Discovering Term Relations Through An Example-based Web Mining Approach
Date Issued
2006
Date
2006
Author(s)
Hsiao, Chung-Li
DOI
en-US
Abstract
There are lots of terminological resources on the web and continually increasing day by day. Term-based approaches are major information retrieval methods. Organizing terms into a well-formed information structure such as term graph is helpful for advanced IR applications, such as question answering and summarization. However, there are two problems to construct the useful term graph from the increasing terminological resources. One is that no context information can be used from terminological resources as in document-based approach of relation extraction. Another is that no explicitly specific relation types are predefined.
To solve the problems, we proposed an example-based Web mining approach to discover term relations from a term set. We identify relations by organizing Related Term Pairs (RTPs) according to similarity of their relations with user-given RTP example. We utilize a Web-mining approach to estimate similarity by the context words occurred in the search results of querying the RTP. We test our approach in a simulate term set. The experiment examine performance of several relation types, and the influence of example selection and example amount.
Subjects
術語關係
術語圖
術語組織
網路探勘
term relation
term graph
term organizing
Web mining
Type
other
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