To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance...To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and information content is presented in this paper. With the help of interrelationship between concepts, the information content of concepts and the strength of the edges in the ontology network, we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user’s question and answers in knowledge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is very satisfied. Key words intelligent question answering system - ontology - semantic similarity - geometric distance - information content CLC number TP39 Foundation item: Supported by the important science and technology item of China of “The 10th Five-year Plan” (2001BA101A05-04)Biography: LIU Ya-jun (1953-), female, Associate professor, research direction: software engineering, information processing, data-base application.展开更多
With the advent of the era of big data,knowledge engineering has received extensive attention.How to extract useful knowledge from massive data is the key to big data analysis.Knowledge graph technology is an importan...With the advent of the era of big data,knowledge engineering has received extensive attention.How to extract useful knowledge from massive data is the key to big data analysis.Knowledge graph technology is an important part of artificial intelligence,which provides a method to extract structured knowledge from massive texts and images,and has broad application prospects.The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search,intelligent question answering and personalized recommendation.Although knowledge graph has been applied to various systems,the basic theory and application technology still need further research.On the basis of comprehensively expounding the definition and architecture of knowledge graph,this paper reviews the key technologies of knowledge graph construction,including the research progress of four core technologies such as knowledge extraction technology,knowledge representation technology,knowledge fusion technology and knowledge reasoning technology,as well as some typical applications.Finally,the future development direction and challenges of the knowledge graph are prospected.展开更多
文摘To solve the problem of the inadequacy of semantic processing in the intelligent question answering system, an integrated semantic similarity model which calculates the semantic similarity using the geometric distance and information content is presented in this paper. With the help of interrelationship between concepts, the information content of concepts and the strength of the edges in the ontology network, we can calculate the semantic similarity between two concepts and provide information for the further calculation of the semantic similarity between user’s question and answers in knowledge base. The results of the experiments on the prototype have shown that the semantic problem in natural language processing can also be solved with the help of the knowledge and the abundant semantic information in ontology. More than 90% accuracy with less than 50 ms average searching time in the intelligent question answering prototype system based on ontology has been reached. The result is very satisfied. Key words intelligent question answering system - ontology - semantic similarity - geometric distance - information content CLC number TP39 Foundation item: Supported by the important science and technology item of China of “The 10th Five-year Plan” (2001BA101A05-04)Biography: LIU Ya-jun (1953-), female, Associate professor, research direction: software engineering, information processing, data-base application.
基金This research work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan ProvinceHunan Provincial Key Laboratory of Big Data Science and Technology,Finance and Economics+3 种基金Key Laboratory of Information Technology and Security,Hunan Provincial Higher Education.This research is funded by the Open Foundation for the University Innovation Platform in the Hunan Province,grant number 18K103Open project,Grant Numbers 20181901CRP03,20181901CRP04,20181901CRP05Hunan Provincial Education Science 13th Five-Year Plan(Grant No.XJK016BXX001)Social Science Foundation of Hunan Province(Grant No.17YBA049).
文摘With the advent of the era of big data,knowledge engineering has received extensive attention.How to extract useful knowledge from massive data is the key to big data analysis.Knowledge graph technology is an important part of artificial intelligence,which provides a method to extract structured knowledge from massive texts and images,and has broad application prospects.The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search,intelligent question answering and personalized recommendation.Although knowledge graph has been applied to various systems,the basic theory and application technology still need further research.On the basis of comprehensively expounding the definition and architecture of knowledge graph,this paper reviews the key technologies of knowledge graph construction,including the research progress of four core technologies such as knowledge extraction technology,knowledge representation technology,knowledge fusion technology and knowledge reasoning technology,as well as some typical applications.Finally,the future development direction and challenges of the knowledge graph are prospected.
