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TSMixerE:Entity Context-Aware Method for Static Knowledge Graph Completion 认领 引用
1
作者 Jianzhong Chen Yunsheng Xu +2 位作者 Zirui Guo Tianmin Liu Ying Pan 《Computers, Materials & Continua》 SCIE EI 2026年第4期2207-2230,共24页
The rapid development of information technology and accelerated digitalization have led to an explosive growth of data across various fields.As a key technology for knowledge representation and sharing,knowledge graph... The rapid development of information technology and accelerated digitalization have led to an explosive growth of data across various fields.As a key technology for knowledge representation and sharing,knowledge graphs play a crucial role by constructing structured networks of relationships among entities.However,data sparsity and numerous unexplored implicit relations result in the widespread incompleteness of knowledge graphs.In static knowledge graph completion,most existing methods rely on linear operations or simple interaction mechanisms for triple encoding,making it difficult to fully capture the deep semantic associations between entities and relations.Moreover,many methods focus only on the local information of individual triples,ignoring the rich semantic dependencies embedded in the neighboring nodes of entities within the graph structure,which leads to incomplete embedding representations.To address these challenges,we propose Two-Stage Mixer Embedding(TSMixerE),a static knowledge graph completion method based on entity context.In the unit semantic extraction stage,TSMixerE leveragesmulti-scale circular convolution to capture local features atmultiple granularities,enhancing the flexibility and robustness of feature interactions.A channel attention mechanism amplifies key channel responses to suppress noise and irrelevant information,thereby improving the discriminative power and semantic depth of feature representations.For contextual information fusion,a multi-layer self-attentionmechanism enables deep interactions among contextual cues,effectively integrating local details with global context.Simultaneously,type embeddings clarify the semantic identities and roles of each component,enhancing the model’s sensitivity and fusion capabilities for diverse information sources.Furthermore,TSMixerE constructs contextual unit sequences for entities,fully exploring neighborhood information within the graph structure to model complex semantic dependencies,thus improving the completeness and generalization of embedding representations. 展开更多
关键词 Knowledge graph knowledge graph complementation convolutional neural network feature interaction context
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From data to insight:Building a knowledge graph for risk analysis of hazardous chemical accidents 认领 引用
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作者 Kai Zhao Xilei Lu +7 位作者 Lining Wan Linlin Zhang Yulong Jin Pengtao Wen Jinhao Gao Miao He Qibo Wang Li Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第3期92-110,共19页
A substantial amount hazardous chemical accident(HCA)data have been accumulated in the form of unstructured textual data,making systematic analysis and utilization challenging.More importantly,manually identifying and... A substantial amount hazardous chemical accident(HCA)data have been accumulated in the form of unstructured textual data,making systematic analysis and utilization challenging.More importantly,manually identifying and analyzing key information from a considerable quantity of accident data is inefficient and highly susceptible to subjective bias.To efficiently unlock the value of HCA investigation reports and uncover underlying accident patterns,a semi-automated method for knowledge graph(KG)construction has been developed to model the HCA data.First,an ontology that accurately expresses key factors of HCAs is established.Second,an automated method is developed for the identification,standardization,and enhancement of accident factors,which combines deep learning(DL)and natural language processing(NLP)techniques.Specifically,the deep neural network model,named interaction region and type information(IRTI)is proposed to extract accident factors and their relationships from lengthy HCA data with complex overlapping issues.Non-standard accident factors are standardized using ChatGPT-4 in combination with the proposed text clustering model,named contrastive learningbased short text clustering(CLSTC).The processed accident factors are used to construct the hazardous chemical accident knowledge graph(HCAKG).Finally,the risk factors in the HCAKG are statistically analyzed,and their internal topological relationships are explored to facilitate quantitative analysis.Data from HCA investigation reports are used to demonstrate the effectiveness of this method.The result shows that it improves the accuracy and efficiency of accident data analysis,promoting better risk assessment and management strategies. 展开更多
关键词 Chemical processes Safety Neural networks Knowledge graph Hazardous chemical accident Risk analysis
