Matches in ScholarlyData for { ?s ?p 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). The amount of structured data is such that supporting an effective search and discovery of entities is crucial for ensuring a satisfying user experience. Indeed, this need is reflected by international benchmarking campaigns, such as the Text REtrieval Conference or the Semantic Search Challenge which propose entity-oriented search tracks. In this work, we present a multi-valued attribute 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. We extend based on our model two state-of-the-art field-based rankings, i.e., BM25F and PL2F, and demonstrate that our model improves significantly the retrieval performance compared to existing models through evaluations on heterogeneous datasets. Finally, we introduce query dependent and independent weights specifically designed for our model which provide significant performance improvement.. }
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- 53 abstract "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). The amount of structured data is such that supporting an effective search and discovery of entities is crucial for ensuring a satisfying user experience. Indeed, this need is reflected by international benchmarking campaigns, such as the Text REtrieval Conference or the Semantic Search Challenge which propose entity-oriented search tracks. In this work, we present a multi-valued attribute 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. We extend based on our model two state-of-the-art field-based rankings, i.e., BM25F and PL2F, and demonstrate that our model improves significantly the retrieval performance compared to existing models through evaluations on heterogeneous datasets. Finally, we introduce query dependent and independent weights specifically designed for our model which provide significant performance improvement.".