Massive Query Expansion by Exploiting Graph Knowledge Bases

TL;DR

This paper introduces a massive query expansion method leveraging knowledge bases like Wikipedia, combining lexical and topological approaches to improve search precision by over 27%.

Contribution

It presents a novel large-scale query expansion technique that integrates lexical and topological analysis of knowledge graphs for enhanced search accuracy.

Findings

Precision improved by more than 27%

Combining lexical and topological expansion yields better results

Effective use of Wikipedia as a knowledge base

Abstract

Keyword based search engines have problems with term ambiguity and vocabulary mismatch. In this paper, we propose a query expansion technique that enriches queries expressed as keywords and short natural language descriptions. We present a new massive query expansion strategy that enriches queries using a knowledge base by identifying the query concepts, and adding relevant synonyms and semantically related terms. We propose two approaches: (i) lexical expansion that locates the relevant concepts in the knowledge base; and, (ii) topological expansion that analyzes the network of relations among the concepts, and suggests semantically related terms by path and community analysis of the knowledge graph. We perform our expansions by using two versions of the Wikipedia as knowledge base, concluding that the combination of both lexical and topological expansion provides improvements of the system's precision up to more than 27%.