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Distribution network data asset protection considering multiple topology and multi-dimensional knowledge graph 认领 引用
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作者 Junfeng Yang Li Liu +5 位作者 Nawaraj Kumar Mahato Luhan Li Jiaxuan Yang Gangjun Gong Jun Lu Chao Yang 《Global Energy Interconnection》 EI CSCD 2026年第3期585-610,共26页
The proliferation of diverse entities within distribution network has led to an increase in the scale and complexity of data asset interactions,exacerbating security risks,such as unauthorized access,data tampering,an... The proliferation of diverse entities within distribution network has led to an increase in the scale and complexity of data asset interactions,exacerbating security risks,such as unauthorized access,data tampering,and forgery.In response to these challenges,this study introduces a novel framework that enhances the protection of data assets.It incorporates a multi-dimensional knowledge graph(MDKG)to refine access control and overcome current limitations by integrating a comprehensive set of data asset attributes,roles,policies,and permissions.This approach fosters the development of a nuanced and adaptable access-control mechanism.Furthermore,the framework integrates multiple topology(MTP)for holistic security risk detection,leveraging attention mechanisms,and cross-fusion to adapt to the dynamic data security landscape.Empirical evaluations affirm the effectiveness of MDKG-based access control,whereas comparative experiments demonstrate the superiority of the MTP-based security risk model over existing models.The framework was proven to be effective in countering security risks.This study provides innovative perspectives on data asset protection and establishes a solid foundation for the advancement of smart grid technology. 展开更多
关键词 Distribution network Data asset protection Security access control Multi-dimensional knowledge graph Multiple topology Security risk detection
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KIG:A Knowledge Graph-Guided Iterative-Updating Graph Neural Network for Multisensor Time Series Time-Delay Estimation 认领 引用
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作者 Siyuan Xu Dong Pan +3 位作者 Zhaohui Jiang Zhiwen Chen Haoyang Yu Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期327-345,共19页
Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider... Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider the complex interdependencies between different sensors in MTS,and temporal alignment in many methods is typically treated as an isolated task disconnected from the downstream objectives,leading to unsatisfactory performances in follow-up applications.To address these challenges,this paper proposes a novel knowledge graph(KG)-guided iterative-updating graph neural network(GNN)for time-delay estimation(TDE)in MTS.Initially,a domain-specific KG is constructed from domain mechanism knowledge,providing a foundation for GNN's initialization.Next,capitalizing on the inherent structure of the graph topology,a GNN-based TDE method is developed.Then,a customized loss function is constructed,which synthesizes both the performances of downstream tasks and graph-based constraints.Moreover,an innovative algorithm for GNN structure learning and iterative-updating is proposed to renovate the graph structure further.Finally,experimental results across various regression and classification tasks on numerical simulation,public datasets,and the real blast furnace ironmaking dataset demonstrate that the proposed method can achieve accurate temporal alignment of MTS. 展开更多
关键词 Blast furnace ironmaking process graph neural network(GNN) knowledge graph(KG) multisensor time series(MTS) temporal alignment time-delay estimation(TDE)
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Q&A system for international construction contracts driven by large language model and knowledge graph 认领 引用
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作者 Yongcheng FU Tong LYU +1 位作者 Yongqiang CHEN Ziyun LV 《ENGINEERING Management》 CSCD 2026年第1期124-145,共22页
As international construction projects continue to expand,construction enterprises are accumulating vast amounts of contract‑related text data,making the effective management and extraction of knowledge from these den... As international construction projects continue to expand,construction enterprises are accumulating vast amounts of contract‑related text data,making the effective management and extraction of knowledge from these dense texts essential to mitigate knowledge loss and ensure efficient contract management.The advent of large language models(LLMs)presents a promising avenue for enhancing contract knowledge management through intelligent systems.However,challenges such as hallucination,inflexibility,and lack of interpretability often diminish practitioners’confidence in applying these models to real‑world scenarios.This study seeks to develop a knowledge‑based question‑and‑answer(Q&A)system for international construction contracts by integrating both the knowledge graph(KG)and the LLM.Built upon a