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dARe – Using Argumentation to Explain Conclusions from a Controlled Natural Language Knowledge Base

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Advances in Artificial Intelligence: From Theory to Practice (IEA/AIE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10351))

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Abstract

We present an approach to reasoning with knowledge bases comprised of strict and defeasible rules over literals. A controlled natural language is proposed as a human/machine interface to facilitate the specification of knowledge and verbalisation of results. Techniques from formal argumentation theory are employed to justify conclusions of the approach; this aims at facilitating human acceptance of computed answers.

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Notes

  1. 1.

    While ASP can deal with this example, the common “not provably not” reading of “usually, \(\langle statement \rangle \)” phrases is not always correct.

  2. 2.

    Adding an abnormality atom into the body of line 5 (like in rule (12) of [5]) would address inconsistency, but not get us our intended reading. It would introduce the issue of having to create abnormality predicates from language input, where such predicates are not explicit.

  3. 3.

    http://attempto.ifi.uzh.ch/site/description/.

  4. 4.

    See [27, 28] for an example of several natural language statements that are worked with ACE and related to an instantiated argumentation framework.

  5. 5.

    RACE and PENG-ASP have the same expressions [12, 13]. RACE is based on Satchmo (written in Prolog), while PENG-ASP uses ASP.

  6. 6.

    An integration to AceRules is feasible; see, in a related setting, If Nixon is a quaker then Nixon usually is a pacifist. in https://argument-pipeline.herokuapp.com/, which is based on [26]. However, that work relied on ad-hoc manipulations of semantic representations.

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Author information

Authors and Affiliations

  1. Department of Computing Science, University of Aberdeen, Aberdeen, UK

    Adam Wyner

  2. Computer Science Institute, Leipzig University, Leipzig, Germany

    Hannes Strass

Authors
  1. Adam Wyner
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  2. Hannes Strass
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Correspondence to Adam Wyner .

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Editors and Affiliations

  1. Artois University, Lens, France

    Salem Benferhat

  2. Artois University, Lens, France

    Karim Tabia

  3. Texas State University, San Marcos, Texas, USA

    Moonis Ali

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Wyner, A., Strass, H. (2017). dARe – Using Argumentation to Explain Conclusions from a Controlled Natural Language Knowledge Base. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_35

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  • DOI: https://doi.org/10.1007/978-3-319-60045-1_35

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