The purpose of this research is to describe an efficient search methodology to help improve the search results in the top portion of a lengthy search list. When facing a lengthy search list, people often limit themselves to the top ten items on the list, even though there may be more useful information after the top ten items.
This study proposes an ontology‐based adaptive semantic search to significantly improve the search experience. To capture the semantic difference of search terms, naïve ontology is used to store the relationship among terms. Before a search term is processed by the search engine Lucene, the related words of the search term are selected from ontology structures to form new query phrases in the process of query expansion. The weighting of the expanded query phrases is dynamically learned by observing the users' clicking behaviors.
Research results show that with the aid of ontology the average precision rate of all cases is dramatically higher than the precision rate for the default search result. Even in the worst cases, in some situations, this ontology is still close to the precision rate for the default search result.
This paper shows how it is possible to improve the precision rate of items retrieved after a search and thus avoid information overload.