domain‑specific KG derived from the 2022 edition of the Fédération Internationale des Ingénieurs‑Conseils(FIDIC)Yellow Book and the NEC4 Conditions of Contract,the system leverages LLM to conduct synergistic reasoning with the KG,enabling it to answer complex queries using both tacit knowledge and external sources.Experimental results demonstrate that the proposed approach markedly enhances the model’s performance in Q&A tasks of contract knowledge,achieving an average success rate exceeding 87%in terms of both accuracy and interpretability.This model provides a specialized Q&A system for international construction enterprises,facilitating flexible knowledge acquisition and task‑oriented analysis in contract management,while also introducing a novel framework for integrating AI technologies into the management of international construction contracts. 展开更多
关键词 international construction contract knowledge graph large language model knowledge management
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Few-Shot Knowledge Graph Completion with Structure-Aware Graph Attention Network 认领 引用
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作者 YANG Rongtai SHAO Yubin +2 位作者 DU Qingzhi ZHANG Feng QI Yuting 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期1024-1033,I0019,共10页
Few-shot knowledge graph completion refers to inferring missing entity using limited instances.A key challenge lies in entity representation,which is complicated by diverse neighbor attributes.Although the entity'... Few-shot knowledge graph completion refers to inferring missing entity using limited instances.A key challenge lies in entity representation,which is complicated by diverse neighbor attributes.Although the entity's neighborhood topology holds potential to address this,its significance is overlooked in current research.In this paper,we propose a structure-aware graph attention network for few-shot knowledge graph completion.Firstly,to enhance entity representations,we design a structure-aware graph attention encoder to capture the graph's structural features of nodes,generating embedding for entity pairs.Secondly,a semantic prototype matching network is employed to compute the prediction score.Experiments on the NELL-One and Wiki-One datasets show that our proposed model outperforms the best baseline models by 0.021,0.026,0.039,0.032 and 0.016,0.064,0.043,0.040 in terms of MRR,Hits@10,Hits@5,and Hits@1 metrics,respectively.This demonstrates that our model can effectively leverage neighborhood topological information to improve the accuracy of knowledge completion,and achieve a better generalization. 展开更多
关键词 knowledge graph completion neighborhood topology structure-aware graph attention entity representations semantic prototype
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Intelligent Teaching Scenarios Based on Knowledge Graphs and the Integration of“Teacher-Machine-Student” 认领 引用
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作者 Yanhang Zhang Xiaohong Su +1 位作者 Yu Zhang Tiantian Wang 《计算机教育》 2026年第3期81-88,共8页
This paper delves into effective pathways for transforming course ecosystems from resource provision to knowledge service and competency development through university-enterprise collaboration in co-building knowledge... This paper delves into effective pathways for transforming course ecosystems from resource provision to knowledge service and competency development through university-enterprise collaboration in co-building knowledge graphs and intelligent shared courses.This approach enables personalized,learning-driven teaching.Based on knowledge graphs and integrated teacher-machine-student smart teaching scenarios,it not only innovates autonomous learning environments and human-computer interaction models while optimizing teaching experiences for both instructors and students,but also effectively addresses the issues of students’“scattered,superficial,and fragmented learning”.This establishes the foundation for personalized teaching tailored to individual aptitudes. 展开更多
关键词 Knowledge graphs Teacher-machine-student Smart teaching
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Knowledge graph-enhanced framework for electric power engineering report generation 认领 引用
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作者 Chen Qian Yu-Yan Chen +3 位作者 Jia-Ying Yang Xiao-Wen Le Xiao-Yang Shen Yi-Heng Zeng 《Journal of Electronic Science and Technology》 EI CAS CSCD 2026年第1期46-64,共19页
