Post-search ambiguous query classification method based on contextual and temporal information

Abstract

Web search involves user queries to process and then in response provide information. Commonly, the provided information results much irrelevant information which need to be filtered according to the user needs. Queries submitted to search engines are by nature ambiguous. The ambiguous queries constitute a significant fraction of such instances and pose real challenges to the web search. It has also created an interest for the researchers to deal with search by considering the context along with temporal perspective. Furthermore, contextual as well as temporal information retrieval has been a topic of excessive interest in recent years. The purpose is to enhance the effectiveness of retrieved information in documents and queries. This paper presents a new method PsAQCM of classifying the ambiguous queries based on the post-search results by applying content similarity approach. Java-based prototype is developed to derive the contextual and temporal information from the web results based on the 220, 44, and 114 ambiguous queries of GISQC_DS, AMBIENT and MORESQUE dataset separately. Our proposed method attained 51%, 82% and 78% independently, improved results in terms of query ambiguity resolution. In future work, we intend to develop a small scale search engine which will enable us to carry out a full text analysis in order to improve the search performance in case of ambiguous queries.