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Diversicon: Pluggable Lexical Domain Knowledge

  • Original Article
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Journal on Data Semantics

Abstract

Natural language understanding is a key task in a wide range of applications targeting data interoperability or analytics. For the analysis of domain-specific data, specialised knowledge resources (terminologies, grammars, word vector models, lexical databases) are necessary. The heterogeneity of such resources is, however, a major obstacle to their efficient use, especially in combination. This paper presents the open-source Diversicon Framework that helps application developers in finding, integrating, and accessing lexical domain knowledge, both symbolic and statistical, in a unified manner. The major components of the framework are: (1) an API and domain knowledge model that allow applications to retrieve domain knowledge through a common interface from a diversity of resource types, (2) implementations of the API for some of the most commonly used symbolic and statistical knowledge sources, (3) a domain-aware knowledge base that helps integrate static lexico-semantic resources, and (4) an online catalogue that either hosts or links to the existing resources from multiple domains. Support for Diversicon is already integrated into two of the most popular ontology matcher applications, a fact that we exploit to validate the framework and demonstrate its use on a example study that evaluates the effect of several common-sense and domain knowledge resources on a medical ontology matching task.

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Notes

  1. For example, the SNOMED ontology of medical terms contains over 500,000 concepts and 1,500,000 labels in English only.

  2. http://oaei.ontologymatching.org/.

  3. http://www.udcc.org.

  4. The vectors of antonyms, such as hot and cold, are typically considered as very closely related by these models.

  5. https://deeplearning4j.org/.

  6. https://github.com/thomasjungblut/glove.

  7. http://github.com/diversicon-kb/.

  8. http://github.com/diversicon-kb/divercli.

  9. http://github.com/diversicon-kb/divmaker.

  10. http://www.diversicon-kb.eu.

  11. http://getdkan.org.

  12. https://specialist.nlm.nih.gov/lexicon/.

  13. https://www.nlm.nih.gov/research/umls/.

  14. https://nlp.stanford.edu/projects/glove/.

  15. https://www.ncbi.nlm.nih.gov/pubmed/.

  16. The Diversicon-based SMATCH extensions are downloadable from https://github.com/s-match/.

  17. The Diversicon-equipped version of LogMap is downloadable from https://github.com/diversicon-kb/logmap-matcher.

  18. https://github.com/diversicon-kb.

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Authors and Affiliations

  1. Department of Information Engineering and Computer Science, University of Trento, via Sommarive 5, 38123, Trento, Italy

    Gábor Bella, David Leoni & Fausto Giunchiglia

  2. School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, UK

    Fiona McNeill

  3. School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, Scotland, UK

    Francisco José Quesada Real

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  1. Gábor Bella
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  2. Fiona McNeill
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  3. David Leoni
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  4. Francisco José Quesada Real
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  5. Fausto Giunchiglia
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Correspondence to Gábor Bella.

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Bella, G., McNeill, F., Leoni, D. et al. Diversicon: Pluggable Lexical Domain Knowledge. J Data Semant 8, 219–234 (2019). https://doi.org/10.1007/s13740-019-00107-1

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