Due to the complex structural hierarchy,with deeply nested associative relations between entities such as equipment,specifications,and business processes,intelligent power grid engineering is challenging.Meanwhile,lim... Due to the complex structural hierarchy,with deeply nested associative relations between entities such as equipment,specifications,and business processes,intelligent power grid engineering is challenging.Meanwhile,limited by the fragmented data and loss of contextual information,the generated reports are prone to the problems such as content redundancy and omission of critical information,failing to meet the demands of efficient decision-making and accurate management in modern power systems.To address these issues,this paper proposes a knowledge graph(KG)-enhanced framework to automatically generate electric power engineering reports.In the KG construction phase,a feature-fused entity recognition model named BERT-BiLSTM-CRF is adopted to improve the accuracy of entity recognition in scenarios involving power engineering professional terminology,thereby solving the problem of ambiguous entity boundaries in traditional models;then a BERT-attention relation extraction model is proposed to enhance the completeness of extracting complex hierarchical and implicit relations in power grid data.In the report generation phase,an improved Transformer architecture is adopted to accurately transform structured knowledge into natural language reports that comply with engineering specifications,addressing the issue of semantic inconsistency caused by the loss of structural information in existing models.By validating with real-world projects,the results show that the proposed framework significantly outperforms existing baseline models in entity recognition,confirming its superiority and applicability in practical engineering. 展开更多
关键词 Entity recognition Improved Transformer model Knowledge graph enhancement Power grid engineering report generation Relation extraction
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Knowledge Graph-Driven Training Data Construction for Urban Flood-Traffic Scenario Generation Using Small Language Models 认领 引用
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作者 Geunhwi Park Juneyoung Park +1 位作者 Chunjoo Yoon Jaehong Park 《Computers, Materials & Continua》 SCIE EI 2026年第8期2128-2151,共24页
Urban flooding caused by extreme rainfall events disrupts transportation systems,yet generating realistic flood-traffic scenarios for disaster preparedness remains a labor-intensive manual process.This study proposes ... Urban flooding caused by extreme rainfall events disrupts transportation systems,yet generating realistic flood-traffic scenarios for disaster preparedness remains a labor-intensive manual process.This study proposes a Knowledge Graph(KG)-driven pipeline that automatically generates domain-specific training data for fine-tuning small language models(sLLMs)to synthesize urban flood-traffic scenarios.A domain KG comprising 58 entities and 285 relationships was constructed for Jinju City,South Korea,integrating empirical flood data from 112 local documents with quantitative rainfall-traffic impact values from 14 international studies.Nine domain constraint rules,including a novel spatial consistency rule,ensure the physical plausibility of generated scenarios.Through constrained weighted graph walks,800 semi-structured English narrative scenarios were automatically generated in approximately 5 min,substantially reducing the labor required compared to manual creation.Three sLLMs spanning different architectures and parameter scales—Flan-T5-Large(770M),Qwen2.5-3B-Instruct(3B),and Qwen2.5-7B-Instruct(7B)—were fine-tuned using QLoRA on a single GPU with 16 GB VRAM.Evaluation on 78 test samples demonstrated consistent performance improvements with increasing model scale:Qwen2.5-7B achieved BLEU-4 of 0.5524,ROUGE-L of 0.6883,BERTScore F1 of 0.9662,and KG Fact Consistency of 1.0000,representing a 33.8%BLEU-4 improvement over Flan-T5-Large.Both Qwen models achieved KG Fact Consistency of 1.0000.The 3B model achieved 98.6%of the 7B model’s BLEU-4 at 53%of the VRAM cost with identical factual consistency,representing the most cost-effective configuration.All models were trained for 10 epochs on the same GPU,demonstrating practical feasibility for municipal disaster response deployment. 展开更多
关键词 Knowledge graph training data generation urban flood traffic scenario small language model fine-tuning text generation QLoRA
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Learning Time Embedding for Temporal Knowledge Graph Completion 认领 引用
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作者 Jinglu Chen Mengpan Chen +2 位作者 Wenhao Zhang Huihui Ren Daniel Dajun Zeng 《Computers, Materials & Continua》 SCIE EI 2026年第2期827-851,共25页
Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,transl... Temporal knowledge graph completion(TKGC),which merges temporal information into traditional static knowledge graph completion(SKGC),has garnered increasing attention recently.Among numerous emerging approaches,translation-based embedding models constitute a prominent approach in TKGC research.However,existing translation-based methods typically incorporate timestamps into entities or relations,rather than utilizing them independently.This practice fails to fully exploit the rich semantics inherent in temporal information,thereby weakening the expressive capability of models.To address this limitation,we propose embedding timestamps,like entities and relations,in one or more dedicated semantic spaces.After projecting all embeddings into a shared space,we use the relation-timestamp pair instead of the conventional relation embedding as the translation vector between head and tail entities.Our method elevates timestamps to the same representational significance as entities and relations.Based on this strategy,we introduce two novel translation-based embedding models:TE-TransR and TE-TransT.With the independent representation of timestamps,our method not only enhances capabilities in link prediction but also facilitates a relatively underexplored task,namely time prediction.To further bolster the precision and reliability of time prediction,we introduce a granular,time unit-based timestamp setting and a relation-specific evaluation protocol.Extensive experiments demonstrate that our models achieve strong performance on link prediction benchmarks,with TE-TransR outperforming existing baselines in the time prediction task. 展开更多
