Effective Retrieval Model for Entity with Multi-valued Attributes: BM25MF and Beyond
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- 作者：Stéphane Campinas
Renaud Delbru
Giovanni Tummarello - 关键词：RDF ; Entity Retrieval ; Search ; Ranking ; Semi ; Structured Data ; BM25 ; BM25F ; BM25MF ; PL2 ; PL2F ; PL2MF
- 刊名：Lecture Notes in Computer Science
- 出版年：2012
- 出版时间：2012
- 年：2012
- 卷：7603
- 期：1
- 页码：216-226
- 全文大小：343KB
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Renaud Delbru
Giovanni Tummarello
文摘
The task of entity retrieval becomes increasingly prevalent as more and more structured information about entities is available on the Web in various forms such as documents embedding metadata (RDF, RDFa, Microdata, Microformats). International benchmarking campaigns, e.g., the Text REtrieval Conference or the Semantic Search Challenge, propose entity-oriented search tracks. This reflects the need for an effective search and discovery of entities. In this work, we present a multi-valued attributes model for entity retrieval which extends and generalises existing field-based ranking models. Our model introduces the concept of multi-valued attributes and enables attribute and value-specific normalization and weighting. Based on this model we extend two state-of-the-art field-based rankings, i.e., BM25F and PL2F, and demonstrate based on evaluations over heterogeneous datasets that this model improves significantly the retrieval performance compared to existing models. Finally, we introduce query dependent and independent weights specifically designed for our model which provide significant performance improvement.