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Explanation-Friendly Query Answering Under Uncertainty

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Reasoning Web. Explainable Artificial Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11810))

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Abstract

Many tasks often regarded as requiring some form of intelligence to perform can be seen as instances of query answering over a semantically rich knowledge base. In this context, two of the main problems that arise are: (i) uncertainty, including both inherent uncertainty (such as events involving the weather) and uncertainty arising from lack of sufficient knowledge; and (ii) inconsistency, which involves dealing with conflicting knowledge. These unavoidable characteristics of real world knowledge often yield complex models of reasoning; assuming these models are mostly used by humans as decision-support systems, meaningful explainability of their results is a critical feature. These lecture notes are divided into two parts, one for each of these basic issues. In Part 1, we present basic probabilistic graphical models and discuss how they can be incorporated into powerful ontological languages; in Part 2, we discuss both classical inconsistency-tolerant semantics for ontological query answering based on the concept of repair and other semantics that aim towards more flexible yet principled ways to handle inconsistency. Finally, in both parts we ponder the issue of deriving different kinds of explanations that can be attached to query results.

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Notes

  1. 1.

    Note, however, that the human aspect is not necessarily present, since the argumentation process could be carried out between software agents.

  2. 2.

    As an aside, and using concepts that will be defined shortly, the fundamental result linking homomorphisms to conjunctive query answering over relational databases can be informally stated as follows: let Q be a BCQ, and J be a database instance; then, if and only if there exists a homomorphism from the canonical database instance \(I^Q\) (essentially, an instance built using the predicates and variables from Q) to J [5, 19].

  3. 3.

    https://eugdpr.org/.

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Acknowledgments

This work was partially supported by funds provided by CONICET, Agencia Nacional de Promoción Científica y Tecnológica, Universidad Nacional del Sur (UNS), Argentina, and by the EU H2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement 690974 for the project “MIREL: MIning and REasoning with Legal texts”.

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Authors and Affiliations

  1. Department of Computer Science, Institute for Computer Science (UBA–CONICET), Universidad de Buenos Aires (UBA), C1428EGA, Ciudad Autonoma de Buenos Aires, Argentina

    Maria Vanina Martinez

  2. Department of Computer Science and Engineering, Institute for Computer Science and Engineering (UNS–CONICET), Universidad Nacional del Sur (UNS), San Andres 800, 8000, Bahia Blanca, Argentina

    Gerardo I. Simari

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  1. Maria Vanina Martinez
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  2. Gerardo I. Simari
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Correspondence to Maria Vanina Martinez .

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Editors and Affiliations

  1. Technische Universität Dresden, Dresden, Sachsen, Germany

    Markus Krötzsch

  2. Bosch Center for Artificial Intelligence, Renningen, Germany

    Daria Stepanova

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Martinez, M.V., Simari, G.I. (2019). Explanation-Friendly Query Answering Under Uncertainty. In: Krötzsch, M., Stepanova, D. (eds) Reasoning Web. Explainable Artificial Intelligence. Lecture Notes in Computer Science(), vol 11810. Springer, Cham. https://doi.org/10.1007/978-3-030-31423-1_2

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