A Review of Graph-Based Models for Entity-Oriented Search

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Devezas, JL (Author) Other; Sérgio Nunes (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: SN Computer Science

Vol. 2

Final page: 437

ISSN: 2661-8907

Indexing

Scopus

Other information

Authenticus ID: P-00V-B34

DOI: 10.1007/s42979-021-00828-w

Abstract (EN): Entity-oriented search tasks heavily rely on exploiting unstructured and structured collections. Moreover, it is frequent for text corpora and knowledge bases to provide complementary views on a common topic. While, traditionally, the retrieval unit was the document, modern search engines have evolved to also retrieve entities and to provide direct answers to the information needs of the users. Cross-referencing information from heterogeneous sources has become fundamental, however a mismatch still exists between text-based and knowledge-based retrieval approaches. The former does not account for complex relations, while the latter does not properly support keyword-based queries and ranked retrieval. Graphs are a good solution to this problem, since they can be used to represent text, entities and their relations. In this survey, we examine text-based approaches and how they evolved to leverage entities and their relations in the retrieval process. We also cover multiple aspects of graph-based models for entity-oriented search, providing an overview on link analysis and exploring graph-based text representation and retrieval, leveraging knowledge graphs for document or entity retrieval, building entity graphs from text, using graph matching for querying with subgraphs, exploiting hypergraph-based representations, and ranking based on random walks on graphs. We close with a discussion on the topic and a view of the future to motivate the research of graph-based models for entity-oriented search, particularly as joint representation models for the generalization of retrieval tasks. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

Language: English

Type (Professor's evaluation): Scientific