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IndRT-GCNets: knowledge reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Computers, Materials & Continua 2024年第1期78卷 695-712页

作者： Yajing Ma Gulila Altenbek Yingxia Yu College of Information Science and Engineering Xinjiang UniversityUrumqi830017China Xinjiang Laboratory of Multilanguage Information Technology Xinjiang UniversityUrumqi830017China The Base of Kazakh and Kirghiz Language of National Language Resource Monitoring and Research Center on Minority Languages Xinjiang UniversityUrumqi830017China

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute *** framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each ***,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each ***,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive ***,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity ***,the evolution of entity and relation representations is utilized to predict events in the next subsequent *** multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

关键词： knowledge reasoning entity and relation representation structural dependency relationship evolutionary representation temporal graph convolution

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GATiT:An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in knowledge reasoning

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Computers, Materials & Continua 2024年第9期80卷 4767-4790页

作者： Yu Song Pengcheng Wu Dongming Dai Mingyu Gui Kunli Zhang School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450001China

The growing prevalence of knowledge reasoning using knowledge graphs(KGs)has substantially improved the accuracy and efficiency of intelligent medical ***,current models primarily integrate electronic medical records(EMRs)and KGs into the knowledge reasoning process,ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the *** better integrate EMR text information,we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning(GATiT),which comprises text representation,subgraph construction,knowledge reasoning,and diagnostic *** the text representation process,GATiT uses a pre-trained model to obtain text representations of the EMRs and additionally enhances embeddings by including chief complaint information and numerical information in the *** the subgraph construction process,GATiT constructs text subgraphs and disease subgraphs from the KG,utilizing EMR text and the disease to be *** differentiate the varying importance of nodes within the subgraphs features such as node categories,relevance scores,and other relevant factors are introduced into the text ***-passing strategy and attention weight calculation of the graph attention network are adjusted to learn these features in the knowledge reasoning ***,in the diagnostic classification process,the interactive attention-based fusion method integrates the results of knowledge reasoning with text representations to produce the final diagnosis *** results on multi-label and single-label EMR datasets demonstrate the model’s superiority over several state-of-theart methods.

关键词： Intelligent diagnosis knowledge graph graph attention network knowledge reasoning

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GNN-based temporal knowledge reasoning for UAV mission planning systems

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The Journal of China Universities of Posts and Telecommunications 2024年第1期31卷 12-25页

作者： Chai Rong Duan Xiaofang Wang Lixuan School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

Unmanned aerial vehicles(UAVs)are increasingly applied in various mission scenarios for their versatility,scalability and *** UAV mission planning systems(UMPSs),an efficient mission planning strategy is essential to meet the requirements of UAV ***,rapidly changing environments and unforeseen threats pose challenges to UMPSs,making efficient mission planning *** address these challenges,knowledge graph technology can be utilized to manage the complex relations and constraints among UAVs,missions,and *** paper investigates knowledge graph application in UMPSs,exploring its modeling,representation,and storage concepts and ***,the construction of a specialized knowledge graph for UMPS is ***,the paper delves into knowledge reasoning within UMPSs,emphasizing its significance in timely updates in the dynamic environment.A graph neural network(GNN)-based approach is proposed for knowledge reasoning,leveraging GNNs to capture structural information and accurately predict missing entities or relations in the knowledge *** relation reasoning,path information is also incorporated to improve the accuracy of *** account for the temporal dynamics of the environment in UMPS,the influence of timestamps is captured through the attention *** effectiveness and applicability of the proposed knowledge reasoning method are verified via simulations.

关键词： unmanned aerial vehicle mission planning knowledge graph knowledge reasoning graph neural network

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Research on knowledge reasoning of TCM based on knowledge graphs

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Digital Chinese Medicine 2022年第4期5卷 386-393页

作者： GUO Zhiheng LIU Qingping ZOU Beiji School of Informatics Hunan University of Chinese MedicineChangshaHunan 410208China School of Computer Science and Engineering Central South UniversityChangshaHunan 410083China

With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing ***,there is much attention on the collection of useful knowledge as well as its effective organization and *** graphs have thus emerged,and knowledge reasoning based on this tool has become one of the hot spots of *** paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning,and explores the significance of knowledge ***,the mainstream knowledge reasoning methods,including knowledge reasoning based on traditional rules,knowledge reasoning based on distributed feature representation,and knowledge reasoning based on neural networks are ***,using stroke as an example,the knowledge reasoning methods are expounded,the principles and characteristics of commonly used knowledge reasoning methods are summarized,and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted ***,we summarize the problems faced in the development of knowledge reasoning in TCM,and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.

关键词： Traditional Chinese medicine(TCM) Stroke knowledge graph knowledge reasoning Assisted decision-making Transloction Embedding(TransE)model

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Medical knowledge Graph:Data Sources,Construction,reasoning,and Applications

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Big Data Mining and Analytics 2023年第2期6卷 201-217页

作者： Xuehong Wu Junwen Duan Yi Pan Min Li Computer Science and Engineering Central South UniversityChangsha 410083China School of Computer Science Hunan First Normal UniversityChangsha 410006China School of Computer Science and Engineering Central South UniversityChangsha 410083China Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical ***,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical *** this end,we offer an in-depth review of MKG in this *** research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG ***,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for *** addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major ***,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

关键词： medical knowledge graph knowledge graph construction knowledge reasoning intelligent medical applications intelligent healthcare

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New knowledge Network Evaluation Method for Design Rationale Management

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Chinese Journal of Mechanical Engineering 2015年第1期28卷 173-186页

