tuned large language models (LLMs) to extend biomedical knowledge graphs and interpret predicted edges based on published literature. An LLM is used to validate predictions and provide explanations. Evaluated on a corpus of experimentally confirmed protein interactions, the approach achieved a Matthews correlation coefficient (MCC) of 0.772. Applied to insomnia, the approach identified 25 interactions between 32 human proteins absent in known knowledge bases, including regulatory interactions between MAOA and 5-HT2C, binding between ADAM22 and 14-3-3 proteins, which is implicated in neurological diseases, and a circadian regulatory loop involving RORB and NR1D1. The hybrid GNN-LLM method analyzes biomedical literature efficiency to uncover potential molecular interactions for complex disorders. It can accelerate therapeutic target discovery by focusing expert verification on the most relevant automatically extracted information. [ABSTRACT FROM AUTHOR]

Over the years, the field of landslide disaster research has amassed a wealth of data and specialized knowledge. However, these resources originate from a wide array of sources and often feature complex data structures, highlighting a persistent lack of methods to integrate multi-source, heterogeneous data. Traditional landslide monitoring methods typically focus on singular monitoring targets and data sources, which limits a comprehensive understanding of the complex processes involved in landslides. This paper introduces a landslide monitoring model based on a knowledge graph. This model employs P-Tuning to fine-tune ChatGLM2 for the extraction of triples. Differential InSAR (D-InSAR) is utilized to extract ground deformation data, which is then integrated with the knowledge graph for landslide monitoring and analysis. This study focuses on the co-seismic landslide in Jishishan, Gansu, China. By analyzing the landslide knowledge graph and the spatiotemporal deformation map, the results are as follows: (1) For this event, 106 entities and attributes were constructed, along with two recommended calculation routes. (2) The deformation at the earthquake's central region reached up to 8.784 cm, with a slightly smaller deformation zone to the northwest peaking at 9.662 cm. Significant unilateral subsidence was observed in the mountain range to the southwest. (3) The area affected by the co-seismic landslide primarily includes farmland and villages, covering an area of 0.3408 square kilometers. (4) Analysis based on the knowledge graph indicates that this landslide was primarily caused by the rapid liquefaction of water-saturated soil layers due to the earthquake, resulting in instability. This study contributes to the analysis of post-disaster losses, attribution, and impacts. [ABSTRACT FROM AUTHOR]

A growing body of biomedical literature suggests a bidirectional regulatory relationship between cardiac calcium (Ca2+)-regulating proteins and reactive oxygen species (ROS) that is integral to the pathogenesis of various cardiac disorders via oxidative stress (OS) signaling. To address the challenge of finding hidden connections within the growing volume of biomedical research, we developed a data science pipeline for efficient data extraction, transformation, and loading. Employing the CaseOLAP (Context-Aware Semantic Analytic Processing) algorithm, our pipeline quantifies interactions between 128 human cardiomyocyte Ca2+-regulating proteins and eight cardiovascular disease (CVD) categories. Our machine-learning analysis of CaseOLAP scores reveals that the molecular interfaces of Ca2+-regulating proteins uniquely associate with cardiac arrhythmias and diseases of the cardiac conduction system, distinguishing them from other CVDs. Additionally, a knowledge graph analysis identified 59 of the 128 Ca2+-regulating proteins as involved in OS-related cardiac diseases, with cardiomyopathy emerging as the predominant category. By leveraging a link prediction algorithm, our research illuminates the interactions between Ca2+-regulating proteins, OS, and CVDs. The insights gained from our study provide a deeper understanding of the molecular interplay between cardiac ROS and Ca2+-regulating proteins in the context of CVDs. Such an understanding is essential for the innovation and development of targeted therapeutic strategies. [ABSTRACT FROM AUTHOR]

Due to its inherent complexities in the process and the dynamic interactions with external environmental factors, the construction industry is widely considered one of the most hazardous industries worldwide. With advancements in artificial intelligence (AI), construction safety management practices have increasingly used knowledge-driven approaches. Such incorporation of knowledge-based methods has led to significant improvements in various elements of construction safety management systems, including hazard identification and risk assessment, selection of risk mitigation strategies, analysis of accident information, sharing of health and safety knowledge, access to regulations, and identification of applicable safety requirements. Against this background, this paper presents a systematic literature review to provide an overview of the current state of the art in the use of knowledge-driven approaches in construction safety management. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedure, this study reviews how the knowledge-driven approach is utilized in the construction safety management field to automate different activities that come under it. Journal papers published from 2000 were considered for this review, and the analysis focused on the contributions of research, the evolution of knowledge-driven approaches, sources of incorporated knowledge, methods of system development, yearly publications, and publication by journals. The results provide a comparison of related studies over two decades and offer insights into trends and gaps in this research field. Notably, the trend analysis shows a dramatic increase in the number, as well as the depth, of research efforts utilizing AI techniques for analyzing unstructured data, such as construction images and texts from construction documents, and drawing data-based decisions for accident prevention. [ABSTRACT FROM AUTHOR]

