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Reasoning on Datalog± Ontologies with Abductive Logic Programming.

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Title: Reasoning on Datalog± Ontologies with Abductive Logic Programming.
Authors: Gavanelli, Marco1 marco.gavanelli@unife.it, Lamma, Evelina1 evelina.lamma@unife.it, Riguzzi, Fabrizio2 fabrizio.riguzzi@unife.it, Bellodi, Elena1 elena.bellodi@unife.it, Zese, Riccardo1 riccardo.zese@unife.it, Cota, Giuseppe1 giuseppe.cota@unife.it
Source: Fundamenta Informaticae. 2018, Vol. 159 Issue 1-2, p65-93. 29p.
Subjects: Logic programming, Datalog (Computer program language), Ontologies (Information retrieval), Constraint satisfaction, Computational complexity
Abstract: Ontologies form the basis of the Semantic Web. Description Logics (DLs) are often the languages of choice for modeling ontologies. Integration of DLs with rules and rule-based reasoning is crucial in the so-called Semantic Web stack vision - a complete stack of recommendations and languages each based on and/or exploiting the underlying layers - which adds new features to the standards used in theWeb. The growing importance of the integration between DLs and rules is proved by the definition of the profile OWL 2 RL1 and the definition of languages such as RIF² and SWRL³. Datalog± is an extension of Datalog which can be used for representing lightweight ontologies and expressing some languages of the DL-Lite family, with tractable query answering under certain language restrictions. In particular, it is able to express the DL-Lite version defined in OWL. In this work, we show that Abductive Logic Programming (ALP) can be used to represent Datalog± ontologies, supporting query answering through an abductive proof procedure, and smoothly achieving the integration of ontologies and rule-based reasoning. Often, reasoning with DLs means finding explanations for the truth of queries, that are useful when debugging ontologies and to understand answers given by the reasoning process. We show that reasoning under existential rules can be expressed by ALP languages and we present a solving system, which is experimentally proved to be competitive with DL reasoning systems. In particular, we consider an ALP framework named SCIFF derived from the IFF abductive framework. Forward and backward reasoning is naturally supported in this ALP framework. The SCIFF language smoothly supports the integration of rules, expressed in a Logic Programming language, with Datalog± ontologies, mapped into SCIFF (forward) integrity constraints. The main advantage is that this integration is achieved within a single language, grounded on abduction in computational logic, and able to model existential rules. [ABSTRACT FROM AUTHOR]
Copyright of Fundamenta Informaticae is the property of Polskie Towarzystwo Matematyczne and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Reasoning on Datalog<superscript>±</superscript> Ontologies with Abductive Logic Programming.
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  Data: <searchLink fieldCode="AR" term="%22Gavanelli%2C+Marco%22">Gavanelli, Marco</searchLink><relatesTo>1</relatesTo><i> marco.gavanelli@unife.it</i><br /><searchLink fieldCode="AR" term="%22Lamma%2C+Evelina%22">Lamma, Evelina</searchLink><relatesTo>1</relatesTo><i> evelina.lamma@unife.it</i><br /><searchLink fieldCode="AR" term="%22Riguzzi%2C+Fabrizio%22">Riguzzi, Fabrizio</searchLink><relatesTo>2</relatesTo><i> fabrizio.riguzzi@unife.it</i><br /><searchLink fieldCode="AR" term="%22Bellodi%2C+Elena%22">Bellodi, Elena</searchLink><relatesTo>1</relatesTo><i> elena.bellodi@unife.it</i><br /><searchLink fieldCode="AR" term="%22Zese%2C+Riccardo%22">Zese, Riccardo</searchLink><relatesTo>1</relatesTo><i> riccardo.zese@unife.it</i><br /><searchLink fieldCode="AR" term="%22Cota%2C+Giuseppe%22">Cota, Giuseppe</searchLink><relatesTo>1</relatesTo><i> giuseppe.cota@unife.it</i>
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  Data: <searchLink fieldCode="JN" term="%22Fundamenta+Informaticae%22">Fundamenta Informaticae</searchLink>. 2018, Vol. 159 Issue 1-2, p65-93. 29p.
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  Data: <searchLink fieldCode="DE" term="%22Logic+programming%22">Logic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Datalog+%28Computer+program+language%29%22">Datalog (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Ontologies+%28Information+retrieval%29%22">Ontologies (Information retrieval)</searchLink><br /><searchLink fieldCode="DE" term="%22Constraint+satisfaction%22">Constraint satisfaction</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink>
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  Data: Ontologies form the basis of the Semantic Web. Description Logics (DLs) are often the languages of choice for modeling ontologies. Integration of DLs with rules and rule-based reasoning is crucial in the so-called Semantic Web stack vision - a complete stack of recommendations and languages each based on and/or exploiting the underlying layers - which adds new features to the standards used in theWeb. The growing importance of the integration between DLs and rules is proved by the definition of the profile OWL 2 RL1 and the definition of languages such as RIF² and SWRL³. Datalog± is an extension of Datalog which can be used for representing lightweight ontologies and expressing some languages of the DL-Lite family, with tractable query answering under certain language restrictions. In particular, it is able to express the DL-Lite version defined in OWL. In this work, we show that Abductive Logic Programming (ALP) can be used to represent Datalog± ontologies, supporting query answering through an abductive proof procedure, and smoothly achieving the integration of ontologies and rule-based reasoning. Often, reasoning with DLs means finding explanations for the truth of queries, that are useful when debugging ontologies and to understand answers given by the reasoning process. We show that reasoning under existential rules can be expressed by ALP languages and we present a solving system, which is experimentally proved to be competitive with DL reasoning systems. In particular, we consider an ALP framework named SCIFF derived from the IFF abductive framework. Forward and backward reasoning is naturally supported in this ALP framework. The SCIFF language smoothly supports the integration of rules, expressed in a Logic Programming language, with Datalog± ontologies, mapped into SCIFF (forward) integrity constraints. The main advantage is that this integration is achieved within a single language, grounded on abduction in computational logic, and able to model existential rules. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Fundamenta Informaticae is the property of Polskie Towarzystwo Matematyczne and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Logic programming
        Type: general
      – SubjectFull: Datalog (Computer program language)
        Type: general
      – SubjectFull: Ontologies (Information retrieval)
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      – SubjectFull: Constraint satisfaction
        Type: general
      – SubjectFull: Computational complexity
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      – TitleFull: Reasoning on Datalog± Ontologies with Abductive Logic Programming.
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