Name
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%. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Data mining,Vocabulary mismatch,Computer science,Community analysis,Artificial intelligence,Natural language processing,Knowledge base,Ambiguity,Graph,Search engine,Information retrieval,Query expansion,Natural language |
DocType | Volume | Citations |
Journal | abs/1310.5698 | 1 |
PageRank | References | Authors |
0.34 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joan Guisado-Gámez | 1 | 16 | 3.11 |
| David Dominguez-Sal | 2 | 189 | 16.35 |
| Josep-Lluis Larriba-Pey | 3 | 245 | 21.70 |