# Semantic Driven Fielded Entity Retrieval

## TL;DR

This paper enhances entity retrieval by integrating semantic field-level features into the FSDM model, improving ranking accuracy on the DBpedia dataset through a semantic re-ranking approach.

## Contribution

It introduces a novel semantic re-ranking method that combines field-level semantic features with FSDM for improved entity search performance.

## Key findings

- Achieved 2.5% improvement in NDCG@10
- Achieved 1.2% improvement in NDCG@100
- Significant enhancement over existing FSDM model

## 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 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.