# A Purely Entity-Based Semantic Search Approach for Document Retrieval
## 2 Related Works
### Entity-Based Document Retrieval  
In the literature, over the last decade, many document retrieval approaches have explored different types of uses of entity-based representation to improve the representation of documents and queries. [PERSON] et al. (2017) [6] proposed a neural information retrieval approach using both term-based and entity-based representations for queries and documents. The approach performs four-way interactions, allowing four matching possibilities between the query and the document. The four possible representations of the document and query are: query terms (words) to document terms, query entities to document entities, query entities to document terms, and query terms to document entities. Thus, they achieve their document retrieval and ranking by integrating those combinations into neural models. [PERSON] et al. (2018) [7] also introduced a neural ranking model that combines entity-based and term-based representations of documents and queries. They used a translation layer in their neural architecture, matching queries and documents without using handcrafted features. [PERSON] et al. (2018) [19] proposed a document retrieval approach that uses neural embeddings that consider both word embeddings and entity embeddings. Also, they compared word and entity embedding performances. [PERSON] et al. (2019) [8] proposed a neural embeddings-based representation for documents by considering the term, entity, and semantic type within the same embedding space. [PERSON] et al. (2022) [22] proposed the EM-BERT model, which incorporates entity embeddings into a point-wise document ranking approach. The model combines words and entities into an embedding representation to represent both the query and the document using the BERT [24] model. Although many entity-based document retrieval approaches have been proposed, the problem of how to effectively leverage entity-based representation still needs to be resolved. [PERSON] et al. [10] presented a survey on existing neural ranking models, highlighting the models that learn with external knowledge, such as knowledge bases. They indicated that more research is needed to improve the effectiveness of neural ranking models with external knowledge and to understand external knowledge's role in ranking tasks. Moreover, [PERSON] et al. [9] explain that the problem of how to effectively leverage entity-based representation in conjunction with term-based representation still needs to be solved. On the other hand, these approaches and models use knowledge graphs as embedding-based representations, where entity embeddings are learned from knowledge graphs in many ways in the literature [29; 30; 31].  
To understand how to effectively leverage the knowledge graph alongside any other model, we introduced PES4 IR, a novel solution, to empirically study the impact of purely entity-based representation for document retrieval. With PES4 IR, queries and documents are represented only by an entity-based representation, without the use of a neural network and embedding-based representation. Below, we discuss the background related to the entity-based document retrieval task.