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IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations
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作者 Yajing Ma Gulila Altenbek Yingxia Yu 《Computers, Materials & Continua》 SCIE EI 2024年第1期695-712,共18页
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 Recurr... 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 information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,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 period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On 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. 展开更多
关键词 knowledge reasoning entity and relation representation structural dependency relationship evolutionary representation temporal graph convolution
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Multi-modal knowledge graph inference via media convergence and logic rule
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作者 Feng Lin Dongmei Li +5 位作者 Wenbin Zhang Dongsheng Shi Yuanzhou Jiao Qianzhong Chen Yiying Lin Wentao Zhu 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第1期211-221,共11页
Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the intro... Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features. 展开更多
关键词 logic rule media convergence multi-modal knowledge graph inference representation learning
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Knowledge Organization and Representation under the AI Lens 被引量:3
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作者 Jian Qin 《Journal of Data and Information Science》 CSCD 2020年第1期3-17,共15页
Purpose:This paper compares the paradigmatic differences between knowledge organization(KO)in library and information science and knowledge representation(KR)in AI to show the convergence in KO and KR methods and appl... Purpose:This paper compares the paradigmatic differences between knowledge organization(KO)in library and information science and knowledge representation(KR)in AI to show the convergence in KO and KR methods and applications.Methodology:The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper.Findings:A key difference between KO and KR lays in the purpose of KO is to organize knowledge into certain structure for standardizing and/or normalizing the vocabulary of concepts and relations,while KR is problem-solving oriented.Differences between KO and KR are discussed based on the goal,methods,and functions.Research limitations:This is only a preliminary research with a case study as proof of concept.Practical implications:The paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO.Originality/value:Ontologies and linked data as the evidence of the convergence of KO and KR paradigms provide theoretical and methodological support to innovate KO in the AI era. 展开更多
关键词 knowledge representation knowledge organization Artificial Intelligence Paradigms
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Collective Intelligence and Uncertain Knowledge Representation in Cloud Computing 被引量:1
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作者 刘玉超 张海粟 +2 位作者 马于涛 李德毅 陈桂生 《China Communications》 SCIE CSCD 2011年第6期58-66,共9页
The lasting evolution of computing environment, software engineering and interaction methods leads to cloud computing. Cloud computing changes the configuration mode of resources on the Internet and all kinds of resou... The lasting evolution of computing environment, software engineering and interaction methods leads to cloud computing. Cloud computing changes the configuration mode of resources on the Internet and all kinds of resources are virtualized and provided as services. Mass participation and online interaction with social annotations become usual in human daily life. People who own similar interests on the Internet may cluster naturally into scalable and boundless communities and collective intelligence will emerge. Human is taken as an intelligent computing factor, and uncertainty becomes a basic property in cloud computing. Virtualization, soft computing and granular computing will become essential features of cloud computing. Compared with the engineering technological problems of IaaS (Infrastructure as a service), PaaS (Platform as a Service) and SaaS (Software as a Service), collective intelligence and uncertain knowledge representation will be more important frontiers in cloud computing for researchers within the community of intelligence science. 展开更多
关键词 collective intelligence social annotation common sense uncertain knowledge representation
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Design of Multi-attribute Knowledge Base Based on Hybrid Knowledge Representation 被引量:1
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作者 唐志杰 杨保安 张科静 《Journal of Donghua University(English Edition)》 EI CAS 2006年第6期62-66,共5页
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 ... 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. 展开更多
关键词 Hybrid knowledge multi-attribute knowledge base knowledge representation knowledge reasoning object-oriented method.