As a significant percentage of disasters and fatal accidents still occur in the construction sector, it is legally obligatory to conduct workplace risk assessments to avoid accidents and enhance safety. Identifying harmful and hazardous elements is crucial to discern the distinctive characteristics of potential accidents. However, conventional risk-assessment approaches, which rely on the skills and experience of safety managers, may overlook important factors, leading to inconsistencies in the procedures employed across different sites. Such unstructured safety knowledge reduces accessibility and utility, increases reliance on individual skills, and renders information management inefficient. Recently, the focus has shifted from efficient data storage to obtaining valuable knowledge tailored to specific use-cases. Knowledge-graph-based systems integrate and manage the relationships between knowledge entities, thereby enhancing the development of knowledge bases. Research on automatically extracting and managing predefined knowledge from various forms of data through natural language processing (NLP) is ongoing. This study proposes a novel method that uses NLP and graph models to automatically extract predefined knowledge from unstructured construction data and build an entity-relationship-based risk-assessment knowledge base. We developed an entity-name recognition and keyword-extraction engine that defines the core knowledge related to construction safety and risk assessments. This engine can automatically extract predefined knowledge from unstructured data by learning from NLP data. The extracted risk-assessment knowledsge was used to create a knowledge base, and its efficiency and effectiveness were validated through comparisons with existing methods. The results of this study are significant because they lay the foundation for an automatic knowledge-management system for construction safety and risk assessment, offering both practical and academic contributions to the field of construction safety. [ABSTRACT FROM AUTHOR]

As consumer electronics evolve towards greater intelligence, their automation and complexity also increase, making it difficult for users to diagnose faults when they occur. To address the problem where users, relying solely on their own knowledge, struggle to diagnose faults in consumer electronics promptly and accurately, we propose a multimodal knowledge graph-based text generation method. Our method begins by using deep learning models like the Residual Network (ResNet) and Bidirectional Encoder Representations from Transformers (BERT) to extract features from user-provided fault information, which can include images, text, audio, and even olfactory data. These multimodal features are then combined to form a comprehensive representation. The fused features are fed into a graph convolutional network (GCN) for fault inference, identifying potential fault nodes in the electronics. These fault nodes are subsequently fed into a pre-constructed knowledge graph to determine the final diagnosis. Finally, this information is processed through the Bias-term Fine-tuning (BitFit) enhanced Chinese Pre-trained Transformer (CPT) model, which generates the final fault diagnosis text for the user. The experimental results show that our proposed method achieves a 4.4% improvement over baseline methods, reaching a fault diagnosis accuracy of 98.4%. Our approach effectively leverages multimodal fault information, addressing the challenges users face in diagnosing faults through the integration of graph convolutional network and knowledge graph technologies. [ABSTRACT FROM AUTHOR]

Research on named entity recognition (NER) and command-line generation for network security evaluation tools is relatively scarce, and no mature models for recognition or generation have been developed thus far. Therefore, in this study, the aim is to build a specialized corpus for network security evaluation tools by combining knowledge graphs and information entropy for automatic entity annotation. Additionally, a novel NER approach based on the KG-BERT-BiLSTM-CRF model is proposed. Compared to the traditional BERT-BiLSTM model, the KG-BERT-BiLSTM-CRF model demonstrates superior performance when applied to the specialized corpus of network security evaluation tools. The graph attention network (GAT) component effectively extracts relevant sequential content from datasets in the network security evaluation domain. The fusion layer then concatenates the feature sequences from the GAT and BiLSTM layers, enhancing the training process. Upon successful NER execution, in this study, the identified entities are mapped to pre-established command-line data for network security evaluation tools, achieving automatic conversion from textual content to evaluation commands. This process not only improves the efficiency and accuracy of command generation but also provides practical value for the development and optimization of network security evaluation tools. This approach enables the more precise automatic generation of evaluation commands tailored to specific security threats, thereby enhancing the timeliness and effectiveness of cybersecurity defenses. [ABSTRACT FROM AUTHOR]

The AI-driven applications and large data generated by IoT devices connected to large-scale utility infrastructures pose significant operational challenges, including increased latency, communication overhead, and computational imbalances. Addressing these is essential to shift the workloads from the cloud to the edge and across the entire computing continuum. However, to achieve this, significant challenges must still be addressed, particularly in decision making to manage the trade-offs associated with workload offloading. In this paper, we propose a task-offloading solution using Reinforcement Learning (RL) to dynamically balance workloads and reduce overloads. We have chosen the Deep Q-Learning algorithm and adapted it to our workload offloading problem. The reward system considers the node's computational state and type to increase the utilization of the computational resources while minimizing latency and bandwidth utilization. A knowledge graph model of the computing continuum infrastructure is used to address environment modeling challenges and facilitate RL. The learning agent's performance was evaluated using different hyperparameter configurations and varying episode lengths or knowledge graph model sizes. Results show that for a better learning experience, a low, steady learning rate and a large buffer size are important. Additionally, it offers strong convergence features, with relevant workload tasks and node pairs identified after each learning episode. It also demonstrates good scalability, as the number of offloading pairs and actions increases with the size of the knowledge graph and the episode count. [ABSTRACT FROM AUTHOR]

Aiming to solve the problems of high cost and large error, a prediction model of oil and gas pipeline corrosion rate was established by using a knowledge graph and neural network. The knowledge map of oil and gas pipeline corrosion was constructed by the seven-step method and the fusion of Bidirectional Encoder Representations from Transformers, Bidirectional Long Short-Term Memory, and Conditional Random Fields models (BERT-Bi LSTM-CRF) combined extraction, and the triplet information was stored by the Neo4j graph database. With eight factors, such as carbon dioxide content, as input signals, three kinds of neural networks were used to screen the basic prediction model, and the genetic algorithm (GA) and particle swarm optimization algorithm (PSO) were used to optimize and compare. The results show that the extraction accuracy of BERT-Bi LSTM-CRF is 94.46%, the recall rate is 90.39%, and the F1 value is 92.38%. The optimized PSO-BP model has the best effect, and the coefficient of determination in the training stage and the test stage is as high as 0.9954 and 0.9935, and the total mean square error of the sample is reduced by 92.8% compared with that before optimization. The corrosion rate model proposed in this paper ensures the safe operation of oil and gas pipelines and offers a theoretical foundation for corrosion prediction. [ABSTRACT FROM AUTHOR]

