Semantic Driven Fielded Entity Retrieval
This work addresses entity search for knowledge-base applications, presenting an incremental enhancement to existing models.
The paper tackles the problem of improving entity retrieval in knowledge bases by integrating semantic features into a fielded retrieval model, achieving significant improvements of 2.5% in NDCG@10 and 1.2% in NDCG@100 on the DBpedia-Entity dataset.
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 field-level semantic features into FSDM. We use FSDM to retrieve a pool of documents, and then to use semantic field-level features to re-rank 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.