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Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval 被引量:1
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作者 Anitha Velu Menakadevi Thangavelu 《Computers, Materials & Continua》 SCIE EI 2022年第3期4707-4724,共18页
The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information... The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR. 展开更多
关键词 Heterogeneous climatic data information retrieval semantic web sensor observation services knowledge representation ONTOLOGY
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Knowledge Graph Representation Reasoning for Recommendation System 被引量:2
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作者 Tao Li Hao Li +4 位作者 Sheng Zhong Yan Kang Yachuan Zhang Rongjing Bu Yang Hu 《Journal of New Media》 2020年第1期21-30,共10页
In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS ... In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS model.KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm,then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning,then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning.Finally,through the full connection layer and sigmoid function to get the prediction ratings,and the items are sorted according to the prediction ratings to get the user’s recommendation list.KGRS is tested on the movielens-100k dataset.Compared with the related representative algorithm,including the state-of-the-art interpretable recommendation models RKGE and RippleNet,the experimental results show that KGRS has good recommendation interpretation and higher recommendation accuracy. 展开更多
关键词 knowledge graph collaborative filtering deep learning interpretable recommendation knowledge representation learning
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A New Method of Semantic Network Knowledge Representation Based on Extended Petri Net 被引量:1
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作者 Ru Qi Zhou 《Computer Technology and Application》 2013年第5期245-253,共9页
Abstract: It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic netw... Abstract: It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic network knowledge representation processing. A new extended Petri net is defined based on qualitative mapping, which strengths the expressive ability of the feature of thinking and the mode of action of brain. A model of semantic network knowledge representation based on new Petri net is given. Semantic network knowledge has a more efficient representation and reasoning mechanism. This model not only can reflect the characteristics of associative memory in semantic network knowledge representation, but also can use Petri net to express the criterion changes and its change law of recognition judgment, especially the cognitive operation of thinking based on extraction and integration of sensory characteristics to well express the thinking transition course from quantitative change to qualitative change of human cognition. 展开更多
关键词 Semantic network Petri net knowledge representation qualitative mapping.
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Bayesian Diagnostic Network: A Powerful Model for Representation and Reasoning of Engineering Diagnostic Knowledge 被引量:1
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作者 HUZhao-yong 《International Journal of Plant Engineering and Management》 2005年第1期28-35,共8页
Engineering diagnosis is essential to the operation of industrial equipment.The key to successful diagnosis is correct knowledge representation and reasoning. The Bayesiannetwork is a powerful tool for it. This paper ... Engineering diagnosis is essential to the operation of industrial equipment.The key to successful diagnosis is correct knowledge representation and reasoning. The Bayesiannetwork is a powerful tool for it. This paper utilizes the Bayesian network to represent and reasondiagnostic knowledge, named Bayesian diagnostic network. It provides a three-layer topologicstructure based on operating conditions, possible faults and corresponding symptoms. The paper alsodiscusses an approximate stochastic sampling algorithm. Then a practical Bayesian network for gasturbine diagnosis is constructed on a platform developed under a Visual C++ environment. It showsthat the Bayesian network is a powerful model for representation and reasoning of diagnosticknowledge. The three-layer structure and the approximate algorithm are effective also. 展开更多
关键词 engineering diagnosis bayesian network REASONING knowledge representation
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Product Knowledge Representation and Integration Technology in Web-based Collaborative Design
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作者 HAO Wentao,TIAN Ling,LUO Wei,TONG Bingshu (Department of Precision Instruments and Mechanology,Tsinghua University,Beijing 100084,China) 《武汉理工大学学报》 CAS CSCD 北大核心 2006年第S1期220-227,共8页
Because of the complexity of modern product design,the web-based collaborative product design aroused considerable attention of manufacturers in the last few years with the development of Internet technology. But it i... Because of the complexity of modern product design,the web-based collaborative product design aroused considerable attention of manufacturers in the last few years with the development of Internet technology. But it is still hardly achievable due to the difficulty to share product knowledge from different designers and systems. In this paper,we firstly create an ontology-based product model,which consists of PPR (Product,Process and Resource) concept models and PPR characteristic models,to describe product knowledge. Afterwards,how to represent the model in XML is discussed in detail. Then the mechanism of product knowledge collection and integration from different application systems based on interface agents is introduced. At last,a web-based open-architecture product knowledge integrating and sharing prototype system AD-HUB is developed. An example is also given and it shows that the theory discussed in this paper is efficient to represent and integrate product knowledge in web-based collaborative design processes. 展开更多
关键词 COLLABORATIVE design knowledge representation ONTOLOGY interface agent
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A Semantic Model Faced on the Uniform Product Knowledge Representation