In the new era of the vigorous development of digitalization and intelligence, digital technology has widely penetrated various fields. International authoritative standardization bodies, such as the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), proposed a timely new standard concept called Standards Machine Applicable, Readable, and Transferable (SMART) in order to meet this development trend. Its core feature is that the standard can be machine-readable, usable, understandable, and resolvable without human labor so as to achieve the goals of standard formulation, promotion, publicity, and implementation more effectively. Simultaneously, China's standardization industry is responding to the strategic deployment of "new quality productivity" by actively promoting the digital development of standards and establishing standard information databases, standard formulation management systems, etc., which provide data support and a platform basis for applying new technologies. Advanced technologies such as big data, artificial intelligence, blockchain, and knowledge graphs can be combined with standardization to improve the efficiency of standard development, application accuracy, and implementation effects. To align with these trends, this study focuses on analyzing the responses of national and international standards in the field of urban sustainable development to the United Nations Sustainable Development Goals (UN-SDGs). This study proposes an innovative approach involving the application of knowledge graph technology to the standardization of urban sustainable development and establishing a response correlation between the indicator library for cities' sustainable development (ILCSD) and SDGs. It also provides additional functions, such as the intelligent extraction of cities' sustainable characteristic evaluation indicators and aided decision analysis, which greatly enhance the practicability and efficiency of the ILCSD as a technical tool. Based on knowledge graphs, this study analyzes the different responses of important standards in the field of urban sustainable development to the 17 SDGs, accurately identifies weak trends and gaps in standards, and provides a basis for improving the standardization system of urban sustainable development. Simultaneously, by comparing national and international standards and technologies, this study promotes the mutual recognition of standards, which can help China's urban sustainable development work align with international standards. In addition, the process of establishing and maintaining knowledge graphs facilitates the continuous adoption of new standards through which the indicator library is automatically updated. Finally, in this study, we propose several inspirations for the standardization of urban sustainable development in China, such as an optimization standard system of benchmarking SDGs and a localization application of the original SDG indicators. [ABSTRACT FROM AUTHOR]

Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper. [ABSTRACT FROM AUTHOR]

Knowledge graph embedding represents entities and relations as low-dimensional continuous vectors. Recently, researchers have attempted to leverage the potential semantic connections between entities with hierarchical relationships in the knowledge graph. However, existing knowledge embedding methods that model entity hierarchical structures face the following challenges: (1) Setting the same embedding dimension for entities at different hierarchical levels can lead to overfitting issues and the waste of storage and computational resources; (2) Manually searching the embedding dimension in discrete space makes it easy to miss the optimal parameters and increases training costs. Therefore, we propose a knowledge embedding method, HEAES, that automatically learns the embedding dimensions for entities based on their hierarchical structure. Specifically, we first propose a new modeling method to capture the hierarchical relationships of entities, and we then train the model on a triple classification task. Then, we adaptively learn the pruning threshold to trim the embedding vectors, automatically learning different embedding dimensions for entities at different hierarchical levels. Experiments on the YAGO26K and DB111K datasets verify that introducing entity hierarchical information helps boost the model performance. [ABSTRACT FROM AUTHOR]

The loss of power grid efficiency is affected by a number of factors, including the physical characteristics of the equipment used, the operational characteristics of the power grid itself, the characteristics of the electricity being used, and the natural conditions present. The application of a simple line loss rate value is inadequate for the objective evaluation of the implementation of line loss management in different provinces. Accordingly, this paper puts forth an innovative comprehensive evaluation method for municipal power grid line loss management, namely the construction of a comprehensive management evaluation knowledge graph for line loss. This method integrates the dimensionality reduction capability of nuclear principal component analysis with the combined decision support capability of DEMATEL-Entropy Weight-TOPSIS, thereby achieving a comprehensive evaluation of power grid line loss in the whole process and across multiple dimensions. The empirical analysis of cross-sectional data based on line loss management in Guangxi from 2014 to 2016 demonstrates that the line loss evaluation index system proposed in this paper effectively reduces the redundancy of line loss data. To illustrate, the municipal power grid in Guangxi (GL city) has demonstrated a gradual enhancement in the competitiveness of its management and planning loss reduction strategies. However, there has been a notable decline in the competitiveness of its operational and technical loss reduction approaches, with a reduction of approximately 23.7%. Therefore, the overall development of the city's power grid line loss level can be considered stable. This method allows for an objective and reasonable reflection of the actual situation differences in line loss management work, thus providing a basis for future line loss management evaluation. [ABSTRACT FROM AUTHOR]

Power safety named entity recognition (NER) is essential for determining the cause of faults, assessing potential risks, and planning maintenance schedules, contributing to the comprehension and analysis of power safety documentation content and structure. Such analysis is crucial for the development of a knowledge graph within the power safety domain and the augmentation of the associated dataset. This paper introduces a power safety NER model using efficiently learning an encoder that classifies token replacements accurately (ELECTRA) model. This model employs root mean square layer normalization (RMSNorm) and the switched gated linear unit (SwiGLU) activation function, which substitutes the conventional layer normalization (LayerNorm) and the Gaussian error linear units (GeLU). This model also integrates bidirectional long short-term memory (BiLSTM) with conditional random fields (CRF) to bolster performance in NER tasks. Experimental results show that the improved ELECTRA model achieved an F1 value of 93% on the constructed power safety NER dataset. It outperforms the BERT-BiLSTM-CRF model, achieving a 3.3% performance improvement. [ABSTRACT FROM AUTHOR]

