Efficient Datalog Rewriting for Query Answering in TGD Ontologies

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Wang, Z
Xiao, P
Wang, K
Zhuang, Z
Wan, H
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2021
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Abstract

Tuple-generating dependencies (TGDs or existential rules) are an expressive constraint language for ontology-mediated query answering and thus query answering is of high complexity. Existing systems based on first-order rewriting methods can lead to queries too large for DBMS to handle. It is shown that datalog rewriting can result in more compact queries, yet previously proposed datalog rewriting methods are mostly inefficient for implementation. In this paper, we fill the gap by proposing an efficient datalog rewriting approach for answering conjunctive queries over TGDs, and identify and combine existing fragments of TGDs for which our rewriting method terminates. We implemented a prototype system Drewer, and experiments show that it is able to handle a wide range of benchmarks in the literature. Moreover, Drewer shows superior performance over state-of-the-art systems on both the compactness of rewriting and the efficiency of query answering.

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IEEE Transactions on Knowledge and Data Engineering

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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Artificial intelligence

Data management and data science

Information and computing sciences

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Wang, Z; Xiao, P; Wang, K; Zhuang, Z; Wan, H, Efficient Datalog Rewriting for Query Answering in TGD Ontologies, IEEE Transactions on Knowledge and Data Engineering, 2021

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