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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]

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]

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]

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]

Achieving significant breakthroughs in both the fundamental theories and technological applications of artificial intelligence is essential for fostering its long-term development. Under the guidance of Professor Qian Xuesen's theory of technological science, exploring the internal mechanisms of knowledge evolution in artificial intelligence holds profound theoretical and practical significance for promoting sustainable technological advancement. This study draws on literature from the Web of Science (WOS) database and employs methods such as knowledge mapping, natural language processing, clustering analysis, and citation analysis to outline the knowledge structure of the field, clarify the trajectory of sustainable development, and trace the technological genealogy of VR/AR technologies.This study divides the knowledge structure within the field of technological science into "basic theoretical knowledge—applied basic knowledge—applied knowledge", enriching Qian's theory of technological science from within and providing strong intellectual support and technological pathways for sustainable technological development in practice. Artificial intelligence encompasses 10 distinct knowledge domains, among which machine learning and deep learning constitute the basic theoretical knowledge, data intelligence, computer vision, and swarm intelligence are the applied basic knowledge, and image processing and human-computer intelligence are the applied knowledge. The development of VR/AR technology has formed two main sustainable development paths: "machine learning—data intelligence—intelligent systems—human computer intelligence", and "deep learning—computer vision—image processing". [ABSTRACT FROM AUTHOR]

Sound planning for urban mobility is a key facet of securing a sustainable future for our urban systems, and requires the careful and comprehensive assessment of its components, such as the status of the cities' public transportation network, and how urban planners should invest in developing it. We use agent-based modelling, a tried and true method for such endeavours, for studying the history, planned future works and possible evolution of the tram line network in the Greater Geneva region. We couple these models with knowledge graphs, in a way that both are able to mutually enrich each other. Results show that the information organisation powers of knowledge graphs are highly relevant for effortlessly recounting past events and designing scenarios to be directly incorporated inside the agent-based model. The model features all 5 tram lines from the current real-world network, servicing a total of 15 communes. In turn, the model is capable of replaying past events, predicting future developments and exploring user-defined scenarios. It also harnesses its self-organisation properties to autonomously reconstruct an artificial public transportation network for the region based on two different initial networks, servicing up to 29 communes depending on the scenario. The data gathered from the simulation is effortlessly imported back into the initial knowledge graphs. The artificial networks closely resemble their real-world counterparts and demonstrate the predictive and prescriptive powers of our agent-based model. They constitute valuable assets towards a comprehensive assessment of urban mobility systems, compelling progress for the agent-based modelling field, and a convincing demonstration of its technical capabilities. [ABSTRACT FROM AUTHOR]

Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China's coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor knowledge to reduce knowledge redundancy and explore designs. A framework combining knowledge graphs and graph representational learning methods was proposed to identify targeted performance, decipher hidden information, and discover new designs. Unlike process-parameter-based machine learning methods, it used the relationship as semantic features to improve prediction precision (up to 0.81). Based on the proposed framework, a strain sensor was designed and tested, demonstrating a wide strain range (300%) and closely matching predicted performance. This predicted sensor performance outperforms similar materials. Overall, the present work is favorable to design constraints and paves the way for the long-awaited implementation of text-mining-based knowledge management for sensor systems, which will facilitate the intelligent sensor design process. [ABSTRACT FROM AUTHOR]

Diagnosing complex air compressor systems with traditional data-driven deep learning models often results in isolated fault diagnosis, ignoring correlations between concurrent faults. This paper introduces a knowledge graph construction approach for the air compressor fault diagnosis field, using after-sales business data as the source. We propose a model based on Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa), specifically tailored for constructing a knowledge graph for air compressor fault diagnosis. By integrating Whole Word Masking (WWM) technology, Bidirectional Long Short-Term Memory (BiLSTM), and Conditional Random Fields (CRFs), our approach effectively extracts specific entities from unstructured data. On our dataset, the model achieved an average accuracy of 0.7962 and an F1 score of 0.7956, demonstrating notable improvements in both accuracy and recall for entity recognition tasks. The extracted entities were subsequently stored in a Neo4j graph database, facilitating the construction of a domain-specific knowledge graph for air compressor fault diagnosis. [ABSTRACT FROM AUTHOR]