作者： JING Shikai ZHAN Hongfei LIU Jihong WANG Kuan JIANG Hao ZHOU Jingtao School of Mechanical Engineering Beijing Institute of Technology Shanghai Aircraft Design and Research Institute School of Mechanical Engineering and Automation Beihang University School of Mechanical Engineering Northwestern Polytechnical University

Current design rationale （DR） systems have not demonstrated the value of the approach in practice since little attention is put to the evaluation method of DR knowledge. To systematize knowledge management process for future computer-aided DR applications, a prerequisite is to provide the measure for the DR knowledge. In this paper, a new knowledge network evaluation method for DR management is presented. The method characterizes the DR knowledge value from four perspectives, namely, the design rationale structure scale, association knowledge and reasoning ability, degree of design justification support and degree of knowledge representation conciseness. The DR knowledge comprehensive value is also measured by the proposed method. To validate the proposed method, different style of DR knowledge network and the performance of the proposed measure are discussed. The evaluation method has been applied in two realistic design cases and compared with the structural measures. The research proposes the DR knowledge evaluation method which can provide object metric and selection basis for the DR knowledge reuse during the product design process. In addition, the method is proved to be more effective guidance and support for the application and management of DR knowledge.

关键词： design rationale knowledge reasoning justification support decision support knowledge network evaluation

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New approach to eliminate structural redundancy in case resource pools using α mutual information

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Journal of Systems Engineering and Electronics 2013年第4期24卷 625-633页

作者： Man Xu Haiyan Yu Jiang Shen Department of Industrial Engineering Nankai University College of Management and Economics Tianjin University

Structural redundancy elimination in case resource pools （CRP） is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this paper proposes a novel approach to ensure redundancy elimination of a reasoning system in CRP. By using α entropy and mutual information, functional measures to eliminate redundancy of a system are developed with respect to a set of outputs. These measures help to distinguish both the optimal feature and the relations among the nodes in reasoning networks from the redundant ones with the elimination criterion. Based on the optimal feature and its harmonic weight, a model for knowledge reasoning in CRP （CRPKR） is built to complete the task of query matching, and the missing values are estimated with Bayesian networks. Moreover, the robustness of decisions is verified through parameter analyses. This approach is validated by the simulation with benchmark data sets using cloud SQL. Compared with several state-of-the-art techniques, the results show that the proposed approach has a good performance and boosts the robustness of decisions.

关键词： case resource pool (CRP) knowledge reasoning redundancy elimination α mutual information robust decision.

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A Survey of knowledge Graph Construction Using Machine Learning

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 225-257页

作者： Zhigang Zhao Xiong Luo Maojian Chen Ling Ma School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Shunde Innovation School University of Science and Technology BeijingFoshan528399China Beijing Key Laboratory of knowledge Engineering for Materials Science Beijing100083China

knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured *** framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity *** this transformative process,the advancement of artificial intelligence and intelligent information services is ***,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial *** article embarks on a comprehensive journey through the last strides in the field of KG via machine *** a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge ***,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link ***,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

关键词： knowledge graph(KG) semantic network relation extraction entity linking knowledge reasoning

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Design of Multi-attribute knowledge Base Based on Hybrid knowledge Representation

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Journal of Donghua University(English Edition) 2006年第6期23卷 62-66页

作者：唐志杰杨保安张科静 Glorious Sun School of Business and Management Donghua University

Based on the knowledge representation and knowledge reasoning, this paper addresses the creation of the multi-attribute knowledge base on the basis of hybrid knowledge representation, with the help of object-oriented programming language and relational database. Compared with general knowledge base, multi-attribute knowledge base can enhance the ability of knowledge processing and application; integrate the heterogeneous knowledge, such as model, symbol, case-based sample knowledge; and support the whole decision process by integrated reasoning.

关键词： Hybrid knowledge multi-attribute knowledge base knowledge representation knowledge reasoning object-oriented method.

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Quant 4.0:engineering quantitative investmentwith automated,explainable,and knowledge-driven artificial intelligence

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Frontiers of Information Technology & Electronic Engineering 2024年第11期25卷 1421-1445页

作者： Jian GUO Saizhuo WANG Lionel M.NI Heung-Yeung SHUM IDEA Research International Digital Economy AcademyShenzhen 518045China The Hong Kong University of Science and Technology Hong Kong 999077China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511453China

Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,*** has become one of the mainstream investment methodologies over the past decades,and has experienced three generations:quant 1.0,trading by mathematical modeling to discover mis-priced assets in markets;quant 2.0,shifting the quant research pipeline from small“strategy workshops”to large“alpha factories”;quant 3.0,applying deep learning techniques to discover complex nonlinear pricing *** its advantage in prediction,deep learning relies on extremely large data volume and labor-intensive tuning of“black-box”neural network *** address these limitations,in this paper,we introduce quant 4.0 and provide an engineering perspective for next-generation *** 4.0 has three key differentiating ***,automated artificial intelligence(AI)changes the quant pipeline from traditional hand-crafted modeling to state-of-the-art automated modeling and employs the philosophy of“algorithm produces algorithm,model builds model,and eventually AI creates AI.”Second,explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black boxes,and explains complicated and hidden risk ***,knowledge-driven AI supplements data-driven AI such as deep learning and incorporates prior knowledge into modeling to improve investment decisions,in particular for quantitative value *** all these together,we discuss how to build a system that practices the quant 4.0 *** also discuss the application of large language models in quantitative ***,we propose 10 challenging research problems for quant technology,and discuss potential solutions,research directions,and future trends.

关键词： Artificial general intelligence Artificial intelligence Automated machine learning Causality engineering Deep learning Feature engineering Investment engineering knowledge graph knowledge reasoning knowledge representation Model compression Neural architecture search Quant 4.0 Quantitative investment Risk graph Explainable artificial intelligence

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