Conference item
Reformulating ontological queries using materialised rewritings
- Abstract:
- Query rewriting is a prominent reasoning technique in ontologybased data access (OBDA) applications. Roughly speaking, a rewriting of a query Q w.r.t. an ontology is another query Q^1 that can be directly evaluated over the data without further reference to the input ontology. In this paper, we observe that many OBDA applications could significantly benefit from precomputing rewritings for certain queries. For example, in query optimisation, materialised rewritings of frequently asked queries can be used to speed up the query reformulation process. Moreover, in systems where users have different levels of access to information, materialised rewritings for the views assigned to each user can be exploited to obtain the set of answers to the input query derivable from the assigned views. Consequently, we investigate the problem of reformulating a query given a set of materialised rewritings and present a practical algorithm. Subsequently, we use our approach to design a fully edged query rewriting algorithm which can exploit materialised rewritings to speed up the rewriting process. Our experimental results confirm the potential of our technique in practice.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 323.0KB, Terms of use)
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- Publisher copy:
- 10.1145/2912845.2912867
Authors
- Publisher:
- Association of Computing Machinery
- Host title:
- 6th International Conference on Web-Intelligence, Mining and Semantics
- Journal:
- ,th International Conference on Web-Intelligence, Mining and Semantics More from this journal
- Publication date:
- 2016-04-01
- Acceptance date:
- 2016-04-01
- DOI:
- Keywords:
- Pubs id:
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pubs:614981
- UUID:
-
uuid:1ab08560-5a81-4f6e-a23b-02eab9f8b5bb
- Local pid:
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pubs:614981
- Source identifiers:
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614981
- Deposit date:
-
2016-04-12
- ARK identifier:
Terms of use
- Copyright holder:
- Association for Computing Machinery
- Copyright date:
- 2016
- Notes:
- © 2016 ACM.
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