Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Advances in Information Retrieval |
| Untertitel | 31th European Conference on IR Research, ECIR 2009, Proceedings |
| Seiten | 189-201 |
| Seitenumfang | 13 |
| ISBN (elektronisch) | 978-3-642-00958-7 |
| Publikationsstatus | Veröffentlicht - 2009 |
| Veranstaltung | 31th European Conference on Information Retrieval, ECIR 2009 - Toulouse, Frankreich Dauer: 6 Apr. 2009 → 9 Apr. 2009 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 5478 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Entity Ranking has recently become an important search task in Information Retrieval. The goal is not to find documents matching query terms, but, instead, finding entities. In this paper we propose a formal model to search entities as well as a complete Entity Ranking system, providing examples of its application to the enterprise context.We experimentally evaluate our system on the Expert Search task in order to show how it can be adapted to different scenarios. The results show that combining simple IR techniques we improve of 53% in terms of P@10 over our baseline.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Proceedings. 2009. S. 189-201 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 5478 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - A vector space model for ranking entities and its application to expert search
AU - Demartini, Gianluca
AU - Gaugaz, Julien
AU - Nejdl, Wolfgang
PY - 2009
Y1 - 2009
N2 - Entity Ranking has recently become an important search task in Information Retrieval. The goal is not to find documents matching query terms, but, instead, finding entities. In this paper we propose a formal model to search entities as well as a complete Entity Ranking system, providing examples of its application to the enterprise context.We experimentally evaluate our system on the Expert Search task in order to show how it can be adapted to different scenarios. The results show that combining simple IR techniques we improve of 53% in terms of P@10 over our baseline.
AB - Entity Ranking has recently become an important search task in Information Retrieval. The goal is not to find documents matching query terms, but, instead, finding entities. In this paper we propose a formal model to search entities as well as a complete Entity Ranking system, providing examples of its application to the enterprise context.We experimentally evaluate our system on the Expert Search task in order to show how it can be adapted to different scenarios. The results show that combining simple IR techniques we improve of 53% in terms of P@10 over our baseline.
UR - http://www.scopus.com/inward/record.url?scp=67650742582&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-00958-7_19
DO - 10.1007/978-3-642-00958-7_19
M3 - Conference contribution
AN - SCOPUS:67650742582
SN - 978-3-642-00957-0
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 189
EP - 201
BT - Advances in Information Retrieval
T2 - 31th European Conference on Information Retrieval, ECIR 2009
Y2 - 6 April 2009 through 9 April 2009
ER -