Multi-Strategy Query Expansion Method Based on Semantics
Li Li, Hongbing Wang
Abstract
Li Li, Hongbing Wang
Abstract
Query expansion adds related words to a user query in order to improve retrieval results. It’s an important step in information retrieval. Most of current query expansion methods pay attention to specific expansion strategies or algorithms, while neglecting the query itself. In reaction to the phenomenon, a multistrategy query expansion method based on semantics was proposed. This method started by analyzing the semantic structure of user query, and adopted corresponding strategy to select expansion terms. The expansion words are derived from three parts: WordNet, massive web page set and search engine performance evaluation data, which were merged semantically in each expansion algorithm later. The experiment showed this method can improve retrieval results to some extent. Subject Categories and Descriptors H.3.3 [Information Search and Retrieval]; Query Formulation: I.2.7 [Natural Language Processing] General Terms: Information Retrieval, Query Expansion
OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Query expansion adds related words to a user query in order to improve retrieval results. It’s an important step in information retrieval. Most of current query expansion methods pay attention to specific expansion strategies or algorithms, while neglecting the query itself. In reaction to the phenomenon, a multistrategy query expansion method based on semantics was proposed. This method started by analyzing the semantic structure of user query, and adopted corresponding strategy to select expansion terms. The expansion words are derived from three parts: WordNet, massive web page set and search engine performance evaluation data, which were merged semantically in each expansion algorithm later. The experiment showed this method can improve retrieval results to some extent. Subject Categories and Descriptors H.3.3 [Information Search and Retrieval]; Query Formulation: I.2.7 [Natural Language Processing] General Terms: Information Retrieval, Query Expansion
Key concepts: Query expansion, Computer science, Information retrieval, Web search query, Web query classification, Query optimization, Query language, Sargable