In the era of information and communication technology (ICT), the advancement of science and technology has led to a trend of diversification in map representation. However, the lack of professional knowledge means that there is still a challenge in determining the appropriate type of thematic map for land use expression. To address this issue, this paper proposes a knowledge recommendation method for land use thematic maps based on the theory of visualization dimensions. Firstly, we establish a knowledge ontology of land use thematic maps centered on spatial data, data characteristics, visualization dimensions, thematic map forms, and application scenarios. A land use thematic map knowledge graph is constructed through knowledge extraction and storage operations. Secondly, knowledge embedding is performed on the knowledge graph to enable the knowledge-based expression of map visualization elements. Finally, based on the knowledge elements of land use thematic expression, a similarity calculation model is established to calculate the similarity between input data and the spatial data characteristics, visualization dimensions, and application scenarios within the knowledge graph, deriving a comprehensive similarity result to achieve precise recommendation for land use thematic map forms. The results show that the method can provide a more accurate visualization reference for the selection of land use themes, meeting the diversified needs of land use thematic expression to a certain extent. [ABSTRACT FROM AUTHOR]

Network risk assessment should include the impact of the relationship between vulnerabilities, in order to conduct a more in-depth and comprehensive assessment of vulnerabilities and network-related risks. However, the impact of extracting the relationship between vulnerabilities mainly relies on manual processes, which are subjective and inefficient. To address these issues, this paper proposes a dual-layer knowledge representation model that combines various attributes and structural information of entities. This article first constructs a vulnerability knowledge graph and proposes a two-layer knowledge representation learning model based on it. Secondly, in order to more accurately assess the actual risk of vulnerabilities in specific networks, this paper proposes a vulnerability risk calculation model based on impact relationships, which realizes the risk assessment and ranking of vulnerabilities in specific network scenarios. Finally, based on the research on automatic prediction of the impact relationship between vulnerabilities, this paper proposes a new Bayesian attack graph network risk assessment model for inferring the possibility of device intrusion in the network. The experimental results show that the model proposed in this study outperforms traditional evaluation methods in relationship prediction tasks, demonstrating its efficiency and accuracy in complex network environments. This model achieves efficient resource utilization by simplifying training parameters and reducing the demand for computing resources. In addition, this method can quantitatively evaluate the success probability of attacking specific devices in the network topology, providing risk assessment and defense strategy support for network security managers. [ABSTRACT FROM AUTHOR]

Medical texts are rich in specialized knowledge and medical information. As the medical and healthcare sectors are becoming more digitized, many medical texts must be effectively harnessed to derive insights and patterns. Thus, great attention is directed to this emerging research area. Generally, natural language processing (NLP) algorithms are employed to extract comprehensive information from unstructured medical texts, aiming to construct a graphical database for medical knowledge. One of the needs is to optimize model sizes while maintaining the precision of the BART algorithm. A novel carefully designed algorithm, called ALBERT+Bi-LSTM+CRF, is introduced. In this way, both enhanced efficiency and scalability are attained. When entities are extracted, the constructed algorithm achieves 91.8%, 92.5%, and 94.3% for the F-score, precision, and recall, respectively. The proposed algorithm also achieves remarkable outcomes in extracting relations, with 88.3%, 88.1%, and 88.4% for the F-score, precision, and recall, respectively. This further underscores its practicality in the graphical construction of medical knowledge. [ABSTRACT FROM AUTHOR]

Knowledge tracing is a significant research area in educational data mining, aiming to predict future performance based on students' historical learning data. In the field of programming, several challenges are faced in knowledge tracing, including inaccurate exercise representation and limited student information. These issues can lead to biased models and inaccurate predictions of students' knowledge states. To effectively address these issues, we propose a novel programming knowledge tracing model named GPPKT (Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing), which enhances performance by using knowledge graphs and personalized answer sequences. Specifically, we establish the associations between well-defined knowledge concepts and exercises, incorporating student learning abilities and latent representations generated from personalized answer sequences using Variational Autoencoders (VAE) in the model. This deep knowledge tracing model employs Long Short-Term Memory (LSTM) networks and attention mechanisms to integrate the embedding vectors, such as exercises and student information. Extensive experiments are conducted on two real-world programming datasets. The results indicate that GPPKT outperforms state-of-the-art methods, achieving an AUC of 0.8840 and an accuracy of 0.8472 on the Luogu dataset, and an AUC of 0.7770 and an accuracy of 0.8799 on the Codeforces dataset. This demonstrates the superiority of the proposed model, with an average improvement of 9.03% in AUC and 2.02% in accuracy across both datasets. [ABSTRACT FROM AUTHOR]

