Name
Abstract | ||
|---|---|---|
Patent retrieval is considered as recall-oriented retrieval that aims to find all relevant patent documents for a patent query. However, current methods encounter the term mismatch problem, because of the frequent use of nonstandard technical terms in patent documents. In order to deal with this problem, we propose a new patent query expansion approach by exploiting semantic knowledge base, which can enrich the query with semantically related concepts. Concretely, to understand the query semantics, we present the WordNet and Wikipedia-based expansion algorithms enhancing the initial query. We further provide the combination strategy to execute query and obtain retrieval results. Experiments are performed based on Java environment using the CLEF-IP collection. Results show that the performance of our approach is significantly better than other state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/FSKD.2017.8393111 | 2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) |
Keywords | Field | DocType |
Semantic knowledge, Patent retrieval, Term mismatch, Query expansion | Semantic memory,Query expansion,Information retrieval,Computer science,Knowledge-based systems,Encyclopedia,Artificial intelligence,WordNet,Semantics,Machine learning,The Internet,Electronic publishing | Conference |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Wang | 1 | 1 | 0.35 |
| Lanfen Lin | 2 | 78 | 24.70 |