Effective Retrieval Model for Entity with Multi-valued Attributes: BM25MF and Beyond

  • Stéphane Campinas
  • Renaud Delbru
  • Giovanni Tummarello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7603)


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.


RDF Entity Retrieval Search Ranking Semi-Structured Data BM25 BM25F BM25MF PL2 PL2F PL2MF 


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stéphane Campinas
    • Renaud Delbru
      • Giovanni Tummarello

        There are no affiliations available

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