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Ranking Schemas by Focus: A Cognitively-Inspired Approach

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Graph-Based Representation and Reasoning (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12879))

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Abstract

The main goal of this paper is to evaluate knowledge base schemas, modeled as a set of entity types, each such type being associated with a set of properties, according to their focus. We model the notion of focus as “the state or quality of being relevant in storing and retrieving information”. This definition of focus is adapted from the notion of “categorization purpose”, as first defined in cognitive psychology. In turn, this notion is formalized based on a set of knowledge metrics that, for any given focus, rank knowledge base schemas according to their quality. We apply the proposed methodology on a large data set of state-of-the-art knowledge base schemas and we show how it can be used in practice (Data and scripts are available at https://github.com/knowdive/Focus).

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Notes

  1. 1.

    “Something (A) is relevant to a task (T) if it increases the likelihood of accomplishing the goal (G), which is implied by T” [14].

  2. 2.

    http://schema.org/.

  3. 3.

    https://wiki.dbpedia.org/.

  4. 4.

    https://www.w3.org/2001/sw/WebOnt/.

  5. 5.

    See [20] for an overview of the multiple available approaches and applications.

  6. 6.

    https://www.w3.org/TR/turtle/.

  7. 7.

    https://lov.linkeddata.es/dataset/lov.

  8. 8.

    https://developers.google.com/freebase.

  9. 9.

    http://www.adampease.org/OP/.

  10. 10.

    https://pythonhosted.org/ordf/ordf_vocab_opencyc.html.

  11. 11.

    http://def.seegrid.csiro.au/isotc211/iso19115/2003/metadata.

  12. 12.

    http://www.ontotext.com/proton/protonext.html.

  13. 13.

    http://ns.inria.fr/ludo/v1/docs/gamemodel.html.

  14. 14.

    https://lov.linkeddata.es/dataset/lov/vocabs/akt.

  15. 15.

    https://gabriel-alex.github.io/cwmo/.

  16. 16.

    https://github.com/dbpedia/ontology-tracker/tree/master/ontologies/spitfire-project.eu.

  17. 17.

    A lot of KBSs have entity types labels codified by an ID.

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Acknowledgement

The research conducted by Mattia Fumagalli is supported by the “NEXON - Foundations of Next-Generation Ontology-Driven Conceptual Modeling” project, funded by the Free University of Bozen-Bolzano. The research conducted by Fausto Giunchiglia and Daqian Shi has received funding from the “DELPhi - DiscovEring Life Patterns”, funded by the MIUR Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) 2017 – DD n. 1062.

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Authors and Affiliations

  1. Conceptual and Cognitive Modeling Research Group (CORE), Free University of Bozen-Bolzano, Bolzano, Italy

    Mattia Fumagalli

  2. Department of Information Engineering and Computer Science (DISI), University of Trento, Trento, Italy

    Daqian Shi & Fausto Giunchiglia

Authors
  1. Mattia Fumagalli
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  2. Daqian Shi
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  3. Fausto Giunchiglia
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Corresponding author

Correspondence to Daqian Shi .

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  1. University of Lübeck, Lübeck, Germany

    Tanya Braun

  2. University of Lübeck, Lübeck, Germany

    Marcel Gehrke

  3. University of Kassel, Kassel, Germany

    Tom Hanika

  4. University of Toulouse, Toulouse, France

    Nathalie Hernandez

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Fumagalli, M., Shi, D., Giunchiglia, F. (2021). Ranking Schemas by Focus: A Cognitively-Inspired Approach. In: Braun, T., Gehrke, M., Hanika, T., Hernandez, N. (eds) Graph-Based Representation and Reasoning. ICCS 2021. Lecture Notes in Computer Science(), vol 12879. Springer, Cham. https://doi.org/10.1007/978-3-030-86982-3_6

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  • DOI: https://doi.org/10.1007/978-3-030-86982-3_6

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