Query Expansion in Enterprise Search

Abstract

Although web search remains an active research area, interest in enterprise search has not kept up with the information requirements of the contemporary workforce. To address these issues, this research aims to develop, implement, and study the query expansion techniques most effective at improving relevancy in enterprise search. The case-study instrument was a custom Apache Solr-based search application deployed at a medium-sized manufacturing company. It was hypothesized that a composition of techniques tailored to enterprise content and information needs would prove effective in increasing relevancy evaluation scores. Query expansion techniques leveraging entity recognition, alphanumeric term identification, and intent classification were implemented and studied using real enterprise content and query logs. They were evaluated against a set of test queries derived from relevance survey results using standard relevancy metrics such as normalized discounted cumulative gain (nDCG). Each of these modules produced meaningful and statistically significant improvements in relevancy.