Knowledge Element Analogy Relation Recognition using Tezt and Graph Structure
Knowledge element analogy relation is a correspond- ing relationship in content, function or other aspects between two knowledge elements. This paper proposes a framework of relation Gaussian processes-based learning for knowledge element analogy relation recognition, which can integrate information from text and relation graph structure. Based on terms or core terms co-occurrence and type compatibility, two rules are first developed to construct candidate analogy relation instances from knowledge element set. Next, three kernels are devised to capture information of terms, semantic types and relative positions of two knowledge elements, and graph Laplacian and expectation propagation algorithm are employed to approximate the relation graph structure. Then, these two types of information are integrated to predict analogy relation. Experimental evaluation on four data sets related to “computer discipline demonstrates that the rules are effective and integrating three text kernels with relation graph structure can achieve better performance than only text kernels.
Knowledge element knowledge element analogy relation recognition candidate analogy relation instances construc-tion kernel graph structure
Wei WANG Qinghua ZHENG Yingying CHEN
Department of Computer Science and Technology, Xian Jiaotong University Xian, Shaanxi, China Schoo Department of Computer Science and Technology, Xian Jiaotong University Xian, Shaanxi, China
国际会议
大连
英文
1-8
2009-09-24（万方平台首次上网日期，不代表论文的发表时间）