Soundness preserving approximation for TBox reasoning

Yuan Ren; J.Z. Pan; Y. Zhao

Soundness preserving approximation for TBox reasoning

Yuan Ren, J.Z. Pan, Y. Zhao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Large scale ontology applications require efficient and robust description logic (DL) reasoning services. Expressive DLs usually have very high worst case complexity while tractable DLs are restricted in terms of expressive power. This brings a new challenge: can users use expressive DLs to build their ontologies and still enjoy the efficient services as in tractable languages. In this paper, we present a soundness preserving approximate reasoning framework for TBox reasoning in OWL2-DL. The ontologies are encoded into eL with additional data structures. A tractable algorithm is presented to classify such approximation by realizing more and more inference patterns. Preliminary evaluation shows that our approach can classify existing benchmarks in large scale efficiently with a high recall.

Original language	English
Title of host publication	Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence and the Twenty-second Innovative Applications of Artificial Intelligence Conference, 11-15 July, 2010, Atlanta, Georgia, USA
Publisher	AAAI Press
Pages	351-356
Number of pages	6
ISBN (Electronic)	978-1-57735-462-8
ISBN (Print)	978-1-57735-463-5
Publication status	Published - 15 Jul 2010
Event	Twenty-Fourth AAAI Conference on Artificial Intelligence - Atlanta, United States Duration: 11 Jul 2010 → 15 Nov 2010 Conference number: 24 https://www.aaai.org/Conferences/AAAI/aaai10.php

Publication series

Name	Twenty-Fourth AAAI Conference on Artificial Intelligence
Publisher	AAAI Press
Number	1
Volume	24
ISSN (Print)	2159-5399
ISSN (Electronic)	2374-3468

Conference

Conference	Twenty-Fourth AAAI Conference on Artificial Intelligence
Abbreviated title	AAAI 2010
Country/Territory	United States
City	Atlanta
Period	11/07/10 → 15/11/10
Internet address	https://www.aaai.org/Conferences/AAAI/aaai10.php

Keywords / Materials (for Non-textual outputs)

approximate reasoning
description logic
expressive power
inference

Access to Document

https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1955Licence: All Rights Reserved
https://ojs.aaai.org/index.php/AAAI/article/view/7602Licence: All Rights Reserved

Cite this

Ren, Y., Pan, J. Z., & Zhao, Y. (2010). Soundness preserving approximation for TBox reasoning. In Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence and the Twenty-second Innovative Applications of Artificial Intelligence Conference, 11-15 July, 2010, Atlanta, Georgia, USA (pp. 351-356). (Twenty-Fourth AAAI Conference on Artificial Intelligence; Vol. 24, No. 1). AAAI Press. https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1955

Ren, Yuan ; Pan, J.Z. ; Zhao, Y. / Soundness preserving approximation for TBox reasoning. Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence and the Twenty-second Innovative Applications of Artificial Intelligence Conference, 11-15 July, 2010, Atlanta, Georgia, USA. AAAI Press, 2010. pp. 351-356 (Twenty-Fourth AAAI Conference on Artificial Intelligence; 1).

@inproceedings{274da0c4e15d460caff0f2a0488768dc,

title = "Soundness preserving approximation for TBox reasoning",

abstract = "Large scale ontology applications require efficient and robust description logic (DL) reasoning services. Expressive DLs usually have very high worst case complexity while tractable DLs are restricted in terms of expressive power. This brings a new challenge: can users use expressive DLs to build their ontologies and still enjoy the efficient services as in tractable languages. In this paper, we present a soundness preserving approximate reasoning framework for TBox reasoning in OWL2-DL. The ontologies are encoded into eL with additional data structures. A tractable algorithm is presented to classify such approximation by realizing more and more inference patterns. Preliminary evaluation shows that our approach can classify existing benchmarks in large scale efficiently with a high recall.",

keywords = "approximate reasoning, description logic, expressive power, inference",

author = "Yuan Ren and J.Z. Pan and Y. Zhao",

year = "2010",

month = jul,

day = "15",

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series = "Twenty-Fourth AAAI Conference on Artificial Intelligence",

publisher = "AAAI Press",

number = "1",

pages = "351--356",

booktitle = "Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence and the Twenty-second Innovative Applications of Artificial Intelligence Conference, 11-15 July, 2010, Atlanta, Georgia, USA",

note = "Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010 ; Conference date: 11-07-2010 Through 15-11-2010",