Knowledge-aware recommendation systems have shown superior performance by connecting user item interaction graph (UIG) with knowledge graph (KG) and enriching semantic connections collected by the corresponding networks. Among the existing methods, self-supervised learning has attracted the most attention for its significant effects in extracting node self-discrimination auxiliary supervision, which can largely improve the recommending rationality. However, existing methods usually employ a single (either node or edge) perspective for representation learning, over-emphasizing the pair-wise topology structure in the graph, thus overlooking the important semantic information among neighborhood-wise connection, limiting the recommendation performance. To solve the problem, we propose Hierarchical self-supervised learning for Knowledge-aware Recommendation (HKRec). The hierarchical property of the method is shown in two perspectives. First, to better reveal the knowledge graph semantic relations, we design a Triple-Graph Masked Autoencoder (T-GMAE) to force the network to estimate the masked node features, node connections, and node degrees. Second, to better align the user-item recommendation knowledge with the common knowledge, we conduct contrastive learning in a hybrid way, i.e., both neighborhood-level and edge-level dropout are adopted in a parallel way to allow more comprehensive information distillation. We conduct an in-depth experimental evaluation on three real-world datasets, comparing our proposed HKRec with state-of-the-art baseline models to demonstrate its effectiveness and superiority. Respectively, Recall@20 and NDCG@20 improved by 2.2% to 24.95% and 3.38% to 22.32% in the Last-FM dataset, by 7.0% to 23.82% and 5.7% to 39.66% in the MIND dataset, and by 1.76% to 34.73% and 1.62% to 35.13% in the Alibaba-iFashion dataset. [ABSTRACT FROM AUTHOR]

Link prediction is crucial in forecasting potential distribution channels within the dynamic and heterogeneous Xinjiang jujube sales market. This study utilizes knowledge graphs to represent entities and constructs a complex network model for market analysis. Graph neural networks (GNNs) have shown excellent performance in handling graph-structured data, but they do not necessarily significantly outperform in link prediction tasks due to an overreliance on node features and a neglect of structural information. Additionally, the Xinjiang jujube dataset exhibits unique complexity, including multiple types, attributes, and relationships, distinguishing it from typical GNN datasets such as DBLP and protein-protein interaction datasets. To address these challenges, we introduce the Heterogeneous Multi-Head Attention Graph Neural Network model (HMAGNN). Our methodology involves mapping isomeric nodes to common feature space and labeling nodes using an enhanced Weisfeiler–Lehman (WL) algorithm. We then leverage HMAGNN to learn both structural and attribute features individually. Throughout our experimentation, we identify the critical influence of local subgraph structure and size on link prediction outcomes. In response, we introduce virtual nodes during the subgraph extraction process and conduct validation experiments to underscore the significance of these factors. Compared to alternative models, HMAGNN excels in capturing structural features through our labeling approach and dynamically adapts to identify the most pertinent link information using a multi-head attention mechanism. Extensive experiments on benchmark datasets consistently demonstrate that HMAGNN outperforms existing models, establishing it as a state-of-the-art solution for link prediction in the context of jujube sales market analysis. [ABSTRACT FROM AUTHOR]

Adversarial attacks on Graph Neural Networks (GNNs) have emerged as a significant threat to the security of graph learning. Compared with Graph Modification Attacks (GMAs), Graph Injection Attacks (GIAs) are considered more realistic attacks, in which attackers perturb GNN models by injecting a small number of fake nodes. However, most existing black-box GIA methods either require comprehensive knowledge of the dataset and the ground-truth labels or a large number of queries to execute the attack, which is often unfeasible in many scenarios. In this paper, we propose an unsupervised method for leveraging the rich knowledge contained in the graph data themselves to enhance the success rate of graph injection attacks on the initial query. Specifically, we introduce GraphContrastive Learning-based Graph Injection Attack (GCIA), which consists of a node encoder, a reward predictor, and a fake node generator. The Graph Contrastive Learning (GCL)-based node encoder transforms nodes for low-dimensional continuous embedding, the reward predictor acts as a simplified surrogate for the target model, and the fake node generator produces fake nodes and edges based on several carefully designed loss functions, utilizing the node encoder and reward predictor. Extensive results demonstrate that the proposed GCIA method achieves a first query success rate of 91.2% on the Reddit dataset and improves the success rate to over 99.7% after 10 queries. [ABSTRACT FROM AUTHOR]

Urban waterlogging is one of the major "diseases" faced by cities, posing a great challenge to the healthy and sustainable development of cities. The traditional geographic knowledge graph struggles to capture dynamic changes in urban waterlogging over time. Therefore, the objective of this study is to analyze the time, events, properties, geographic objects, and activities associated with urban waterlogging emergency responses from the geographic spatial and temporal processes perspective and to construct an urban waterlogging emergency knowledge graph by combining top-down and bottom-up approaches. We propose a conceptual model of urban waterlogging emergency response ontology based on spatiotemporal processes by analyzing the basic laws and influencing factors of urban waterlogging occurrence and development. Secondly, we describe the construction process of the urban waterlogging emergency response knowledge graph from knowledge extraction, knowledge fusion, and knowledge storage. Finally, the knowledge graph was visualized using 159 urban waterlogging events in China from 2020–2022, with a quality assessment indicating 81% correctness, 65.5% completeness, and 95% data conciseness. The results show that this method can effectively express the spatiotemporal process of an urban waterlogging emergency response and can provide a reference for the spatiotemporal modeling of the knowledge graph. [ABSTRACT FROM AUTHOR]

