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A Concept-Based Knowledge Representation Model for Semantic Entailment Inference
Zhao MJ(赵美静); Ni WC(倪晚成); Zhang HD(张海东); Yang YP(杨一平); Ni WC(倪晚成)
2014
Conference NameProceedings of 33rd Control Conference (CCC),Chinese, 2014
Source PublicationProceedings of 33rd Control Conference (CCC),Chinese, 2014
Pages522 - 527
Conference Date2014,0728-0730
Conference Place南京
AbstractSemantic entailment is a fundamental problem in natural language understanding that has a large number of applica-tions. Knowledge acquisition and knowledge representation are usually the crucial parts in semantic inference strategy. This paper presents a principled approach to semantic entailment problem that builds on a concept-based knowledge representation model (CKR). This model formally defines the concept as a triple (attribute, relation and behavior) and the knowledge of a concept can be illustrated with the triple. We propose a semantic inference strategy that against identify text segments which dissimilar on their surface form but share a common meaning. The inference strategy avoids syntactic analysis steps. A prelim-inary evaluation on the PASCAL text collection is presented. Experimental results show that our concept-based inference strat-egy is effective and has strong potential space.
KeywordSemantic Inference Knowledge Representation Concept Ckr Semantic Entailment
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11215
Collection综合信息系统研究中心
Corresponding AuthorNi WC(倪晚成)
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Zhao MJ,Ni WC,Zhang HD,et al. A Concept-Based Knowledge Representation Model for Semantic Entailment Inference[C],2014:522 - 527.
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