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Ren, Y, Pan, JZ & Zhao, Y 2010, Soundness preserving approximation for TBox reasoning. in Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence and the Twenty-second Innovative Applications of Artificial Intelligence Conference, 11-15 July, 2010, Atlanta, Georgia, USA. Twenty-Fourth AAAI Conference on Artificial Intelligence, no. 1, vol. 24, AAAI Press, pp. 351-356, Twenty-Fourth AAAI Conference on Artificial Intelligence, Atlanta, Georgia, United States, 11/07/10. <https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1955>

Soundness preserving approximation for TBox reasoning. / Ren, Yuan; Pan, J.Z.; Zhao, Y.
Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence and the Twenty-second Innovative Applications of Artificial Intelligence Conference, 11-15 July, 2010, Atlanta, Georgia, USA. AAAI Press, 2010. p. 351-356 (Twenty-Fourth AAAI Conference on Artificial Intelligence; Vol. 24, No. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Soundness preserving approximation for TBox reasoning

AU - Ren, Yuan

AU - Pan, J.Z.

AU - Zhao, Y.

N1 - Conference code: 24

PY - 2010/7/15

Y1 - 2010/7/15

N2 - Large scale ontology applications require efficient and robust description logic (DL) reasoning services. Expressive DLs usually have very high worst case complexity while tractable DLs are restricted in terms of expressive power. This brings a new challenge: can users use expressive DLs to build their ontologies and still enjoy the efficient services as in tractable languages. In this paper, we present a soundness preserving approximate reasoning framework for TBox reasoning in OWL2-DL. The ontologies are encoded into eL with additional data structures. A tractable algorithm is presented to classify such approximation by realizing more and more inference patterns. Preliminary evaluation shows that our approach can classify existing benchmarks in large scale efficiently with a high recall.

AB - Large scale ontology applications require efficient and robust description logic (DL) reasoning services. Expressive DLs usually have very high worst case complexity while tractable DLs are restricted in terms of expressive power. This brings a new challenge: can users use expressive DLs to build their ontologies and still enjoy the efficient services as in tractable languages. In this paper, we present a soundness preserving approximate reasoning framework for TBox reasoning in OWL2-DL. The ontologies are encoded into eL with additional data structures. A tractable algorithm is presented to classify such approximation by realizing more and more inference patterns. Preliminary evaluation shows that our approach can classify existing benchmarks in large scale efficiently with a high recall.

KW - approximate reasoning

KW - description logic

KW - expressive power

KW - inference

M3 - Conference contribution

SN - 978-1-57735-463-5

T3 - Twenty-Fourth AAAI Conference on Artificial Intelligence

SP - 351

EP - 356

BT - Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence and the Twenty-second Innovative Applications of Artificial Intelligence Conference, 11-15 July, 2010, Atlanta, Georgia, USA

PB - AAAI Press

T2 - Twenty-Fourth AAAI Conference on Artificial Intelligence

Y2 - 11 July 2010 through 15 November 2010

ER -

Ren Y, Pan JZ, Zhao Y. Soundness preserving approximation for TBox reasoning. In Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence and the Twenty-second Innovative Applications of Artificial Intelligence Conference, 11-15 July, 2010, Atlanta, Georgia, USA. AAAI Press. 2010. p. 351-356. (Twenty-Fourth AAAI Conference on Artificial Intelligence; 1). Epub 2010 Jul 3.

Soundness preserving approximation for TBox reasoning

Abstract

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Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

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Cite this