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

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Abstract

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.

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Notes

  1. http://archie.icm.edu.pl/archie-adv_eng.html.

  2. Semantic search as a task either refers to the semantically informed retrieval of documents, or to the retrieval of entities or relations over RDF graphs. We cover work on either approach, as both tasks are entity-oriented, using semantic search indiscriminately in both cases.

  3. http://lucene.apache.org.

  4. A concordance is a list of terms and their context. In this case, the concordance is about entities and their context.

  5. http://lucene.apache.org/solr/.

  6. Sesame is now known as Eclipse RDF4J: http://rdf4j.org/.

  7. http://lemurproject.org/clueweb09/FACC1/.

  8. http://lemurproject.org/clueweb12/.

  9. http://mschuhma.github.io/rewq/.

  10. There is not much evidence of link mining as an area beyond this survey, which leads us to believe that, albeit a good one, this showed no relevant adoption by the community.

  11. http://www.seg.rmit.edu.au/zettair/.

  12. http://mg4j.di.unimi.it/.

  13. Google Trends is identified in the paper as Google Zeitgeist, which was a previous designation.

  14. Please notice that we normalized the notation to be consistent over the document. Here, \(\lambda = \beta\) and \(d = 1 - \alpha\), when compared to the original paper.

  15. http://sentistrength.wlv.ac.uk/.

  16. Not to be confused with conceptual graphs [177].

  17. https://tagme.d4science.org/tagme/.

  18. https://www.w3.org/DesignIssues/CG.html.

  19. https://wordnet.princeton.edu/.

  20. https://dblp.org.

  21. https://graphbrain.net.

  22. https://bioportal.bioontology.org/.

  23. Please note that quantum approaches to information retrieval have already been explored in the past, for instance with the quantum language models by Sordoni et al. [176].

  24. https://www.w3.org/RDF/.

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  1. INESC TEC and Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal

    José Devezas & Sérgio Nunes

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José Devezas is supported by research grant PD/BD/128160/2016, provided by the Portuguese national funding agency for science, research and technology, Fundação para a Ciência e a Tecnologia (FCT), within the scope of Operational Program Human Capital (POCH), supported by the European Social Fund and by national funds from MCTES.

A Overview of entity-oriented search approaches and tasks

A Overview of entity-oriented search approaches and tasks

See Table 2, 3, 4.

Table 2 Classical information retrieval models applied to entity-oriented search
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Table 3 Learning to rank models for entity-oriented search
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Table 4 Graph-based models for entity-oriented search
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Devezas, J., Nunes, S. A Review of Graph-Based Models for Entity-Oriented Search. SN COMPUT. SCI. 2, 437 (2021). https://doi.org/10.1007/s42979-021-00828-w

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