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.

JF - Proceedings of the IJCAI-2013 Workshop on Weighted Logics for Artificial Intelligence (WL4AI 2013) CY - Beijing, China ER - TY - BOOK T1 - 语义Web技术基础 Y1 - 2013 A1 - Pascal Hitzler A1 - Markus Krötzsch A1 - Sebastian Rudolph PB - Tsinghua University Press ER - TY - CONF T1 - Reasoning with Fuzzy-EL+ Ontologies Using MapReduce T2 - ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track Y1 - 2012 A1 - Zhangquan Zhou A1 - Guilin Qi A1 - Chang Liu A1 - Pascal Hitzler A1 - Raghava Mutharaju ED - Luc De Raedt ED - Christian Bessière ED - Didier Dubois ED - Patrick Doherty ED - Paolo Frasconi ED - Fredrik Heintz ED - Peter J. F. Lucas AB -Fuzzy 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.

JF - ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track PB - IOS Press CY - Montpellier, France VL - 242 UR - http://dx.doi.org/10.3233/978-1-61499-098-7-933 ER - TY - JOUR T1 - Computing Inconsistency Measure based on Paraconsistent Semantics JF - Journal of Logic and Computation Y1 - 2011 A1 - Yue Ma A1 - Guilin Qi A1 - Pascal Hitzler AB -Measuring 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.

VL - 21 UR - http://dx.doi.org/10.1093/logcom/exq053 IS - 6 ER - TY - JOUR T1 - Computational Complexity and Anytime Algorithm for Inconsistency Measurement JF - International Journal of Software and Informatics Y1 - 2010 A1 - Yue Ma A1 - Guilin Qi A1 - Guohui Xiao A1 - Pascal Hitzler A1 - Zuoquan Lin KW - algorithm KW - computational complexity KW - inconsistency measurement KW - Knowledge representation KW - multi-valued logic AB -Measuring 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

VL - 4 UR - http://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41&flag=1 ER - TY - JOUR T1 - Preface - Special issue on commonsense reasoning for the semantic web JF - Annals of Mathematics and Artificial Intelligence Y1 - 2010 A1 - Frank van Harmelen A1 - Andreas Herzig A1 - Pascal Hitzler A1 - Guilin Qi VL - 58 UR - http://dx.doi.org/10.1007/s10472-010-9209-7 ER - TY - CONF T1 - An Anytime Algorithm for Computing Inconsistency Measurement T2 - Knowledge Science, Engineering and Management, Third International Conference, KSEM 2009 Y1 - 2009 A1 - Yue Ma A1 - Guilin Qi A1 - Guohui Xiao A1 - Pascal Hitzler A1 - Zuoquan Lin ED - Dimitris Karagiannis ED - Zhi Jin AB -Measuring 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.

JF - Knowledge Science, Engineering and Management, Third International Conference, KSEM 2009 PB - Springer CY - Vienna, Austria VL - 5914 UR - http://dx.doi.org/10.1007/978-3-642-10488-6_7 ER - TY - CONF T1 - RaDON - Repair and Diagnosis in Ontology Networks T2 - The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2009 Y1 - 2009 A1 - Qiu Ji A1 - Peter Haase A1 - Guilin Qi A1 - Pascal Hitzler A1 - Steffen Stadtmüller ED - Lora Aroyo ED - Paolo Traverso ED - Fabio Ciravegna ED - Philipp Cimiano ED - Tom Heath ED - Eero Hyvönen ED - Riichiro Mizoguchi ED - Eyal Oren ED - Marta Sabou ED - Elena Paslaru Bontas Simperl AB -One 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.

JF - The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2009 PB - Springer CY - Heraklion, Crete, Greece VL - 5554 UR - http://dx.doi.org/10.1007/978-3-642-02121-3_71 ER -