PDF《DL-Lite》

Forgetting for Distance-based Reasoning and Repair in DL-Lite Xiaowang Zhang School of Computer Science and Technology, Tianjin University, Tianjin, China Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China Abstract In this paper, we present a forgetting-based approach to handling inconsisten- cy in DL-Lite.

Our proposed approach can not only characterize distance-based reasoning, which is proven to rationally draw meaningful conclusions even from inconsistent DL-Lite knowledge bases but also recovery the consistency of DL- Lite knowledge bases. We ?rst present vectors forgetting for DL-Lite by ex- tending predicates forgetting in DL-Lite and show the predicates to be forgotten which are obtained by computing minimal hitting sets. Moreover, we develop al- gorithms to compute those predicates by employing Reiter'

s HS-tree method and then analyze the computational complexity of those proposed algorithms. Final- ly, we implement our proposed algorithms and evaluate them on both consistent and inconsistent ontologies. Besides, we discuss some applications of vectors forgetting in privacy protection. Keywords: Semantic Web, description logics, knowledge management, distance-based semantics, ontology reasoning 1. Introduction The DL-Lite [8] is a family of lightweight description logics (DLs), the log- ical foundation of OWL

2 QL, one of the three pro?les of OWL

2 for Web on- tology language recommended by W3C [10, 40, 27, 6]. In description logics, an ontology is expressed as a knowledge base (KB). Inconsistency is not rare in ontology applications and can be caused by several reasons, such as errors in modeling, migration from other formalisms, ontology merging, and ontology Email address: xiaowangzhang@tju.edu.cn (Xiaowang Zhang) Preprint submitted to Knowledge-based Systems June 13,

2016 evolution [39, 7, 28]. Therefore, handling inconsistency of ontologies is always considered an important problem in DL and ontology management communities [4, 35]. However, DL-Lite reasoning mechanism based on classical DL seman- tics faces problem when inconsistency occurs, which is referred to as the trivial- ity problem [12, 19]. That is, any conclusion, that is possibly irrelevant or even contradictory, will be entailed from an inconsistent DL-Lite ontology under the classical semantics [38, 36]. There exist several proposals for reasoning with inconsistent DL-Lite KBs in the literature. These approaches usually fall into one of two fundamentally different streams. The ?rst one is based on the assumption that inconsistencies are caused by erroneous data and thus, they should be removed in order to obtain a consistent KB [20, 31, 11, 13, 49, 33]. In most approaches in this stream, the task of repairing inconsistent ontologies is actually reduced to ?nding a maxi- mum consistent subset of the original KB. A shortcoming of these approaches is the so-called multi-extension problem. That is, in many cases, an inconsis- tent KB may have several different sub-KBs that are maximum consistent. The other stream, based on the idea of living with inconsistency, is to introduce a for- m of paraconsistent reasoning or inconsistency-tolerant reasoning by employing non-standard reasoning methods (e.g., non-standard inference and non-classical semantics). [37, 18, 26, 21, 17, 45] have introduced some strategies to select consistent subsets from an inconsistent KB as substitutes of the original KB in reasoning. [37] proposes a fuzzy semantics. [30] presents the Belnap'

s four- valued semantics of DLs where two additional logical values besides true and false are introduced to indicate contradictory conclusions. Inference power of the four-valued semantics is further enhanced by a new quasi-classical semantics for DLs proposed in [47], which is a generalization of Hunter'