关键词 Temporal knowledge graph(TKG) TKG embedding model link prediction time prediction
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Dynamic Knowledge Graph Reasoning Based on Distributed Representation Learning 认领 引用
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作者 Qiuru Fu Shumao Zhang +4 位作者 Shuang Zhou Jie Xu Changming Zhao Shanchao Li Du Xu 《Computers, Materials & Continua》 SCIE EI 2026年第2期1542-1560,共19页
Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowled... Knowledge graphs often suffer from sparsity and incompleteness.Knowledge graph reasoning is an effective way to address these issues.Unlike static knowledge graph reasoning,which is invariant over time,dynamic knowledge graph reasoning is more challenging due to its temporal nature.In essence,within each time step in a dynamic knowledge graph,there exists structural dependencies among entities and relations,whereas between adjacent time steps,there exists temporal continuity.Based on these structural and temporal characteristics,we propose a model named“DKGR-DR”to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs.In addition,we construct a static attribute graph to represent entities’inherent properties.DKGR-DR is capable of modeling both dynamic and static aspects of entities,enabling effective entity prediction and relation prediction.We conduct experiments on ICEWS05-15,ICEWS18,and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance. 展开更多
关键词 Dynamic knowledge graph reasoning recurrent neural network graph convolutional network graph attention mechanism
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A knowledge modeling method for high-speed railway emergency faults based on structured logic diagrams and knowledge graphs 认领 引用
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作者 Senshen Li Chun Zhang +5 位作者 Guoyuan Yang Wei Bai Shaoxiong Pang Xiaoshu Wang Jian Yao Ning Zhang 《High-Speed Railway》 CSCD 2026年第1期59-67,共9页
Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelli... Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain. 展开更多
关键词 Fault emergency handling Knowledge graph Intelligent O&M
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Defect Identification Method of Power Grid Secondary Equipment Based on Coordination of Knowledge Graph and Bayesian Network Fusion 认领 引用
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作者 Jun Xiong Peng Yang +1 位作者 Bohan Chen Zeming Chen 《Energy Engineering》 EI 2026年第1期296-313,共18页
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo... The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency. 展开更多
关键词 Knowledge graph Bayesian network secondary equipment defect identification
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Knowledge graph-enhanced long-tail learning approach for traditional Chinese medicine syndrome differentiation 认领 引用
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作者 Weikang Kong Chuanbiao Wen Yue Luo 《Digital Chinese Medicine》 CAS CSCD 2026年第1期57-67,共11页
Objective To address the dual challenges of long-tail distribution and feature sparsity in traditional Chinese medicine(TCM)syndrome differentiation within real clinical settings,we propose a data-efficient learning f... Objective To address the dual challenges of long-tail distribution and feature sparsity in traditional Chinese medicine(TCM)syndrome differentiation within real clinical settings,we propose a data-efficient learning framework enhanced by knowledge graphs.Methods We developed Agent-GNN,a three-stage decoupled learning framework,and validated it on the Traditional Chinese Medicine Syndrome Diagnosis(TCM-SD)dataset containing 54152 clinical records across 148 syndrome categories.First,we constructed a comprehensive medical knowledge graph encoding the complete TCM reasoning system.Second,we proposed a Functional Patient Profiling(FPP)method that utilizes large language models(LLMs)combined with Graph Retrieval-Augmented Generation(RAG)to extract structured symptom-etiology-pathogenesis subgraphs from medical records.Third,we employed heterogeneous graph neural networks to learn structured combination patterns explicitly.We compared our method against multiple baselines including BERT,ZY-BERT,ZY-BERT+Know,GAT,and GPT-4 Few-shot,using macro-F1 score as the primary evaluation metric.Additionally,ablation experiments were conducted to validate the contribution