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Towards Provenance in Heterogeneous Knowledge Bases

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Logic Programming and Nonmonotonic Reasoning (LPNMR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13416))

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Abstract

A rapidly increasing amount of data, information and knowledge is becoming available on the Web, often written in different formats and languages, adhering to standardizations driven by the World Wide Web Consortium initiative. Taking advantage of all this heterogeneous knowledge requires its integration for more sophisticated reasoning services and applications. To fully leverage the potential of such systems, their inferences should be accompanied by justifications that allow a user to understand a proposed decision/recommendation, in particular for critical systems (healthcare, law, finances, etc.). However, determining such justifications has commonly only been considered for a single formalism, such as relational databases, description logic ontologies, or declarative rule languages. In this paper, we present the first approach for providing provenance for heterogeneous knowledge bases building on the general framework of multi-context systems, as an abstract, but very expressive formalism to represent knowledge bases written in different formalisms and the flow of information between them. We also show under which conditions and how provenance information in this formalism can be computed.

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Notes

  1. 1.

    https://www.w3.org/.

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Acknowledgment

We thank the anonymous reviewers for their comments and FCT projects RIVER (PTDC/CCI-COM/30952/2017), FORGET (PTDC/CCI-INF/32219/2017), and NOVA LINCS (UIDB/04516/2020) for partial support.

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Authors and Affiliations

  1. Departamento de Informática, NOVA LINCS, FCT NOVA, Caparica, Portugal

    Matthias Knorr, Carlos Viegas Damásio, Ricardo Gonçalves & João Leite

Authors
  1. Matthias Knorr
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  2. Carlos Viegas Damásio
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  3. Ricardo Gonçalves
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  4. João Leite
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Corresponding author

Correspondence to Matthias Knorr .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Georg Gottlob

  2. Miami University, Oxford, OH, USA

    Daniela Inclezan

  3. University of Genova, Genoa, Italy

    Marco Maratea

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Knorr, M., Damásio, C.V., Gonçalves, R., Leite, J. (2022). Towards Provenance in Heterogeneous Knowledge Bases. In: Gottlob, G., Inclezan, D., Maratea, M. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2022. Lecture Notes in Computer Science(), vol 13416. Springer, Cham. https://doi.org/10.1007/978-3-031-15707-3_22

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  • DOI: https://doi.org/10.1007/978-3-031-15707-3_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15706-6

  • Online ISBN: 978-3-031-15707-3

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