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.

1 aZhou, Zhangquan1 aQi, Guilin1 aLiu, Chang1 aHitzler, Pascal1 aMutharaju, Raghava1 aGodo, Lluis1 aPrade, Henri1 aQi, Guilin uhttps://daselab.cs.ksu.edu/publications/scale-reasoning-fuzzy-el-ontologies-based-mapreduce00520nam a2200169 4500008004100000245002600041210002600067260003000093100002000123700002200143700002300165700001300188700001500201700001700216700001500233856010200248 2013 eng d00a语义Web技术基础0 a语义Web技术基础 bTsinghua University Press1 aHitzler, Pascal1 aKrötzsch, Markus1 aRudolph, Sebastian1 aYu, Yong1 aQi, Guilin1 aWang, Haofen1 aLiu, Chang uhttps://daselab.cs.ksu.edu/publications/%E8%AF%AD%E4%B9%89web%E6%8A%80%E6%9C%AF%E5%9F%BA%E7%A1%8001175nas a2200265 4500008004100000245005600041210005400097260003500151300001400186490000800200520040900208100002000617700001500637700001500652700002000667700002300687700001800710700002500728700001900753700002100772700002000793700002000813700002400833856005200857 2012 eng d00aReasoning with Fuzzy-EL+ Ontologies Using MapReduce0 aReasoning with FuzzyEL Ontologies Using MapReduce aMontpellier, FrancebIOS Press a933–9340 v2423 aFuzzy 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.

1 aZhou, Zhangquan1 aQi, Guilin1 aLiu, Chang1 aHitzler, Pascal1 aMutharaju, Raghava1 aDe Raedt, Luc1 aBessière, Christian1 aDubois, Didier1 aDoherty, Patrick1 aFrasconi, Paolo1 aHeintz, Fredrik1 aLucas, Peter, J. F. uhttp://dx.doi.org/10.3233/978-1-61499-098-7-93302117nas a2200145 4500008004100000245007000041210006900111300001600180490000700196520167700203100001201880700001501892700002001907856004401927 2011 eng d00aComputing Inconsistency Measure based on Paraconsistent Semantics0 aComputing Inconsistency Measure based on Paraconsistent Semantic a1257–12810 v213 aMeasuring 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.

1 aMa, Yue1 aQi, Guilin1 aHitzler, Pascal uhttp://dx.doi.org/10.1093/logcom/exq05301617nas a2200229 4500008004100000245008100041210006900122300001100191490000600202520090100208653001401109653002901123653003001152653002901182653002301211100001201234700001501246700001701261700002001278700001701298856007201315 2010 eng d00aComputational Complexity and Anytime Algorithm for Inconsistency Measurement0 aComputational Complexity and Anytime Algorithm for Inconsistency a3–210 v43 aMeasuring 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

10aalgorithm10acomputational complexity10ainconsistency measurement10aKnowledge representation10amulti-valued logic1 aMa, Yue1 aQi, Guilin1 aXiao, Guohui1 aHitzler, Pascal1 aLin, Zuoquan uhttp://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41&flag=100474nas a2200145 4500008004100000245007400041210006900115300001000184490000700194100002400201700002000225700002000245700001500265856004800280 2010 eng d00aPreface - Special issue on commonsense reasoning for the semantic web0 aPreface Special issue on commonsense reasoning for the semantic a1–20 v581 avan Harmelen, Frank1 aHerzig, Andreas1 aHitzler, Pascal1 aQi, Guilin uhttp://dx.doi.org/10.1007/s10472-010-9209-701441nas a2200205 4500008004100000245006500041210006200106260003000168300001200198490000900210520084600219100001201065700001501077700001701092700002001109700001701129700002601146700001301172856005001185 2009 eng d00aAn Anytime Algorithm for Computing Inconsistency Measurement0 aAnytime Algorithm for Computing Inconsistency Measurement aVienna, AustriabSpringer a29–400 v59143 aMeasuring 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.

1 aMa, Yue1 aQi, Guilin1 aXiao, Guohui1 aHitzler, Pascal1 aLin, Zuoquan1 aKaragiannis, Dimitris1 aJin, Zhi uhttp://dx.doi.org/10.1007/978-3-642-10488-6_701371nas a2200301 4500008004100000245005400041210005200095260003900147300001400186490000900200520052000209100001200729700001700741700001500758700002000773700002600793700001600819700002000835700002100855700002100876700001500897700001900912700002400931700001500955700001700970700003100987856005101018 2009 eng d00aRaDON - Repair and Diagnosis in Ontology Networks0 aRaDON Repair and Diagnosis in Ontology Networks aHeraklion, Crete, GreecebSpringer a863–8670 v55543 aOne 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.

1 aJi, Qiu1 aHaase, Peter1 aQi, Guilin1 aHitzler, Pascal1 aStadtmüller, Steffen1 aAroyo, Lora1 aTraverso, Paolo1 aCiravegna, Fabio1 aCimiano, Philipp1 aHeath, Tom1 aHyvönen, Eero1 aMizoguchi, Riichiro1 aOren, Eyal1 aSabou, Marta1 aSimperl, Elena, Paslaru Bo uhttp://dx.doi.org/10.1007/978-3-642-02121-3_71