The whole train design of an Electric Multiple Unit (EMU) involves multiple domains and scenarios, thus requiring comprehensive consideration of various factors during the design process. Traditional design methods often utilize text-based approaches to model systems; however, such documentation-based designs often suffer from semantic heterogeneity, inconsistent data sources, and also struggle to provide a more intuitive overview of the overall design process. To address these issues, this paper proposes a method based on ontology–knowledge collaborative drive to achieve integration of the overall EMU design. Firstly, we employ the System Modeling Language (SysML) to construct the Model-Based Systems Engineering (MBSE) model of the EMU, establishing functional and physical architecture element models, with the EMU MBSE model serving as input. Subsequently, in the requirement model, architecture model, and traceability model, we utilize top-level ontology to construct the EMU ontology framework in a top-down manner. Lastly, leveraging the Neo4j database, we employ a knowledge graph (KG) approach to fill domain knowledge into each model in a bottom-up manner, thereby realizing the ontology–knowledge collaborative drive for the overall EMU design construction. The effectiveness of the proposed method is validated using the EMU Passenger Information System (PIS) and Traction transformer System (TS) as examples. [ABSTRACT FROM AUTHOR]

This paper explores the application of The World Avatar (TWA) dynamic knowledge graph to connect isolated data and assess the impact of rising sea levels in Singapore. Current sea level rise vulnerability assessment tools are often regional, narrow in scope (e.g., economic or cultural aspects only), and are inadequate in representing complex non-geospatial data consistently. We apply TWA to conduct a multi-perspective impact assessment of sea level rise in Singapore, evaluating vulnerable buildings, road networks, land plots, cultural sites, and populations. We introduce OntoSeaLevel, an ontology to describe sea level rise scenarios, and its impact on broader elements defined in other ontologies such as buildings (OntoBuiltEnv ontology), road networks (OpenStreetMap ontology), and land plots (Ontoplot and Ontozoning ontology). We deploy computational agents to synthesise data from government, industry, and other publicly accessible sources, enriching buildings with metadata such as property usage, estimated construction cost, number of floors, and gross floor area. An agent is applied to identify and instantiate the impacted sites using OntoSeaLevel. These sites include vulnerable buildings, land plots, cultural sites, and populations at risk. We showcase these sea level rise vulnerable elements in a unified visualisation, demonstrating TWA's potential as a planning tool against sea level rise through vulnerability assessment, resource allocation, and integrated spatial planning. [ABSTRACT FROM AUTHOR]

In the context of crowd innovation and the generative design driven by big language models, the exploration of personalized requirements has become a key in significantly improving product innovation, concept feasibility, and design interaction efficiency. To mine a large number of vague and unexpressed implicit requirements of personalized products, a domain knowledge graph-based method is proposed in this research. First, based on the classical theory of design science, the characteristics and categories of personalized implicit requirements are analyzed, and the theoretical basis of implicit requirement mining is formed. Next, in order to improve the practicability and construction efficiency of the domain knowledge graph, a more informative ontology is constructed, and better-performing natural language processing (NLP) models are proposed. Then, a multi-category personalized implicit requirement mining method based on a knowledge graph is proposed. Finally, a platform was developed based on the technical solution proposed in this study, and an example verification was conducted in the field of electromechanical engineering. The efficiency improvement of the training model proposed in the research was analyzed, and the practicality of implicit requirement mining methods are discussed. [ABSTRACT FROM AUTHOR]

Commercial aircraft fault diagnosis is an important means to ensure the reliability and safety of commercial aircraft. Traditional knowledge-driven and data-driven fault diagnosis methods lack interpretability in engineering mechanisms, making them difficult to promote and apply. To address the issue of lack of interpretability, this paper conducts a fault knowledge graph for commercial aircraft fault diagnosis, using the fault logic in the logic diagram to increase the interpretability of diagnostic work. Firstly, to avoid the inefficiency of logic diagram applications, an executable logic diagram model is established, which can perform mathematical analysis and achieve fault diagnosis and localization using operational data as input. Then, the logic diagram is sorted out to obtain the hidden fault knowledge in the logic diagram, which is used to construct a fault knowledge graph to help achieve cause localization and rapid troubleshooting. The methods proposed in this paper are all validated through case studies of abnormal low-pressure faults in domestic commercial aircraft hydraulic systems. The results show that the logic diagram model can perform model simulation and fault diagnosis based on operational data, and the fault knowledge graph can quickly locate abnormal monitoring parameters and guide troubleshooting work based on existing information. [ABSTRACT FROM AUTHOR]

