Abductive Logic Programming for Normative Reasoning and Ontologies

  • Marco GavanelliEmail author
  • Evelina Lamma
  • Fabrizio Riguzzi
  • Elena Bellodi
  • Zese Riccardo
  • Giuseppe Cota
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10091)


Abductive Logic Programming (ALP) has been exploited to formalize societies of agents, commitments and norms, taking advantage from ALP operational support as a (static or dynamic) verification tool. In [7], the most common deontic operators (obligation, prohibition, permission) are mapped into the abductive expectations of an ALP framework for agent societies. Building upon such correspondence, in [5], authors introduced \(Deon^{+}\), a language where obligation and prohibition deontic operators are enriched with quantification over time, by means of ALP and Constraint Logic Programming (CLP).

In recent work [30, 31], we have shown that the same ALP framework can be suitable to represent Datalog\(^\pm \) ontologies. Ontologies are a fundamental component of both the Semantic Web and knowledge-based systems, even in the legal setting, since they provide a formal and machine manipulable model of a domain.

In this work, we show that ALP is a suitable framework for representing both norms and ontologies. Normative reasoning and ontological query answering are obtained by applying the same abductive proof procedure, smoothly achieving their integration. In particular, we consider the ALP framework named \(\mathcal {S}\text {CIFF}\)  and derived from the IFF abductive framework, able to deal with existentially (and universally) quantified variables in rule heads and CLP constraints.

The main advantage is that this integration is achieved within a single language, grounded on abduction in computational logic.


Integrity Constraint Normative Reasoning Deontic Logic Conjunctive Query Computational Logic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to thank the anonymous referees for their helpful comments, and JURISIN2015 participants for questions and discussions during the workshop. The second author would like to thank NII, Tokyo (J), for supporting her travel to Japan and for making possible to attend JURISIN2015.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marco Gavanelli
    • 1
    Email author
  • Evelina Lamma
    • 1
  • Fabrizio Riguzzi
    • 2
  • Elena Bellodi
    • 1
  • Zese Riccardo
    • 1
  • Giuseppe Cota
    • 1
  1. 1.Dipartimento di IngegneriaUniversity of FerraraFerraraItaly
  2. 2.Dipartimento di Matematica e InformaticaUniversity of FerraraFerraraItaly

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