02037nas a2200229   4500008004100000245004200041210004200083300001400125490000600139520143400145653002401579653001501603653002201618653000801640653002001648653001701668653002601685100002101711700001201732700002001744856004301764       2013                        eng d00aParaconsistent OWL and Related Logics0 aParaconsistent OWL and Related Logics  a395–4270 v43 aThe Web Ontology Language OWL is currently the most prominent formalism for representing ontologies in Semantic Web applications. OWL is based on description logics, and automated reasoners are used to infer knowledge implicitly present in OWL ontologies. However, because typical description logics obey the classical principle of explosion, reasoning over inconsistent ontologies is impossible in OWL. This is so despite the fact that inconsistencies are bound to occur in many realistic cases, e.g., when multiple ontologies are merged or when ontologies are created by machine learning or data mining tools. In this paper, we present four-valued paraconsistent description logics which can reason over inconsistencies. We focus on logics corresponding to OWL DL and its profiles. We present the logic SROIQ4, showing that it is both sound relative to classical SROIQ and that its embedding into SROIQ is consequence preserving. We also examine paraconsistent varieties of EL++, DL-Lite, and Horn-DLs. The general framework described here has the distinct advantage of allowing classical reasoners to draw sound but nontrivial conclusions from even inconsistent knowledge bases. Truth-value gaps and gluts can also be selectively eliminated from models (by inserting additional axioms into knowledge bases). If gaps but not gluts are eliminated, additional classical conclusions can be drawn without affecting paraconsistency.10aAutomated Deduction10aComplexity10aDescription Logic10aOWL10aParaconsistency10aSemantic Web10aWeb Ontology Language1 aMaier, Frederick1 aMa, Yue1 aHitzler, Pascal  uhttp://dx.doi.org/10.3233/SW-2012-006601617nas a2200229   4500008004100000245008100041210006900122300001100191490000600202520090100208653001401109653002901123653003001152653002901182653002301211100001201234700001501246700001701261700002001278700001701298856007201315       2010                        eng d00aComputational Complexity and Anytime Algorithm for Inconsistency Measurement0 aComputational Complexity and Anytime Algorithm for Inconsistency  a3–210 v43 a<p class="rtejustify">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</p>
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=1