Semantic Driven Fielded Entity Retrieval

Abstract

A common approach for knowledge-base entity search is to consider an entity as a document with multiple fields. Models that focus on matching query terms in different fields are popular choices for searching such entity representations. An instance of such a model is FSDM (Fielded Sequential Dependence Model). We propose to integrate fieldlevel semantic features into FSDM. We use FSDM to retrieve a pool of documents, and then to use semantic field-level features to rerank those documents. We propose to represent queries as bags of terms as well as bags of entities, and eventually, use their dense vector representation to compute semantic features based on query document similarity. Our proposed re-ranking approach achieves significant improvement in entity retrieval on the DBpedia-Entity (v2) dataset over existing FSDM model. Specifically, for all queries we achieve 2.5% and 1.2% significant improvement in NDCG@10 and NDCG@100, respectively. * equal contribution Copyright c CIKM 2018 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.