Rough Ontology Research Articles

Research on the Rough Extension of Ontology Description Language of SWRL

Although ontology has strong ability to express knowledge, it is difficult to express uncertain or imprecise information using the language of ontology. In order to improve the ability to express uncertain information, this paper extends the Semantic Web Rule Language (SWRL) and gives the extension of reasoning rules. According to the rough ontology and the rules of SWRL, it updates the knowledge base. Firstly, the concept of rough ontology and the extension of rough relationship of ontology are put forward; secondly, it gives the extension method for concepts, relationships, axioms, examples, and rules of SWRL. Finally, a psychological counseling case shows that the method can well express the uncertainty of knowledge, and it is able to well express the reasoning rules.

A Semantic Information Retrieval Approach Based on Rough Ontology

With the emergence of the semantic web, ontology has attracted a great deal of attention in field of informa- tion retrieval. But the conceptual formalism supported by typical ontology is not sufficient for handling incomplete infor- mation that is confronted in the real world knowledge. To tackle this problem, a semantic information retrieval approach based on a rough ontology is proposed. Rough ontology in this paper is in the form of an ontology information system. Given a keyword based query, our approach infers the individuals and properties correlated to the query through a proce- dure of association searches in the rough ontology, and takes properties as equivalence relations to construct an approxi- mation space of rough ontology. Afterward, an algorithm of computing similarity in rough ontology is presented, and ap- proximation space is employed to compute similarity for ranking documents in semantic document indexing space. The proposed approach has been compared with two other information retrieval techniques, and the experiments conducted on CNKI collections, support the better efficacy which results from our approach.

D eL orean: A reasoner for fuzzy OWL 2

Today, there is a growing interest in the development of knowledge representations able to deal with uncertainty, which is a very common requirement in real world applications. Despite the undisputed success of ontologies, classical ontologies are not suitable to deal with uncertainty and, consequently, several extensions with fuzzy logic and rough logic, among other formalisms, have been proposed. In this article we describe D eL orean 2, the first ontology reasoner that supports fuzzy extensions of the standard languages OWL and OWL 2. In a strict sense, D eL orean is not a reasoner but a translator from fuzzy rough ontology languages ( GZ SROIQ ( D ) ) into classical ontology languages ( SROIQ ( D ) ) . This allows using classical (widely available) Description Logic inference engines to reason with the representation resulting from the transformation. We describe the main features of the application: evolution, functionality, architecture, graphical interface, input language, and implementation details.

Decision Process Knowledge Acquisition through Integration of Rough Set and Ontology Technology

In order to realize the sharing and reuse of process knowledge, an intelligent approach for process knowledge acquisition based on rough set theory and ontology technology was proposed. The framework of process knowledge acquisition was put forward and process knowledge acquisition system was defined. An information complementation method was presented and the algorithm for automating process knowledge acquisition was designed. Moreover, ontology technology was applied in process knowledge representation to resolve the problem of process knowledge reliability and consistency, and the similarity computing approach of knowledge ontology was presented.

Rough Mereology in Information Systems with Applications to Qualitative Spatial Reasoning

Rough Mereology has been proposed as a paradigm for approximate reasoning in complex information systems. Its primitive notion is that of a predicate of rough inclusion which gives for any two entities of discourse the degree in which one of them is a part of the other. Rough Mereology may be regarded as an extension of Rough Set Theory as it proposes to argue in terms of similarity relations induced from a rough inclusion instead of reasoning in terms of more strict indiscernibility relations. Rough Mereology is also a generalization of Mereology i.e. a theory of reasoning based on the notion of a part. Classical languages of mathematics are of two-fold kind: the language of set theory (naive or formal) expressing classes of objects as sets consisting of ”elements”, ”points” etc. suitable for objects perceived as built of ”atoms” and applied to structures perceived as discrete and the language of part relations suitable for e.g. continuous objects like solids, regions, etc. where two objects are related to each other by saying that one of them is a part of the other. Mereological theories for reasoning about complex structures are at the heart of Qualitative Spatial Reasoning. In this paper, we study basic aspects of Rough Mereology in Information Systems. Mereology makes the distinction between entities perceived as individuals (singletons), to which the part predicate may be applied, and entities perceived as distributive classes (sets, lists, general names etc.) of entities. This distinction is made formal and precise within Ontology i.e. Theory of Being based on the primitive notion of the copula is which is also a basic ingredient of theories for Spatial Reasoning. The practical aim of Ontology is to elaborate a system of concepts (notions, names, sets of entities) about which the reasoning is carried out. Therefore, we begin our study with an analysis of a simple rough set-based Ontology (the template ontology) in Information Systems and in this setting we present our approach to Mereology in Information Systems. In this framework we introduce Rough Mereology and we present some ways for defining rough inclusions. We demonstrate applications of Rough Mereology to approximate reasoning taking as the case subject Qualitative Spatial Reasoning. We address here some of its mereo-topological as well as mereo-geometrical aspects.