A Concept-Based Knowledge Representation Model for Semantic Entailment Inference

摘要：

　　Semantic entailment is a fundamental problem in natural language understanding field which has a large number of applications. Knowledge acquisition and knowledge representation are crucial parts in semantic inference strategies. This pa per 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 by the triple. We propose a semantic inference strategy that against identify text segments which with dissimilar surface form but share a common meaning. The inference strategy avoids syntactic analysis steps. A preliminary evaluation on the PASCAL text collection is presented. Experimental results show that our concept-based inference strategy is effective and has strong development potential.

关键词： Semantic inference Knowledge representation Concept CKR Semantic entailment

作者: ZHAO Meijing NI Wancheng ZHANG Haidong YANG Yiping

作者单位: Dept. Of CASIA-HHT Joint Laboratory of Smart Education Institute of Automation Chinese Academy of Science,Beijing,100190,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 522-527

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）