of each key component to model performance.Results Agent-GNN achieved an overall macro-F1 score of 72.4%,representing an 8.7 percentage points improvement over ZY-BERT+Know(63.7%),the strongest baseline among traditional methods.For long-tail syndromes with fewer than 10 samples,Agent-GNN reached a macro-F1 score of 58.6%,compared with 39.3%for ZY-BERT+Know and 41.2%for GPT-4 Few-shot,representing relative improvements of 49.2%and 42.2%,respectively.Ablation experiments confirmed that the explicit modeling of etiology-pathogenesis nodes contributed 12.4 percentage points to this enhanced long-tail syndrome performance.Conclusion This study proposes Agent-GNN,a knowledge graph-enhanced framework that effectively addresses the long-tail distribution challenge in TCM syndrome differentiation.By explicitly modeling manifestation-mechanism-essence patterns through structured knowledge graphs,our approach achieves superior performance in data-scarce scenarios while providing interpretable reasoning paths for TCM intelligent diagnosis. 展开更多
关键词 Syndrome differentiation Medical knowledge graph Graph neural networks Long-tail learning Data-efficient learning
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Automatic Detection of Health-Related Rumors: A Dual-Graph Collaborative Reasoning Framework Based on Causal Logic and Knowledge Graph 认领 引用
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作者 Ning Wang Haoran Lyu Yuchen Fu 《Computers, Materials & Continua》 SCIE EI 2026年第1期2163-2193,共31页
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p... With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media. 展开更多
关键词 Health rumor detection causal graph knowledge graph dual-graph fusion
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Boosting AI Tutoring in Software Engineering with Knowledge Graph Guided Reasoning 认领 引用
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作者 Quanshun Yang Xudong Lu +5 位作者 Xuran Tang Wei Guo Lizhen Cui Lanju Kong Lei Liu Peng Pan 《计算机教育》 2026年第3期167-175,共9页
Large language models(LLMs)show great potential in educational scenarios but face challenges like hallucination,knowledge gaps,and reasoning discontinuities.This study proposes a dynamic knowledge enhancement framewor... Large language models(LLMs)show great potential in educational scenarios but face challenges like hallucination,knowledge gaps,and reasoning discontinuities.This study proposes a dynamic knowledge enhancement framework.By integrating local knowledge graphs and stepwise prompting mechanisms,it improves LLMs’accuracy and interpretability in solving professional domain problems.The framework has two core modules:an LLM-driven knowledge graph construction system for incremental updates and a unified reasoning engine for generating enhanced prompts.Experiments on 680 educational questions show that the method boosts accuracy by 4.5%and 4.3%for multi-step reasoning and knowledge-dependent questions respectively,and increases reasoning step completeness from 68.2%to 83.7%.It also reduces hallucination problems.Key contributions include the followings:①validation of an effective framework synergizing knowledge graphs with retrieval mechanisms to enhance LLM reliability;②a stepwise prompting strategy enforcing explicit reasoning chain generation,addressing pedagogical requirements for process interpretability;③a lightweight deployment solution for educational systems such as adaptive learning platforms. 展开更多
关键词 Educational question answering Educational LLMs Knowledge graphs Stepwise prompting
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Research on the Construction of a software engineering course knowledge graph empowered by the Xhang AI teaching assistant 认领 引用
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作者 Xuefei Huang Yiming Gai +3 位作者 Ying Li Hao Sheng Da Yang Yingying Zhang 《计算机教育》 2026年第6期221-228,共8页
In view of the needs and challenges of digital transformation in the field of education under the background ofªnew engineeringº,this paper analyzes how to improve the teaching efficiency of software enginee... In view of the needs and challenges of digital transformation in the field of education under the background ofªnew engineeringº,this paper analyzes how to improve the teaching efficiency of software engineering course programming and realize personalized guidance through the large language models,and proposes to use the multi-channel feedback optimization technology of Xhang AI assistant to extract and construct the multimodal knowledge graph of the adaptive course,and realize the personalized fine-tuning of the model combined with the private data collaborative sharing platform,so as to enhance the accuracy and applicability of the knowledge graph of software engineering course.The automatic construction and updating of curriculum knowledge graph based onªXhang AI assistantºcan cover a wider range of educational courses and fields,and promote the development of intelligent and digital education. 展开更多
关键词 Xhang AI assistant Large language model Multimodal knowledge graph Digital strategy Software engineering