The amount of data related to cyber threats and cyber attack incidents is rapidly increasing. The extracted information can provide security analysts with useful Cyber Threat Intelligence (CTI) to enhance their decision-making. However, because the data sources are heterogeneous, there is a lack of common representation of information, rendering the analysis of CTI complicated. With this work, we aim to review ongoing research on the use of semantic web tools such as ontologies and Knowledge Graphs (KGs) within the CTI domain. Ontologies and KGs can effectively represent information in a common and structured schema, enhancing interoperability among the Security Operation Centers (SOCs) and the stakeholders on the field of cybersecurity. When fused with Machine Learning (ML) and Deep Learning (DL) algorithms, the constructed ontologies and KGs can be augmented with new information and advanced inference capabilities, facilitating the discovery of previously unknown CTI. This systematic review highlights the advancements of this field over the past and ongoing decade and provides future research directions. [ABSTRACT FROM AUTHOR]

Automatic radiology report generation is a task that combines artificial intelligence and medical information processing, and it fully relies on computer vision and natural language processing techniques. Nowadays, automatic radiology report generation is still a very challenging task because it requires semantically adequate alignment of data from two modalities: radiology images and text. Existing approaches tend to focus on coarse-grained alignment at the global level and do not take into account the disease characteristics of radiology images at fine-grained semantics, which results in the generated reports potentially omitting key disease diagnostic descriptions. In this work, we propose a new approach, disease-knowledge-enhanced fine-grained image–text alignment for automatic radiology report generation (DKA-RG). The method combines global and disease-level alignment, thus facilitating the extraction of fine-grained disease features by the model. Our approach also introduces a knowledge graph to inject medical domain expertise into the model. Our proposed DKA-RG consists of two training steps: the image–report alignment stage and the image-to-report generation stage. In the alignment stage, we use global contrastive learning to align images and texts from a high level and also augment disease contrastive learning with medical knowledge to enhance the disease detection capability. In the report generation stage, the report text generated from the images is more accurate in describing the disease information thanks to sufficient alignment. Through extensive quantitative and qualitative experiments on two widely used datasets, we validate the effectiveness of our DKA-RG on the task of radiology report generation. Our DKA-RG achieves superior performance on multiple types of metrics (natural language generation and clinical efficacy metrics) compared to existing methods, demonstrating that the method can improve the reliability and accuracy of automatic radiology report generation systems. [ABSTRACT FROM AUTHOR]

The purpose of representation learning is to encode the entities and relations in a knowledge graph as low-dimensional and real-valued vectors through machine learning technology. Traditional representation learning methods like TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of a graph's entities, are effective for learning the embeddings of knowledge bases, but struggle to effectively model complex relations like one-to-many, many-to-one, and many-to-many. To overcome the above issues, we introduce a new method for knowledge representation, reasoning, and completion based on multi-translation principles and TransE (TransE-MTP). By defining multiple translation principles (MTPs) for different relation types, such as one-to-one and complex relations like one-to-many, many-to-one, and many-to-many, and combining MTPs with a typical translating-based model for modeling multi-relational data (TransE), the proposed method, TransE-MTP, ensures that multiple optimization objectives can be targeted and optimized during training on complex relations, thereby providing superior prediction performance. We implement a prototype of TransE-MTP to demonstrate its effectiveness at link prediction and triplet classification on two prominent knowledge graph datasets: Freebase and Wordnet. Our experimental results show that the proposed method enhanced the performance of both TransE and knowledge graph embedding by translating on hyperplanes (TransH), which confirms its effectiveness and competitiveness. [ABSTRACT FROM AUTHOR]

Featured Application: This work expresses the correlation among accident causal factors, flight phases, accident types, and consequences. It identifies the potential risks of various accident causal factors to help eliminate risk hazards and reduce the probability of aviation accidents, providing auxiliary decision making and a practical reference for preventing aviation accidents. Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management. [ABSTRACT FROM AUTHOR]

Carbon-neutral city research has attracted widespread attention. However, a comprehensive review of this research has not been conducted, and it is unclear how the various perspectives have evolved. In this study, CNKI and Web of Science were used as data sources. By summarizing the research results of carbon-neutral cities in recent years, the dynamics evolution trend is revealed, and the frontiers are explored. We found that: (1) the themes and contents of carbon-neutral city research were diverse and intersecting and mainly focused on energy, industrial structure, transportation, and building. (2) The knowledge map of author cooperation did not show many aggregates, which indicated that the cooperation and exchanges between relevant researchers are insufficient. (3) Chinese research on carbon-neutral cities was extensive and fruitful, taking the lead in the research in this field. Based on the current situation and trends, we provided a scientific reference for the development of carbon-neutral cities. Current research foci and cutting-edge findings will help to make cities more climate resilient, sustainable and livable. Understanding and magnifying these foci is what will help countries achieve their climate mitigation and carbon neutrality goals sooner rather than later. [ABSTRACT FROM AUTHOR]

