期刊
出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2611040.2611079
关键词
Information extraction; semantic relations; natural language processing; background knowledge; thesauri; wikipedia
Background knowledge as provided by repositories such as WordNet is of critical importance for linking or mapping ontologies and related tasks. Since current repositories are quite limited in their scope and currentness, we investigate how to automatically build up improved repositories by extracting semantic relations (e.g., is-a and part-of relations) from Wikipedia articles. Our approach uses a comprehensive set of semantic patterns, finite state machines and NLP-techniques to process Wikipedia de finitions and to identify semantic relations between concepts. Our approach is able to extract multiple relations from a single Wikipedia article. An evaluation for different domains shows the high quality and effectiveness of the proposed approach.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据