Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Improving Retrieval Performance by Relevance Feedback
Improving Retrieval Performance by Relevance Feedback
Integrating word relationships into language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Semisupervised Query Expansion with Minimal Feedback
IEEE Transactions on Knowledge and Data Engineering
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Queries to search engine on the Internet are usually short, and cannot provide enough information for effective retrievals. Researchers have developed query expansion to cope with the problem and proved its usefulness. But previous researches have mainly on the general search engine and do not give the semantic enough attention. In this paper, we introduced a novel algorithm especially for the vertical search engine, which makes full use of the character that knowledge in special domain can be described more availably and powerfully than that in the open domain. In the algorithm, we utilize the knowledge, formalized by ontology, to generate semantic diagraph for combinations of words in one query. And then according to the semantic distance between the vertexes in the diagraph, we selected the candidates to be added. Terms added into the initial query are obviously related in semantic with the initial one. And we experimentally show that it can improve the search result clearly.