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Reasoning over Ontologies with DLV

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018)

Abstract

The paper presents DLV, an advanced AI system from the area of Answer Set Programming (ASP), showing its high potential for reasoning over ontologies. Ontological reasoning services represent fundamental features in the development of the Semantic Web. Among them, scientists are focusing their attention on the so-called ontology-based query answering (OBQA) task where a (conjunctive) query has to be evaluated over a logical theory (a.k.a. Knowledge Base, or simply KB) consisting of an extensional database (a.k.a. ABox) paired with an ontology (a.k.a. TBox). From a theoretical viewpoint, much has been done. Indeed, Description logics and Datalog\(^\pm \) have been recognized as the two main families of formal ontology specification languages to specify KBs, while OWL has been identified as the official W3C standard language to physically represent and share them; moreover sophisticated algorithms and techniques have been proposed. Conversely, from a practical point of view, only a few systems for solving complex ontological reasoning services such as OBQA have been developed, and no official standard has been identified yet. The aim of the present paper is to illustrate the applicability of the well-known ASP system DLV for powerful ontology-based reasoning.

This work has been partially supported by Samsung under project “Enhancing the DLV system for large-scale ontology reasoning” (Ref. EHQ180906_0004).

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Notes

  1. 1.

    See https://www.w3.org/.

  2. 2.

    See http://www.w3.org/TR/owl2-profiles/.

  3. 3.

    Following Vardi’s taxonomy [41], the data complexity is calculated taking only the ABox as input, whereas the query and the TBox are considered fixed. The combined complexity is the complexity calculated considering as input, together with the ABox, also the query and the TBox.

  4. 4.

    As usual in DLs, \(A \equiv B\) is a shortcut form \(A \sqsubseteq B\) together with \(B \sqsubseteq A\).

  5. 5.

    The Adolena (Abilities and Disabilities OntoLogy for ENhancing Accessibility) ontology [31] has been developed for the South African National Accessibility Portal. It describes abilities, disabilities and devices.

  6. 6.

    StockExchange [38] is an ontology of the domain of financial institution in the EU.

  7. 7.

    Path5 is a synthetic ontology [38] encoding graph structures, and used to generate an exponential blow-up of the size of the rewritten queries.

  8. 8.

    Galen is an open source medical ontology that is widely used as stress test for OBQA systems since its TBox consists of about 50k/60k axioms. For more details, see https://bioportal.bioontology.org/ontologies/GALEN.

  9. 9.

    NPD FactPages is an ontology describing the petroleum activities in the Norwegian continental shelf.

  10. 10.

    Vicodí is an ontology of European history which falls in OWL 2 RL, developed within the Vicodì project. For more details, see https://cordis.europa.eu/result/rcn/34582_en.html.

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Allocca, C. et al. (2020). Reasoning over Ontologies with DLV. In: Fred, A., Salgado, A., Aveiro, D., Dietz, J., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2018. Communications in Computer and Information Science, vol 1222. Springer, Cham. https://doi.org/10.1007/978-3-030-49559-6_6

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