The objective of this research was to design a method to assign topics to claims debunked by fact-checking agencies. During the fact-checking process, access to more structured knowledge is necessary; therefore, we aim to describe topics with semantic vocabulary. Classification of topics should go beyond simple connotations like instance-class and rather reflect broader phenomena that are recognized by fact checkers. The assignment of semantic entities is also crucial for the automatic verification of facts using the underlying knowledge graphs. Our method is based on sentence embeddings, various clustering methods (HDBSCAN, UMAP, K-means), semantic entity matching, and terms importance assessment based on TF-IDF. We represent our topics in semantic space using Wikidata Q-ids, DBpedia, Wikipedia topics, YAGO, and other relevant ontologies. Such an approach based on semantic entities also supports hierarchical navigation within topics. For evaluation, we compare topic modeling results with claims already tagged by fact checkers. The work presented in this paper is useful for researchers and practitioners interested in semantic topic modeling of fake news narratives. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) represent a transformative innovation in transportation, promising enhanced safety, efficiency, and sustainability. Despite these promises, achieving robustness, reliability, and adherence to ethical standards in AV systems remains challenging due to the complexity of integrating diverse technologies. This survey reviews literature from 2017 to 2023, analyzing over 90 papers to explore the integration of knowledge graphs (KGs) into AV technologies. Our findings indicate that KGs significantly enhance AV systems by providing structured semantic understanding, improving real-time decision-making, and ensuring compliance with regulatory standards. The paper identifies that while KGs contribute to better environmental perception and contextual reasoning, challenges remain in their seamless integration with existing systems and in maintaining processing speed. We also address the ethical dimensions of AV decision-making, advocating for frameworks that prioritize safety and transparency. This review underscores the potential of KGs to address critical challenges in AV technologies, offering a hopeful and optimistic outlook for the development of robust, reliable, and socially responsible autonomous transportation solutions. [ABSTRACT FROM AUTHOR]

In recent years, applying building information modeling (BIM) digital technologies to cultural heritage management, monitoring, restoration, with the objective of advancing the sustainable development of both cultural heritage protection and tourism in China, has become a prominent research focus. However, there are a few studies that comprehensively investigate the relationship between BIM, Chinese cultural heritage, and sustainable tourism development. In order to explore the application of BIM in the protection and inheritance of Chinese cultural heritage, as well as its potential in promoting the sustainable development of cultural heritage tourism, this paper adopts the quantitative research method of bibliometrics to explore the research hotspots, development background, and evolution trends of BIM-driven sustainable development in Chinese cultural heritage tourism. By using data obtained from the China Knowledge Network database, multi-level bibliometrics analysis has been conducted through visualized knowledge graphs. The results suggest that the popular research keywords for driving sustainable cultural heritage tourism in China through BIM since year 2000 (23 years) include heritage tourism, heritage protection, building heritage, digital technology, and tourism development. Three research hotspots have been identified, which are cultural heritage protection, cultural heritage tourism development, and cultural heritage tourism management. In terms of tourism development and management, building virtual interactive scenes of cultural heritage facilitated by BIM to enhance tourism experience of tourists, using BIM to assist in efficient management, intelligent decision-making, and personalized services of cultural heritage tourism, assist in better promoting the sustainable development of cultural heritage tourism. In terms of coordinating and managing stakeholders in cultural heritage tourism, BIM technology provides technical support to the government, industry managers, and community residents in information communication, and industry management by constructing a digital model of cultural heritage to better balance the rights and interests of stakeholders. [ABSTRACT FROM AUTHOR]

The construction industry faces significant challenges with frequent accidents, largely due to the inefficient use of safety guidelines. These guidelines, which are often text and figure heavy, demand substantial human effort to identify the most relevant items for specific tasks and conditions. Additionally, the guidelines contain both central and peripheral elements, and central items are critical yet difficult to identify without extensive domain knowledge. This study proposes a novel recommendation framework to enhance the usability of these safety guidelines. By leveraging natural language processing (NLP) and knowledge graph (KG) modeling techniques, unstructured safety texts are transformed into a structured, interconnected KG. The PageRank and Louvain Clustering algorithm is then employed to rank guidelines by their relevance and importance. A case study on "High-rise Building Construction (General) Safety and Health Guidelines", using 'scaffolding' as the keyword, demonstrates the framework's effectiveness in improving retrieval efficiency and practical application. The analysis highlighted key clusters such as 'fall', 'drop', and 'scaffolding', with critical safety measures identified through their interconnections. This research not only overcomes the fragmentation of safety management documents but also contributes to advancing hazard analysis and risk prevention practices in construction management. [ABSTRACT FROM AUTHOR]

Temporal knowledge graph completion (TKGC) is the task of inferring missing facts based on existing ones in a temporal knowledge graph. In recent years, various TKGC methods have emerged, among which deep learning-based methods have achieved state-of-the-art performance. In order to understand the current research status of TKGC methods based on deep learning and promote further development in this field, in this paper, for the first time, we summarize the deep learning-based methods in TKGC research. First, we detail the background of TKGC, including task definition, benchmark datasets, and evaluation protocol. Then, we divide the existing deep learning-based TKGC methods into eight fine-grained categories according to their core technology and summarize them. Finally, we conclude the paper and present three future research directions for TKGC. [ABSTRACT FROM AUTHOR]