The division of urban functional zones is crucial for understanding urban characteristics and aiding in urban management and planning. Traditional methods, like dividing based on blocks and grids, are insufficient for modern demands. To address this, a knowledge-graph-supported method for building functional category division is proposed. Firstly, the associations between points of interest (POI) and buildings are established using triangulation and buffer zones. Then, a knowledge graph of buildings is constructed through entity and relationship extraction. A functional category classification model supported by the Z-score is designed using the semantic characterizations of surrounding POIs for inference rules. The results demonstrate high accuracy in building functional category division, supporting the refinement and intelligent expression of urban functional zones for urban construction, planning, and management. [ABSTRACT FROM AUTHOR]

Multimodal knowledge graph completion necessitates the integration of information from multiple modalities (such as images and text) into the structural representation of entities to improve link prediction. However, most existing studies have overlooked the interaction between different modalities and the symmetry in the modal fusion process. To address this issue, this paper proposed a Transformer-based knowledge graph link prediction model (MM-Transformer) that fuses multimodal features. Different modal encoders are employed to extract structural, visual, and textual features, and symmetrical hybrid key-value calculations are performed on features from different modalities based on the Transformer architecture. The similarities of textual tags to structural tags and visual tags are calculated and aggregated, respectively, and multimodal entity representations are modeled and optimized to reduce the heterogeneity of the representations. The experimental results show that compared with the current multimodal SOTA method, MKGformer, MM-Transformer improves the Hits@1 and Hits@10 evaluation indicators by 1.17% and 1.39%, respectively, proving that the proposed method can effectively solve the problem of multimodal feature fusion in the knowledge graph link prediction task. [ABSTRACT FROM AUTHOR]

Conflict resolution for knowledge graphs (KGs) is a critical technique in knowledge fusion, ensuring the resolution of conflicts between existing KGs and external knowledge while maintaining post-fusion accuracy. However, current approaches often encounter difficulties with external triples involving unseen entities due to limited knowledge. Moreover, current methodologies typically overlook conflict detection prior to resolution, a crucial step for accurate truth inference. This paper introduces CRDL, an innovative approach that leverages conflict detection and large language models (LLMs) to identify truths. By employing conflict detection, we implement precise filtering strategies tailored to various types of relations and attributes. By designing prompts and injecting relevant information into an LLM, we identify triples with unseen entities. Experimental results demonstrate the superiority of CRDL over baseline methods. Specifically, our method surpasses the state-of-the-art by achieving a 56.4% improvement in recall and a 68.2% increase in F1-score. These results clearly illustrate the enhanced performance and effectiveness of our approach. Additionally, ablation studies and further analyses underscore the importance of the components within CRDL. [ABSTRACT FROM AUTHOR]

The combined use of multiple medications is common in treatment, which may lead to severe drug–drug interactions (DDIs). Deep learning methods have been widely used to predict DDIs in recent years. However, current models need help to fully understand the characteristics of drugs and the relationships between these characteristics, resulting in inaccurate and inefficient feature representations. Beyond that, existing studies predominantly focus on analyzing a single DDIs, failing to explore multiple similar DDIs simultaneously, thus limiting the discovery of common mechanisms underlying DDIs. To address these limitations, this research proposes a method based on M-Transformer and knowledge graph for predicting DDIs, comprising a dual-pathway approach and neural network. In the first pathway, we leverage the interpretability of the transformer to capture the intricate relationships between drug features using the multi-head attention mechanism, identifying and discarding redundant information to obtain a more refined and information-dense drug representation. However, due to the potential difficulty for a single transformer model to understand features from multiple semantic spaces, we adopted M-Transformer to understand the structural and pharmacological information of the drug as well as the connections between them. In the second pathway, we constructed a drug–drug interaction knowledge graph (DDIKG) using drug representation vectors obtained from M-Transformer as nodes and DDI types as edges. Subsequently, drug edges with similar interactions were aggregated using a graph neural network (GNN). This facilitates the exploration and extraction of shared mechanisms underlying drug–drug interactions. Extensive experiments demonstrate that our MTrans model accurately predicts DDIs and outperforms state-of-the-art models. [ABSTRACT FROM AUTHOR]

