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  • Query Answering with Guarded Existential Rules under Stable Model Semantics

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    Wang454541-Accepted.pdf (1.146Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Wang, Zhe
    Wan, Hai
    Xiao, Guohui
    Wang, Chenglin
    Chen, Junhong
    Griffith University Author(s)
    Wang, Zhe
    Year published
    2020
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    Abstract
    In this paper, we study the problem of query answering with guarded existential rules (also called GNTGDs) under stable model semantics. Our goal is to use existing answer set programming (ASP) solvers. However, ASP solvers handle only finitely-ground logic programs while the program translated from GNTGDs by Skolemization is not in general. To address this challenge, we introduce two novel notions of (1) guarded instantiation forest to describe the instantiation of GNTGDs and (2) prime block to characterize the repeated infinitely-ground program translated from GNTGDs. Using these notions, we prove that the ground termination ...
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    In this paper, we study the problem of query answering with guarded existential rules (also called GNTGDs) under stable model semantics. Our goal is to use existing answer set programming (ASP) solvers. However, ASP solvers handle only finitely-ground logic programs while the program translated from GNTGDs by Skolemization is not in general. To address this challenge, we introduce two novel notions of (1) guarded instantiation forest to describe the instantiation of GNTGDs and (2) prime block to characterize the repeated infinitely-ground program translated from GNTGDs. Using these notions, we prove that the ground termination problem for GNTGDs is decidable. We also devise an algorithm for query answering with GNTGDs using ASP solvers. We have implemented our approach in a prototype system. The evaluation over a set of benchmarks shows encouraging results.
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    Conference Title
    Thirty-Fourth AAAI Conference on Artificial Intelligence
    Publisher URI
    https://aaai.org/Conferences/AAAI-20/
    Copyright Statement
    © 2020 AAAI Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/400023
    Collection
    • Conference outputs

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