With the development of Intelligent Machine as a Service (IMaaS), devices increasingly require personalization, intelligence, and service orientation, making resource modeling a key challenge. Knowledge graph (KG) technology, known for unifying heterogeneous data, has become an essential tool for modeling and analyzing manufacturing resources. On this basis, this study proposes a novel resource KG construction method for IMaaS. First, an E-R diagram is used to divide the constant and variable entities and set the constant attributes and the constant relationships. Then, the triplets are named, the value space is set, and the schema layer is constructed. Finally, the related information about devices is used to fill the data layer, and then, the knowledge graph is generated. Meanwhile, this study utilizes desktop FDM 3D printing devices as a case example for validation. The method proposed in this study can enhance the accuracy and maintainability of equipment resource management in the manufacturing sector, effectively promoting subsequent activities such as management, analysis, and decision-making. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are equipped with various sensors to facilitate control and navigation. However, UAV sensors are highly susceptible to damage under complex flight environments, leading to severe accidents and economic losses. Although fault diagnosis methods based on deep neural networks have been widely applied in the mechanical field, these methods often fail to integrate multi-source information and overlook the importance of system prior knowledge. As a result, this study employs a spatial-temporal difference graph convolutional network (STDGCN) for the fault diagnosis of UAV sensors, where the graph structure naturally organizes the diverse sensors. Specifically, a difference layer enhances the feature extraction capability of the graph nodes, and the spatial-temporal graph convolutional modules are designed to extract spatial-temporal dependencies from sensor data. Moreover, to ensure the accuracy of the association graph, this research introduces the UAV's dynamic model as prior knowledge for constructing the association graph. Finally, diagnostic accuracies of 94.93%, 98.71%, and 92.97% were achieved on three self-constructed datasets. In addition, compared to commonly used data-driven approaches, the proposed method demonstrates superior feature extraction capabilities and achieves the highest diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Summarizing the causation of aviation accidents is conducive to enhancing aviation safety. The knowledge graph of aviation accident causation, constructed based on aviation accident reports, can assist in analyzing the causes of aviation accidents. With the continuous development of artificial intelligence technology, leveraging large language models for information extraction and knowledge graph construction has demonstrated significant advantages. This paper proposes an information extraction method for aviation accident causation based on Claude-prompt, which relies on the large-scale pre-trained language model Claude 3.5. Through prompt engineering, combined with a few-shot learning strategy and a self-judgment mechanism, this method achieves automatic extraction of accident-cause entities and their relationships. Experimental results indicate that this approach effectively improves the accuracy of information extraction, overcoming the limitations of traditional methods in terms of accuracy and efficiency in processing complex texts. It provides strong support for subsequently constructing a structured knowledge graph of aviation accident causation and conducting causation analysis of aviation accidents. [ABSTRACT FROM AUTHOR]

Equipment fault diagnosis NER is to extract specific entities from Chinese equipment fault diagnosis text, which is the premise of constructing an equipment fault diagnosis knowledge graph. Named entity recognition for equipment fault diagnosis can also provide important data support for equipment maintenance support. Equipment fault diagnosis text has complex semantics, fuzzy entity boundaries, and limited data size. In order to extract entities from the equipment fault diagnosis text, this paper presents an NER model for equipment fault diagnosis based on RoBERTa-wwm-ext and Deep Learning network integration. Firstly, this model uses the RoBERTa-wwm-ext to extract context-sensitive embeddings of text sequences. Secondly, the context feature information is obtained through the BiLSTM network. Thirdly, the CRF is combined to output the label sequence with a constraint relationship, improve the accuracy of sequence labeling task, and complete the entity recognition task. Finally, experiments and predictions are carried out on the constructed dataset. The results show that the model can effectively identify five types of equipment fault diagnosis entities and has higher evaluation indexes than the traditional model. Its precision, recall, and F1 value are 94.57%, 95.39%, and 94.98%, respectively. The case study proves that the model can accurately recognize the entity of the input text. [ABSTRACT FROM AUTHOR]

Precise and comprehensive model development is essential for predicting power network balance and maintaining power system analysis and optimization. The development of big data technologies and measurement systems has introduced new challenges in power grid modeling, simulation, and fault prediction. In-depth analysis of grid data has become vital for maintaining steady and safe operations. Traditional knowledge graphs can structure data in graph form, but identifying topological errors remains a challenge. Meanwhile, Graph Convolutional Networks (GCNs) can be trained on graph data to detect connections between entities, facilitating the identification of potential topological errors. Therefore, this paper proposes a method for power grid topological error identification that combines knowledge graphs with GCNs. The proposed method first constructs a knowledge graph to organize grid data and introduces a new GCN model for deep training, significantly improving the accuracy and robustness of topological error identification compared to traditional GCNs. This method is tested on the IEEE 30-bus system, the IEEE 118-bus system, and a provincial power grid system. The results demonstrate the method's effectiveness in identifying topological errors, even in scenarios involving branch disconnections and data loss. [ABSTRACT FROM AUTHOR]