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作者 JIAN Chengfeng ZHANG Meiyu Software College,Zhejiang University of Technology,Hangzhou 310014,China, 《武汉理工大学学报》 CAS CSCD 北大核心 2006年第S3期1031-1035,共5页
In order to realize the uniform knowledge representation including STEP and SGML,aimed at the defects of cur- rent methods,a new semantic model that is named XOEM+OWL is put forward.And then the correspondent mapping ... In order to realize the uniform knowledge representation including STEP and SGML,aimed at the defects of cur- rent methods,a new semantic model that is named XOEM+OWL is put forward.And then the correspondent mapping between STEP Schema Graph and OWL Schema Graph are build as Cos(sc,oc),so we can get the semantic pattern matching degree for the semantic representation on the product information.At last the example is presented. 展开更多
关键词 virtual ORGANIZATION knowledge representation STEP XOEM+OWL SCHEMA graph
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Method of Dynamic Knowledge Representation and Learning Based on Fuzzy Petri Nets
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作者 危胜军 胡昌振 孙明谦 《Journal of Beijing Institute of Technology》 EI CAS 2008年第1期41-45,共5页
A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The... A method of knowledge representation and learning based on fuzzy Petri nets was designed. In this way the parameters of weights, threshold value and certainty factor in knowledge model can be adjusted dynamically. The advantages of knowledge representation based on production rules and neural networks were integrated into this method. Just as production knowledge representation, this method has clear structure and specific parameters meaning. In addition, it has learning and parallel reasoning ability as neural networks knowledge representation does. The result of simulation shows that the learning algorithm can converge, and the parameters of weights, threshold value and certainty factor can reach the ideal level after training. 展开更多
关键词 knowledge representation knowledge learning fuzzy Petri nets fuzzy reasoning
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Expert system for earthquake prediction(ESEP)(2)-Knowledge representation and evidence combination
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作者 范洪顺 王碧泉 《Earthquake Science》 CSCD 1994年第S1期23-29,共7页
This paper deals with knowledge representation of ESEP (Expert System for Earthqauke Prediction). Attending the characteristics of the knowledge in earthquake prediction domain, production representation and procedura... This paper deals with knowledge representation of ESEP (Expert System for Earthqauke Prediction). Attending the characteristics of the knowledge in earthquake prediction domain, production representation and procedural representation are connected in the knowledge repesentation model of ESEP named ESEP/K, and three new ways of evidence conbination are proposed for production rules besides 'AND' and 'OR'. 展开更多
关键词 expert system ESEP knowledge representation evidence combination earthquake prediction
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Knowledge Representation in Patient Safety Reporting: An Ontological Approach
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作者 Liang Chen Yang Gong 《Journal of Data and Information Science》 2016年第2期75-91,共17页
Purpose: The current development of patient safety reporting systems is criticized for loss of information and low data quality due to the lack of a uniformed domain knowledge base and text processing functionality. ... Purpose: The current development of patient safety reporting systems is criticized for loss of information and low data quality due to the lack of a uniformed domain knowledge base and text processing functionality. To improve patient safety reporting, the present paper suggests an ontological representation of patient safety knowledge. Design/methodology/approach: We propose a framework for constructing an ontological knowledge base of patient safety. The present paper describes our design, implementation,and evaluation of the ontology at its initial stage. Findings: We describe the design and initial outcomes of the ontology implementation. The evaluation results demonstrate the clinical validity of the ontology by a self-developed survey measurement. Research limitations: The proposed ontology was developed and evaluated using a small number of information sources. Presently, US data are used, but they are not essential for the ultimate structure of the ontology.Practical implications: The goal of improving patient safety can be aided through investigating patient safety reports and providing actionable knowledge to clinical practitioners.As such, constructing a domain specific ontology for patient safety reports serves as a cornerstone in information collection and text mining methods.Originality/value: The use of ontologies provides abstracted representation of semantic information and enables a wealth of applications in a reporting system. Therefore, constructing such a knowledge base is recognized as a high priority in health care. 展开更多
关键词 Patient safety Medical error knowledge representation Health information technology ONTOLOGY
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Knowledge Representation and Fuzzy Reasoning of an Agricultural Expert System
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作者 吴顺祥 倪子伟 李茂青 《Journal of Southwest Jiaotong University(English Edition)》 2002年第2期185-193,共9页
The design scheme of an agricultural expert system based on longan and cauliflower planting techniques is presented. Using an object-oriented design and a combination of the techniques in multimedia, database, expert ... The design scheme of an agricultural expert system based on longan and cauliflower planting techniques is presented. Using an object-oriented design and a combination of the techniques in multimedia, database, expert system and artificial intelligence, an in-depth analysis and summary are made of the knowledge features of die agricultural multimedia expert system and data models involved. According to the practical problems in agricultural field, the architectures and functions of the system are designed, and some design ideas about the hybrid knowledge representation and fuzzy reasoning are proposed. 展开更多