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Exploratory Research on Knowledge Graph Combined with Artificial Intelligence Teaching Assistant in the Fundamentals of Nursing Course 认领 引用
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作者 Jianwei Hu Lijuan Wang +1 位作者 Li Yang Yu Cai 《Journal of Contemporary Educational Research》 2026年第3期72-77,共6页
Higher education is undergoing digital construction,and traditional education can no longer meet the training needs of nursing talents in the new era.The teaching of Fundamentals of Nursing faces several dilemmas,incl... Higher education is undergoing digital construction,and traditional education can no longer meet the training needs of nursing talents in the new era.The teaching of Fundamentals of Nursing faces several dilemmas,including cumbersome course content and scattered knowledge points.With the iterative update of modern digital technologies,the knowledge graph-structured,semantic,scalable,and multi-source integrated-provides a technical path to solve these problems.This study proposes a planned teaching application scheme based on the Xuexitong platform,which integrates the knowledge graph,artificial intelligence teaching assistant,and task engine.This scheme not only provides practical reference for the teaching innovation of Fundamentals of Nursing but also offers new ideas for the in-depth integration of educational technology and nursing teaching. 展开更多
关键词 Knowledge graph Artificial intelligence Teaching assistant Fundamentals of Nursing Undergraduate nursing education
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An Analysis of China-ASEAN Publishing Exchange Research from the Perspective of a Community with a Shared Future for Mankind:A Visual Analysis Based on Knowledge Graphs 认领 引用
19
作者 Yi Li Qiang Bao 《Journal of Contemporary Educational Research》 2026年第3期161-176,共16页
Based on 128 journal articles from the CNKI database spanning 2006 to 2026,this study employs the CiteSpace knowledge graph method to conduct a visual analysis of the knowledge structure,evolutionary trends,and fronti... Based on 128 journal articles from the CNKI database spanning 2006 to 2026,this study employs the CiteSpace knowledge graph method to conduct a visual analysis of the knowledge structure,evolutionary trends,and frontier issues in research on China-ASEAN publishing exchanges from the perspective of a Community with a Shared Future for Mankind.The findings reveal that the scientific research collaboration network in this field exhibits low density,characterized by“decentralization and independence.”Research themes have evolved from“policy-driven”to“efficacy-oriented,”forming a keyword co-occurrence network with“ASEAN”as the core hub and key nodes such as“the Belt and Road,”“Going Global,”“theme publishing,”and“Guangxi.”As a geographical bridge and a typical case connecting China and ASEAN,“Guangxi”holds significant practical importance.Cluster analysis identifies four major frontier directions,revealing a paradigm shift in research from macro-level initiative interpretation to meso-level mechanism construction and micro-level communication efficacy inquiry.Future research should promote the“institution-industry-culture-technology”four-dimensional interdisciplinary integration,strengthen field investigations and localized reception analysis targeting ASEAN audiences,and prudently address ethical challenges arising from AI empowerment,in order to deepen the value co-creation function of publishing exchanges in building the China-ASEAN Community with a Shared Future. 展开更多
关键词 Community with a Shared Future for Mankind China-ASEAN Publishing field Exchange Knowledge graph
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Interdisciplinary Arts and Sciences Education:An Exploration on Knowledge Graph-aided I Ching Learning 认领 引用
20
作者 Yinuo Liu Wendong Li +3 位作者 Wenxuan Ouyang Gaojie Wang Li Pan Shijun Liu 《计算机教育》 2026年第3期130-138,共9页
To integrate traditional culture and modern technology,Shandong University’s School of Software has promoted an interdisciplinary teaching project called IYAN&ITAN,the I Ching Knowledge Graph.The project,driven b... To integrate traditional culture and modern technology,Shandong University’s School of Software has promoted an interdisciplinary teaching project called IYAN&ITAN,the I Ching Knowledge Graph.The project,driven by I Ching texts,guides students to practice natural language processing(NLP)and knowledge graph technology in a task-oriented curriculum,based on constructivism,situated learning,and inquiry-based pedagogy,with a progressive and task-oriented teaching model.The platform established enables the retrieval of knowledge,parsing of text,symbolic-numeric analysis,and historical commentary integration,making possible multidimensional,structured representation of I Ching knowledge,and offering an extensible reference for interdisciplinary learning in the context of New Engineering Education. 展开更多
关键词 Interdisciplinary teaching Knowledge graph Constructivism Situated learning Inquiry-based learning
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