Urban blue-green infrastructure (BGI) not only serves an ecological purpose but also contributes to the physical and psychological well-being of residents by providing cultural ecosystem services (CES), which are the nonmaterial benefits people obtain from ecosystems through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences. CES is a rising BGI research and management subject, with a growing number of papers in recent years. To identify and differentiate the latest research on the development of features based on cultural ecosystem services within blue-green infrastructure, we employed CiteSpace bibliometric methodologies to analyze pertinent papers for focusing on the developmental processes and key research areas. The publishing trend, research clusters, highly cited literature, research history, research frontiers and hot areas, and high-frequency and emerging keywords were studied and assessed after reviewing 14,344 relevant papers by CiteSpace software 6.3.1 from Web of Science. The standard domains concerned, according to the keyword visualization and high-value references, are implemented cultural ecosystem services assessment combined with natural-based solutions in green spaces, urban regions, residential areas, and sustainable development. In conclusion, the following recommendations are made: (1) When urban decision-makers incorporate the perspective of cultural ecosystem services into the strategic formulation of BGI, a broader spectrum of urban BGI types should be taken into account; (2) all categories of CES should be considered; (3) research on the application of cultural ecosystem services in urban blue-green infrastructure should be more effectively and flexibly integrated into urban governance; and (4) CES should be strategically employed to improve the physical health and psychological well-being of urban residents. [ABSTRACT FROM AUTHOR]

The incompleteness of the existing knowledge graphs in the railway domain creates information gaps, impacting their quality and effectiveness in the operation and maintenance of high-speed railway turnout switch machines. To address this, we propose a multi-layer model (KBGC) that combines KG-BERT, graph attention network (GAT), and Convolutional Embedding Network (ConvE) for knowledge graph completion in railway maintenance. KG-BERT fine-tunes a pre-trained BERT model to extract deep semantic features from entities and relationships, converting them into graph structures. GAT captures key structural relationships between nodes using an attention mechanism, producing enriched semantic and structural embeddings. Finally, ConvE reshapes and convolves these embeddings to learn complex entity interactions, enabling accurate link prediction. Extensive experiments on the HRTOM dataset, containing triplet data from high-speed railway turnout switch machines, demonstrate the model's effectiveness, achieving an MRR of 50.8% and a Hits@10 of 60.7%. These findings show that the KBGC model significantly improves knowledge graph completion, aiding railway maintenance personnel in decision making and preventive maintenance, and providing new tools for railway maintenance applications. [ABSTRACT FROM AUTHOR]

Knowledge graph embedding (KGE) has been identified as an effective method for link prediction, which involves predicting missing relations or entities based on existing entities or relations. KGE is an important method for implementing knowledge representation and, as such, has been widely used in driving intelligent applications w.r.t. question-answering systems, recommendation systems, and relationship extraction. Models based on convolutional neural networks (CNNs) have achieved good results in link prediction. However, as the coverage areas of knowledge graphs expand, the increasing volume of information significantly limits the performance of these models. This article introduces a triple-attention-based multi-channel CNN model, named ConvAMC, for the KGE task. In the embedding representation module, entities and relations are embedded into a complex space and the embeddings are performed in an alternating pattern. This approach helps in capturing richer semantic information and enhances the expressive power of the model. In the encoding module, a multi-channel approach is employed to extract more comprehensive interaction features. A triple attention mechanism and max pooling layers are used to ensure that interactions between spatial dimensions and output tensors are captured during the subsequent tensor concatenation and reshaping process, which allows preserving local and detailed information. Finally, feature vectors are transformed into prediction targets for embedding through the Hadamard product of feature mapping and reshaping matrices. Extensive experiments were conducted to evaluate the performance of ConvAMC on three benchmark datasets compared with state-of-the-art (SOTA) models, demonstrating that the proposed model outperforms all compared models across all evaluation metrics on two of the datasets, and achieves advanced link prediction results on most evaluation metrics on the third dataset. [ABSTRACT FROM AUTHOR]

Featured Application: This study proposes a multiple fine-grained nested named entity recognition model, which provides a solution for other specialized fields and lays the foundation for subsequent knowledge graph construction and intelligent inquiry system construction. Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrates the multiple fine-grained feature fusion (MFGFF) module with a BiLSTM neural network and employs an efficient global pointer (EGP) to predict the entity location encoding. In the MFGFF module, we designed three encoders: the character encoder, word encoder, and sentence encoder. This design effectively captured fine-grained features and improved the recognition accuracy of nested entities. Experimental results showed that the model performed robustly, with F1 scores of 91.98%, 73.32%, and 82.54% on the CDNER, CMeEE V2, and CLUENER datasets, respectively, outperforming other commonly used NER models. Specifically, on the CDNER dataset, the model achieved an F1 score of 79.68% for nested entity recognition. This research not only advances the development of a knowledge graph and intelligent question-answering system for chicken diseases, but also provides a viable solution for extracting disease information that can be applied to other livestock species. [ABSTRACT FROM AUTHOR]

Network security policy is regarded as a guideline for the use and management of the network environment, which usually formulates various requirements in the form of natural language. It can help network managers conduct standardized network attack detection and situation awareness analysis in the overall time and space environment of network security. However, in most cases, due to configuration updates or policy conflicts, there are often differences between the real network environment and network security policies. In this case, the consistency detection of network security policies is necessary. The previous consistency detection methods of security policies have some problems. Firstly, the detection direction is single, only focusing on formal reasoning methods to achieve logical consistency detection and solve problems. Secondly, the detection policy field is not comprehensive, focusing only on a certain type of problem in a certain field. Thirdly, there are numerous forms of data structures used for consistency detection, and it is difficult to unify the structured processing and analysis of rule library carriers and target information carriers. With the development of intelligent graph and data mining technology, the above problems have the possibility of optimization. This article proposes a new consistency detection approach for network security policy, which uses an intelligent graph database as a visual information carrier, which can widely connect detection information and achieve comprehensive detection across knowledge domains, physical devices, and detection methods. At the same time, it can also help users grasp the security associations with the real network environment based on the graph algorithm of the knowledge graph and intelligent reasoning. Furthermore, these actual network situations and knowledge bases can help managers improve policies more tailored to local conditions. This article also introduces the consistency detection process of typical cases of network security policies, demonstrating the practical details and effectiveness of this method. [ABSTRACT FROM AUTHOR]