关键词 agricultural expert system knowledge representation fuzzy reasoning
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Knowledge representation and rule-based solution system for dynamic programming model
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作者 胡祥培 王旭茵 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2003年第2期190-194,共5页
A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block mode... A knowledge representation has been proposed using the state space theory of Artificial Intelligence for Dynamic Programming Model, in which a model can be defined as a six tuple M=(I,G,O,T,D,S). A building block modeling method uses the modules of a six tuple to form a rule based solution model. Moreover, a rule based system has been designed and set up to solve the Dynamic Programming Model. This knowledge based representation can be easily used to express symbolical knowledge and dynamic characteristics for Dynamic Programming Model, and the inference based on the knowledge in the process of solving Dynamic Programming Model can also be conveniently realized in computer. 展开更多
关键词 knowledge representation operations research Dynamic Programming Model intelligent modeling support
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Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation
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作者 Jing-Tao Sun Jing-Ming Li Qiu-Yu Zhang 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2022年第12期2150-2159,共10页
Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor do... Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,i.e.UKGE-MS.Specifically,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples.Firstly,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature items.Finally,the events knowledge graph is constructed by means of sparse representation and l1 norm.Extensive experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding algorithms.The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery. 展开更多
关键词 Text event mining knowledge graph of events Mutual information among neighbor domains Sparse representation
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Knowledge Representation Methods in Expert System for Earthquake Prediction ESEP 3.0
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作者 WangWei WuGengfeng +3 位作者 ZhangBofeng ZhengZhaobi LiuHui LiSheng 《Earthquake Research in China》 2005年第1期43-53,共11页
Knowledge representation is a key to the building of expert systems. The performance of knowledge representation methods directly affects the intelligence level and the problem-solving ability of the system. There are... Knowledge representation is a key to the building of expert systems. The performance of knowledge representation methods directly affects the intelligence level and the problem-solving ability of the system. There are various kinds of knowledge representation methods in ESEP3.0. In this paper, the authors introduce the knowledge representation methods, such as structure knowledge, seismological and precursory forecast knowledge, machine learning knowledge, synthetic prediction knowledge, knowledge to validate and verify certainty factors of anomalous evidence and support knowledge, etc. and propose a model for validation of certainty factors of anomalous evidence. The knowledge representation methods represent all kinds of earthquake prediction knowledge well. 展开更多
关键词 expert system knowledge representation fuzzy associative memory (FAM) certainty factor
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Knowledge Representation for the Geometrical Shapes
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作者 Abolfazl Fatholahzadeh Dariush Latifi 《Journal of Mathematics and System Science》 2018年第3期77-83,共7页
This paper outlines the necessity of the knowledge representation for the geometrical shapes (KRGS). We advocate that KRGS for being powerful must contain at least three major components, namely (1) fu... This paper outlines the necessity of the knowledge representation for the geometrical shapes (KRGS). We advocate that KRGS for being powerful must contain at least three major components, namely (1) fuzzy logic scheme; (2) the machine learning technique; and (3) an integrated algebraic and logical reasoning. After arguing the need for using fuzzy expressions in spatial reasoning, then inducing the spatial graph generalized and maximal common part of the expressions is discussed. Finally, the integration of approximate references into spatial reasoning using absolute measurements is outlined. The integration here means that the satisfiability of a fuzzy spatial expression is conducted by both logical and algebraic reasoning. 展开更多
关键词 knowledge representation integrated algebraic and logical fuzzy logic reasoning machine learning.
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Knowledge-Enhanced Bilingual Textual Representations for Cross-Lingual Semantic Textual Similarity
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作者 Hsuehkuan Lu Yixin Cao +1 位作者 Hou Lei Juanzi Li 《国际计算机前沿大会会议论文集》 2019年第1期436-440,共5页
Joint learning of words and entities is advantageous to various NLP tasks, while most of the works focus on single language setting. Cross-lingual representations learning receives high attention recently, but is stil... Joint learning of words and entities is advantageous to various NLP tasks, while most of the works focus on single language setting. Cross-lingual representations learning receives high attention recently, but is still restricted by the availability of parallel data. In this paper, a method is proposed to jointly embed texts and entities on comparable data. In addition to evaluate on public semantic textual similarity datasets, a task (cross-lingual text extraction) was proposed to assess the similarities between texts and contribute to this dataset. It shows that the proposed method outperforms cross-lingual representations methods using parallel data on cross-lingual tasks, and achieves competitive results on mono-lingual tasks. 展开更多
关键词 Text and knowledge representationS Cross-lingual representationS Cross-lingual SEMANTIC TEXTUAL SIMILARITY
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