The rise of knowledge graphs has been instrumental in advancing artificial intelligence (AI) research. Extracting entity and relation triples from unstructured text is crucial for the construction of knowledge graphs. However, Chinese text has a complex grammatical structure, which may lead to the problem of overlapping entities. Previous pipeline models have struggled to address such overlap problems effectively, while joint models require entity annotations for each predefined relation in the set, which results in redundant relations. In addition, the traditional models often lead to task imbalance by overlooking the differences between tasks. To tackle these challenges, this research proposes a global pointer network based on relation prediction and loss function improvement (GPRL) for joint extraction of entities and relations. Experimental evaluations on the publicly available Chinese datasets DuIE2.0 and CMeIE demonstrate that the GPRL model achieves a 1.2–26.1% improvement in F1 score compared with baseline models. Further, experiments of overlapping classification conducted on CMeIE have also verified the effectiveness of overlapping triad extraction and ablation experiments. The model is helpful in identifying entities and relations accurately and can reduce redundancy by leveraging relation filtering and the global pointer network. In addition, the incorporation of a multi-task learning framework balances the loss functions of multiple tasks and enhances task interactions. [ABSTRACT FROM AUTHOR]

With the development of seaborne trade, international maritime crime is becoming increasingly complex. Detecting maritime threats by fusing the physical movement data from traditional physical sensors is not sufficient. Thus, soft data, including intelligence reports and news articles, need to be incorporated into the situational awareness models of maritime threats. In this regard, this study developed an automated construction method of knowledge graphs for pirate events, which lays a foundation for subsequent maritime threat reasoning and situational awareness. First, a knowledge graph ontology model for pirate events was designed. Secondly, the BERT-BiLSTM-CRF model is proposed for named-entity recognition, and an entity linking algorithm based on distant learning and context attention mechanism is proposed to remove the conceptual ambiguity. Thirdly, based on traditional distant supervision relation extraction, which is based on sentence-level attention mechanism, bag-level and group-level attention mechanism methods are additionally proposed to further enhance the performance of distant supervision relation extraction. The proposed model demonstrated high performance in named-entity recognition, entity linking, and relation extraction tasks, with an overall F1-score of over 0.94 for NER and significant improvements in entity linking and relation extraction compared to traditional methods. The constructed knowledge graphs effectively support maritime threat reasoning and situational awareness, offering a substantial contribution to the field of maritime security. Our discussion highlights the model's strengths and potential areas for future work, while the conclusion emphasizes the practical implications and the readiness of our approach for real-world applications. [ABSTRACT FROM AUTHOR]

In the construction industry, the professional evaluation of construction schemes represents a crucial link in ensuring the safety, quality and economic efficiency of the construction process. However, due to the large number and diversity of construction schemes, traditional expert review methods are limited in terms of timeliness and comprehensiveness. This leads to an increasingly urgent requirement for intelligent check of construction schemes. This paper proposes an intelligent compliance checking method for construction schemes that integrates knowledge graphs and large language model (LLM). Firstly, a method for constructing a multi-dimensional, multi-granular knowledge graph for construction standards is introduced, which serves as the foundation for domain-specific knowledge support to the LLM. Subsequently, a parsing module based on text classification and entity extraction models is proposed to automatically parse construction schemes and construct pathways for querying the knowledge graph of construction standards. Finally, an LLM is leveraged to achieve an intelligent compliance check. The experimental results demonstrate that the proposed method can effectively integrate domain knowledge to guide the LLM in checking construction schemes, with an accuracy rate of up to 72%. Concurrently, the well-designed prompt template and the comprehensiveness of the knowledge graph facilitate the stimulation of the LLM's reasoning ability. This work contributes to exploring the application of LLMs and knowledge graphs in the vertical domain of text compliance checking. Future work will focus on optimizing the integration of LLMs and domain knowledge to further improve the accuracy and practicality of the intelligent checking system. [ABSTRACT FROM AUTHOR]

The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving diagnostic criteria such as the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) and to contextualize rich patient data effectively. This study proposes a novel approach for enhancing information extraction from mental health data by integrating medical knowledge graphs and large language models (LLMs). Our method leverages the structured organization of knowledge graphs specifically designed for the rich domain of mental health, combined with the powerful predictive capabilities and zero-shot learning abilities of LLMs. This research enhances the quality of knowledge graphs through entity linking and demonstrates superiority over traditional information extraction techniques, making a significant contribution to the field of mental health. It enables a more fine-grained analysis of the data and the development of new applications. Our approach redefines the manner in which mental health data are extracted and utilized. By integrating these insights with existing healthcare applications, the groundwork is laid for the development of real-time patient monitoring systems. The performance evaluation of this knowledge graph highlights its effectiveness and reliability, indicating significant advancements in automating medical data processing and depression management. [ABSTRACT FROM AUTHOR]