To address the challenges posed by the vast and complex knowledge information in cultural heritage design, such as low knowledge retrieval efficiency and limited visualization, this study proposes a method for knowledge extraction and knowledge graph construction based on graph attention neural networks (GAT). Using Tang Dynasty gold and silver artifacts as samples, we establish a joint knowledge extraction model based on GAT. The model employs the BERT pretraining model to encode collected textual knowledge data, conducts sentence dependency analysis, and utilizes GAT to allocate weights among entities, thereby enhancing the identification of target entities and their relationships. Comparative experiments on public datasets demonstrate that this model significantly outperforms baseline models in extraction effectiveness. Finally, the proposed method is applied to the construction of a knowledge graph for Tang Dynasty gold and silver artifacts. Taking the Gilded Musician Pattern Silver Cup as an example, this method provides designers with a visualized and interconnected knowledge collection structure. [ABSTRACT FROM AUTHOR]

Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and supervised labels. This paper argues that existing knowledge-graph-based recommendation methods still suffer from insufficiently exploiting sparse information and the mismatch between personalized interests and general knowledge. This paper proposes a model named Adaptive Knowledge Contrastive Learning with Dynamic Attention (AKCL-DA) to address the above challenges. Specifically, instead of building contrastive views by randomly discarding information, in this study, an adaptive data augmentation method was designed to leverage sparse information effectively. Furthermore, a personalized dynamic attention network was proposed to capture knowledge-aware personalized behaviors by dynamically adjusting user attention, therefore alleviating the mismatch between personalized behavior and general knowledge. Extensive experiments on Yelp2018, LastFM, and MovieLens datasets show that AKCL-DA achieves a strong performance, improving the NDCG by 4.82%, 13.66%, and 4.41% compared to state-of-the-art models, respectively. [ABSTRACT FROM AUTHOR]

The knowledge within wheat production chain data has multiple levels and complex semantic relationships, making it difficult to extract knowledge from them. Therefore, this paper proposes a fine-grained knowledge extraction method for the wheat production chain based on ontology. For the first time, the conceptual layers of ploughing, planting, managing, and harvesting were defined around the main agricultural activities of the wheat production chain. Based on this, the entities, relationships, and attributes in the conceptual layers were defined at a fine-grained level, and a spatial–temporal association pattern layer with four conceptual layers, twenty-eight entities, and forty-two relationships was constructed. Then, based on the characteristics of the self-constructed dataset, the Word2vec-BiLSTM-CRF model was designed for extracting the knowledge within it, i.e., the entity–relationship–attribute model and the Word2vec-BiLSTM-CRF model in this paper were compared with the four SOTA models. The results show that the accuracy and F1 value improved by 8.44% and 8.89%, respectively, compared with the BiLSTM-CRF model. Furthermore, the entities of the pest and disease dataset were divided into two different granularities for the comparison experiment; the results show that for entities with "disease names" and "pest names", the recognition accuracy at the fine-grained level is improved by 32.71% and 31.58%, respectively, compared to the coarse-grained level, and the recognition performance of various fine-grained entities has been improved. [ABSTRACT FROM AUTHOR]

Ontologies are essential for the management and integration of heterogeneous datasets. This paper presents OntoBuilder, an advanced tool that leverages the structural capabilities of semantic labeled property graphs (SLPGs) in strict alignment with semantic web standards to create a sophisticated framework for data management. We detail OntoBuilder's architecture, core functionalities, and application scenarios, demonstrating its proficiency and adaptability in addressing complex ontological challenges. Our empirical assessment highlights OntoBuilder's strengths in enabling seamless visualization, automated ontology generation, and robust semantic integration, thereby significantly enhancing user workflows and data management capabilities. The performance of the linked data tools across multiple metrics further underscores the effectiveness of OntoBuilder. [ABSTRACT FROM AUTHOR]

Incorporating knowledge graphs as auxiliary information to enhance recommendation systems can improve the representations learning of users and items. Recommendation methods based on knowledge graphs can introduce user–item interaction learning into the item graph, focusing only on learning the node vector representations within a single graph; alternatively, they can treat user–item interactions and item graphs as two separate graphs and learn from each graph individually. Learning from two graphs has natural advantages in exploring original information and interaction information, but faces two main challenges: (1) in complex graph connection scenarios, how to adequately mine the self-information of each graph, and (2) how to merge interaction information from the two graphs while ensuring that user–item interaction information predominates. Existing methods do not thoroughly explore the simultaneous mining of self-information from both graphs and effective interaction information, leading to the loss of valuable insights. Considering the success of contrastive learning in mining self-information and auxiliary information, this paper proposes a dual-graph contrastive learning recommendation method based on knowledge graphs (KGDC) to explore a more accurate representations of users and items in recommendation systems based on external knowledge graphs. In the learning process within the self-graph, KGDC strengthens and represents the information of different connecting edges in both graphs, and extracts the existing information more fully. In interactive information learning, KGDC reinforces the interaction relationship between users and items in the external knowledge graph, realizing the leading role of the main task. We conducted a series of experiments on three standard datasets, and the results show that the proposed method can achieve better results. [ABSTRACT FROM AUTHOR]