Fuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy or vague knowledge. In this paper, we consider the problem of reasoning with fuzzy-EL+, which is a fuzzy extension of EL+. We first identify the challenges and present revised completion classification rules for fuzzy-EL+ that can be handled by MapReduce programs. We then propose an algorithm for scale reasoning with fuzzy-EL+ ontologies using MapReduce. Some preliminary experimental results are provided to show the scalability of our algorithm.

%B Proceedings of the IJCAI-2013 Workshop on Weighted Logics for Artificial Intelligence (WL4AI 2013) %C Beijing, China %P 87-93 %G eng %0 Book %D 2013 %T 语义Web技术基础 %A Pascal Hitzler %A Markus Krötzsch %A Sebastian Rudolph %? Yong Yu %? Guilin Qi %? Haofen Wang %? Chang Liu %I Tsinghua University Press %G eng %0 Conference Paper %B ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track %D 2012 %T Reasoning with Fuzzy-EL+ Ontologies Using MapReduce %A Zhangquan Zhou %A Guilin Qi %A Chang Liu %A Pascal Hitzler %A Raghava Mutharaju %E Luc De Raedt %E Christian Bessière %E Didier Dubois %E Patrick Doherty %E Paolo Frasconi %E Fredrik Heintz %E Peter J. F. Lucas %XFuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy knowledge. In this paper, we consider fuzzy-EL+, which is a fuzzy extension of EL+. We first present revised completion rules for fuzzy-EL+ that can be handled by MapReduce programs. We then propose an algorithm for scale reasoning with fuzzy-EL+ ontologies based on MapReduce.

%B ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track %I IOS Press %C Montpellier, France %V 242 %P 933–934 %G eng %U http://dx.doi.org/10.3233/978-1-61499-098-7-933 %R 10.3233/978-1-61499-098-7-933 %0 Journal Article %J Journal of Logic and Computation %D 2011 %T Computing Inconsistency Measure based on Paraconsistent Semantics %A Yue Ma %A Guilin Qi %A Pascal Hitzler %XMeasuring inconsistency in knowledge bases has been recognized as an important problem in several research areas. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. However, existing methods suffer from two limitations: 1) They are mostly restricted to propositional knowledge bases; 2) Very few of them discuss computational aspects of computing inconsistency measures. In this paper, we try to solve these two limitations by exploring algorithms for computing an inconsistency measure of first-order knowledge bases. After introducing a four-valued semantics for first-order logic, we define an inconsistency measure of a first-order knowledge base, which is a sequence of inconsistency degrees. We then propose a precise algorithm to compute our inconsistency measure. We show that this algorithm reduces the computation of the inconsistency measure to classical satisfiability checking. This is done by introducing a new semantics, named S[n]-4 semantics, which can be calculated by invoking a classical SAT solver. Moreover, we show that this auxiliary semantics also gives a direct way to compute upper and lower bounds of inconsistency degrees. That is, it can be easily revised to compute approximating inconsistency measures. The approximating inconsistency measures converge to the precise values if enough resources are available. Finally, by some nice properties of the S[n]-4 semantics, we show that some upper and lower bounds can be computed in P-time, which says that the problem of computing these approximating inconsistency measures is tractable.

%B Journal of Logic and Computation %V 21 %P 1257–1281 %G eng %U http://dx.doi.org/10.1093/logcom/exq053 %N 6 %R 10.1093/logcom/exq053 %0 Journal Article %J International Journal of Software and Informatics %D 2010 %T Computational Complexity and Anytime Algorithm for Inconsistency Measurement %A Yue Ma %A Guilin Qi %A Guohui Xiao %A Pascal Hitzler %A Zuoquan Lin %K algorithm %K computational complexity %K inconsistency measurement %K Knowledge representation %K multi-valued logic %XMeasuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first give a complete analysis of the computational complexity of computing inconsistency degrees. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximations of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm

%B International Journal of Software and Informatics %V 4 %P 3–21 %G eng %U http://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41&flag=1 %0 Journal Article %J Annals of Mathematics and Artificial Intelligence %D 2010 %T Preface - Special issue on commonsense reasoning for the semantic web %A Frank van Harmelen %A Andreas Herzig %A Pascal Hitzler %A Guilin Qi %B Annals of Mathematics and Artificial Intelligence %V 58 %P 1–2 %G eng %U http://dx.doi.org/10.1007/s10472-010-9209-7 %R 10.1007/s10472-010-9209-7 %0 Conference Paper %B Knowledge Science, Engineering and Management, Third International Conference, KSEM 2009 %D 2009 %T An Anytime Algorithm for Computing Inconsistency Measurement %A Yue Ma %A Guilin Qi %A Guohui Xiao %A Pascal Hitzler %A Zuoquan Lin %E Dimitris Karagiannis %E Zhi Jin %XMeasuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first analyze its computational complexity. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximation of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm.

%B Knowledge Science, Engineering and Management, Third International Conference, KSEM 2009 %I Springer %C Vienna, Austria %V 5914 %P 29–40 %G eng %U http://dx.doi.org/10.1007/978-3-642-10488-6_7 %R 10.1007/978-3-642-10488-6_7 %0 Conference Paper %B The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2009 %D 2009 %T RaDON - Repair and Diagnosis in Ontology Networks %A Qiu Ji %A Peter Haase %A Guilin Qi %A Pascal Hitzler %A Steffen Stadtmüller %E Lora Aroyo %E Paolo Traverso %E Fabio Ciravegna %E Philipp Cimiano %E Tom Heath %E Eero Hyvönen %E Riichiro Mizoguchi %E Eyal Oren %E Marta Sabou %E Elena Paslaru Bontas Simperl %XOne of the major challenges in managing networked and dynamic ontologies is to handle inconsistencies in single ontologies, and inconsistencies introduced by integrating multiple distributed ontologies. Our RaDON system provides functionalities to repair and diagnose ontology networks by extending the capabilities of existing reasoners. The system integrates several new debugging and repairing algorithms, such as a relevance-directed algorithm to meet the various needs of the users.

%B The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2009 %I Springer %C Heraklion, Crete, Greece %V 5554 %P 863–867 %G eng %U http://dx.doi.org/10.1007/978-3-642-02121-3_71 %R 10.1007/978-3-642-02121-3_71