The development of cold chain logistics for seafood plays a pivotal role in guaranteeing food safety, promoting economic progress, reducing losses, and fostering sustainable development, thereby enhancing the overall efficiency of the seafood supply chain. This study conducted a comprehensive investigation into the primary research focuses of the seafood cold chain logistics field using the literature on visualization analysis software (CiteSpace (6.2.R6), VOSviewer (1.6.20), and RStudio Bibliometrix (4.4.0)). A total of 1787 articles were collected and further analyzed from the China National Knowledge Infrastructure (CNKI), Web of Science (WOS), and Google databases over 12 years, establishing a knowledge framework for research in seafood cold chain logistics. Through the utilization of keyword clustering and emerging analysis techniques, the study constructed a knowledge map that intuitively describes the emerging trends and key hotspot in this field. The results indicate a growing trend in the seafood cold chain logistics field, with disciplines such as mathematics, systems, and physics being notably prominent. Key terms such as "cold chain logistics", "highlighted supply chain management", "frozen storage techniques", "cold storage practices", "post-harvest loss prevention strategies", and "optimization of the cold chain" frequently appear in the literature, highlighting the importance of interdisciplinary academic research in these areas. By exploring the current development of the seafood cold chain logistics field, we strengthen the research gaps in the literature and propose future research directions. Therefore, well-conducted bibliometric studies can play a crucial role in advancing the field by providing comprehensive insights, facilitating scholarly discussions, identifying knowledge gaps, generating new research ideas, and showcasing their intended contributions to the field. [ABSTRACT FROM AUTHOR]

This research proposes an innovative, intelligent hand-assisted diagnostic system aiming to achieve a comprehensive assessment of hand function through information fusion technology. Based on the single-vision algorithm we designed, the system can perceive and analyze the morphology and motion posture of the patient's hands in real time. This visual perception can provide an objective data foundation and capture the continuous changes in the patient's hand movement, thereby providing more detailed information for the assessment and providing a scientific basis for subsequent treatment plans. By introducing medical knowledge graph technology, the system integrates and analyzes medical knowledge information and combines it with a voice question-answering system, allowing patients to communicate and obtain information effectively even with limited hand function. Voice question-answering, as a subjective and convenient interaction method, greatly improves the interactivity and communication efficiency between patients and the system. In conclusion, this system holds immense potential as a highly efficient and accurate hand-assisted assessment tool, delivering enhanced diagnostic services and rehabilitation support for patients. [ABSTRACT FROM AUTHOR]

Knowledge graphs are extensively utilized in diverse fields such as search engines, recommendation systems, and dialogue systems, and knowledge graph reasoning plays an important role in the aforementioned domains. Graph neural networks demonstrate the capability to effectively capture and process the graph structure inherent in knowledge graphs, leveraging the relationships between nodes and edges to enable efficient reasoning. Current research on graph neural networks relies on predefined propagation paths. The models based on predefined propagation paths overlook the correlation between entities and query relations, limiting adaptability and scalability. In this paper, we propose an adaptive graph neural network with an incremental learning mechanism to search the adaptive propagation path in order to retain promising targets. In detail, the incremental learning mechanism is able to filter out unrelated entities in the propagation path by incorporating the node sampling technique to increase the learning efficiency of the model. In addition, the incremental learning mechanism can select semantically related entities, which promotes the capture of meaningful connections among different nodes in the knowledge graph. At the same time, we apply the parameter initialization module to accelerate the convergence and improve the robustness of the model. Experimental results on benchmark datasets demonstrate that the adaptive graph neural network with the incremental learning mechanism has excellent semantic awareness ability, which surpasses several excellent knowledge graph reasoning models. [ABSTRACT FROM AUTHOR]

Entity alignment aims to match entities with the same semantics from different knowledge graphs. Most existing studies use neural networks to combine graph-structure information and additional entity information (such as names, descriptions, images, and attributes) to achieve entity alignment. However, due to the heterogeneity of knowledge graphs, aligned entities often do not have the same neighbors, which makes it difficult to utilize the structural information from knowledge graphs and results in a decrease in alignment accuracy. Therefore, in this paper, we propose an interaction model that exploits only the additional information on entities. Our model utilizes names, attributes, and neighbors of entities for interaction and introduces attention interaction to extract features to further evaluate the matching scores between entities. Our model is applicable to Chinese datasets, and experimental results show that it has achieved good results on the Chinese medical datasets denoted MED-BBK-9K. [ABSTRACT FROM AUTHOR]

