TY - GEN
T1 - A tableau algorithm for possibilistic description logic ALC
AU - Qi, Guilin
AU - Pan, Jeff Z.
N1 - Guilin Qi is partially supported by the EU under the IST project NeOn and the X-Media project, and Jeff Z. Pan is partially supported by the EU MOST project.
PY - 2008/12/31
Y1 - 2008/12/31
N2 - Uncertainty reasoning and inconsistency handling are two important problems that often occur in the applications of the Semantic Web. Possibilistic description logics provide a flexible framework for representing and reasoning with ontologies where uncertain and/or inconsistent information is available. Although possibilistic logic has become a popular logical framework for uncertainty reasoning and inconsistency handling, its role in the Semantic Web is underestimated. One of the challenging problems is to provide a practical algorithm for reasoning in possibilistic description logics. In this paper, we propose a tableau algorithm for possibilistic description logic . We show how inference services in possibilistic can be reduced to the problem of computing the inconsistency degree of the knowledge base. We then give tableau expansion rules for computing the inconsistency degree of a possibilistic knowledge. We show that our algorithm is sound and complete. The computational complexity of our algorithm is analyzed. Since our tableau algorithm is an extension of a tableau algorithm for , we can reuse many optimization techniques for tableau algorithms of to improve the performance of our algorithm so that it can be applied in practice.
AB - Uncertainty reasoning and inconsistency handling are two important problems that often occur in the applications of the Semantic Web. Possibilistic description logics provide a flexible framework for representing and reasoning with ontologies where uncertain and/or inconsistent information is available. Although possibilistic logic has become a popular logical framework for uncertainty reasoning and inconsistency handling, its role in the Semantic Web is underestimated. One of the challenging problems is to provide a practical algorithm for reasoning in possibilistic description logics. In this paper, we propose a tableau algorithm for possibilistic description logic . We show how inference services in possibilistic can be reduced to the problem of computing the inconsistency degree of the knowledge base. We then give tableau expansion rules for computing the inconsistency degree of a possibilistic knowledge. We show that our algorithm is sound and complete. The computational complexity of our algorithm is analyzed. Since our tableau algorithm is an extension of a tableau algorithm for , we can reuse many optimization techniques for tableau algorithms of to improve the performance of our algorithm so that it can be applied in practice.
UR - http://www.scopus.com/inward/record.url?scp=58049128186&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-89704-0_5
DO - 10.1007/978-3-540-89704-0_5
M3 - Published conference contribution
AN - SCOPUS:58049128186
SN - 3540897038
SN - 9783540897033
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 61
EP - 75
BT - The Semantic Web - 3rd Asian Semantic Web Conference, ASWC 2008, Proceedings
T2 - 3rd Asian Semantic Web Conference, ASWC 2008
Y2 - 8 December 2008 through 11 December 2008
ER -