The suddenness of landslide disasters often causes significant loss of life and property. Accurate assessment of landslide disaster susceptibility is of great significance in enhancing the ability of accurate disaster prevention. To address the problems of strong subjectivity in the selection of assessment indicators and low efficiency of the assessment process caused by the insufficient application of a priori knowledge in landslide susceptibility assessment, in this paper, we propose a novel landslide susceptibility assessment framework by combing domain knowledge graph and machine learning algorithms. Firstly, we combine unstructured data, extract priori knowledge based on the Unified Structure Generation for Universal Information Extraction Pre-trained model (UIE) fine-tuned with a small amount of labeled data to construct a landslide susceptibility knowledge graph. We use Paired Relation Vectors (PairRE) to characterize the knowledge graph, then construct a target area characterization factor recommendation model by calculating spatial correlation, attribute similarity, Term Frequency–Inverse Document Frequency (TF-IDF) metrics. We select the optimal model and optimal feature combination among six typical machine learning (ML) models to construct interpretable landslide disaster susceptibility assessment mapping. Experimental validation and analysis are carried out on the three gorges area (TGA), and the results show the effectiveness of the feature factors recommended by the knowledge graph characterization learning, with the overall accuracy of the model after adding associated disaster factors reaching 87.2%. The methodology proposed in this research is a better contribution to the knowledge and data-driven assessment of landslide disaster susceptibility. [ABSTRACT FROM AUTHOR]

The geoscience knowledge graph (GeoKG) has gained worldwide attention due to its ability in the formal representation of spatiotemporal features and relationships of geoscience knowledge. Currently, a quantitative review of the state and trends in GeoKG is still scarce. Thus, a bibliometric analysis was performed in this study to fill the gap. Specifically, based on 294 research articles published from 2012 to 2023, we conducted analyses in terms of the (1) trends in publications and citations; (2) identification of the major papers, sources, researchers, institutions, and countries; (3) scientific collaboration analysis; and (4) detection of major research topics and tendencies. The results revealed that the interest in GeoKG research has rapidly increased after 2019 and is continually expanding. China is the most productive country in this field. Co-authorship analysis shows that inter-national and inter-institutional collaboration should be reinforced. Keyword analysis indicated that geoscience knowledge representation, information extraction, GeoKG construction, and GeoKG-based multi-source data integration were current hotspots. In addition, several important but currently neglected issues, such as the integration of Large Language Models, are highlighted. The findings of this review provide a systematic overview of the development of GeoKG and provide a valuable reference for future research. [ABSTRACT FROM AUTHOR]

Research on forest carbon storage (FCS) is crucial for the sustainable development of human society given the context of global climate change. Previous FCS studies formed the science base of the FCS field but lacked a macrolevel knowledge summary. This study combined the scientometric mapping tool VOSviewer and multiple statistical models to conduct a comprehensive knowledge graph mining and analysis of global FCS papers (covering 101 countries, 1712 institutions, 5435 authors, and 276 journals) in the Web of Science database as of 2022, focusing on revealing the macro spatiotemporal pattern, multidimensional research status, and topic evolution process of FCS research at the global scale, so as to grasp the status of global FCS research more clearly and comprehensively, thereby facilitating the future decision-making and practice of researchers. The results showed the following: (1) In the past three decades, the number of FCS papers indicated an increasing trend, with a growth rate of 4.66/yr, particularly significant after 2010. These papers were mainly from Europe, the Americas, and Asia, while there was a huge gap between Africa, Oceania, and the above regions. (2) For the research status at the national, institutional, scholar, and journal levels, the USA, with 331 FCS papers and 18,653 total citations, was the most active and influential country in global FCS research; the United States Forest Service topped the influential ranking with 4115 citations; Grant M. Domke and Jerome Chave were the most active and influential FCS researchers globally, respectively. China's activity (237 papers) and influence (5403 citations) ranked second, and the Chinese Academy of Sciences was the most active research institution in the world. Currently, FCS research is published in a growing number of journals, among which Forest Ecology and Management ranked first in the number of papers (154 papers) and citations (6374 citations). (3) In recent years, the keyword frequency of monitoring methods, driving factors, and reasonable management for FCS has increased rapidly, and many new related keywords have emerged, which means that researchers are not only focusing on the estimation and monitoring of FCS but also increasingly concerned about its driving mechanism and sustainable development. [ABSTRACT FROM AUTHOR]

Effective safety management is crucial in the construction industry. The growing interest in employing Knowledge Graphs (KGs) for safety management in construction is driven by the need for efficient computing-aided safety practices. This paper systematically reviews the literature related to automating safety management processes through knowledge base systems, focusing on the creation and utilization of KGs for construction safety. It captures current methodologies for developing and using KGs in construction safety management, outlining the techniques for each phase of KG development, including scope identification, integration of external data, ontological modeling, data extraction, and KG completion. This provides structured guidance on building a KG for safety management. Moreover, this paper discusses the challenges and limitations that hinder the wider adoption of KGs in construction safety management, leading to the identification of goals and considerations for future research. [ABSTRACT FROM AUTHOR]