An Ontology Design Pattern for Representing Relevance in OWL

Abstract

Abstract. Design patterns are widely-used software engineering abstractions which define guidelines for modeling common application scenarios. Ontology design patterns are the extension of software patterns for knowledge acquisition in the Semantic Web. In this work we present a design pattern for representing relevance depending on context in OWL ontologies, i.e. to assert which knowledge from the domain ought to be considered in a given scenario. Besides the formal semantics and the features of the pattern, we describe a reasoning procedure to extract relevant knowledge in the resulting ontology and a plug-in for Protégé which assists pattern use.

An overview of fuzzy Description Logics for the Semantic Web

“…In Cheng et al (2008b), a conjunctive query algorithm over f-DLR-Lite F, \ KB was proposed, and the authors showed that the query answering algorithm over the extended DL is still FOL reducible and shows polynomial data complexity. (Haarslev et al, 2007) f-ALC O O Proposing some techniques of optimization DeLorean (Bobillo et al, 2007b) f (Habiballa, 2007) f-ALC O O Adding role negation, top role, and bottom role YADLR (Stasinos & Georgios, 2007) SLG algorithm O O Allowing to handle unknown degrees of truth in the fuzzy assertions of the knowledge base FuzzyDL f…”

“…In Cheng et al (2008b), a conjunctive query algorithm over f-DLR-Lite F, \ KB was proposed, and the authors showed that the query answering algorithm over the extended DL is still FOL reducible and shows polynomial data complexity. (Haarslev et al, 2007) f-ALC O O Proposing some techniques of optimization DeLorean (Bobillo et al, 2007b) f-SHOIN O O O O Reducing f-SHOIN to SHOIN to solve GERDS (Habiballa, 2007) f-ALC O O Adding role negation, top role, and bottom role YADLR (Stasinos & Georgios, 2007) SLG algorithm O O Allowing to handle unknown degrees of truth in the fuzzy assertions of the knowledge base FuzzyDL f-SHIF(D) O O O Supporting that the degree of a fuzzy assertion is a constant or a variable PDLP (Li et al, 2006c) PDLP O O A probability DL reasoner, supporting to handle uncertain information SoftFacts (Straccia, 2009c) SoftFacts O An ontology-mediated top-k retrieval system FRESG (Wang et al, 2009a) f-ALC(G) O O O Supporting fuzzy data information…”

Fostering Serendipitous Knowledge Discovery using an Adaptive Multigraph-based Faceted Browser

“…In [20], a framework is proposed for explicitly modeling analogical structures in multi-relational or knowledge graph embedding. Another possible strategy is to analyze ontology design patterns instead of concrete entity-similarity metrics to represent relevance between entities in one context to entities in another context [5]. F 12 : Support extending the memory of user by invoking provocative reminders and relevance feedback.…”

A literature overview of knowledge sharing between Petri nets and ontologies

“…(2012) proposed a fuzzy OWL ontology approach for representing FPNs, where they map the key features of FPNs into fuzzy classes, fuzzy properties, fuzzy individuals and fuzzy axioms of fuzzy OWL ontologies. By means of the existing fuzzy ontology reasoner DeLoean (Bobillo & Delgado, 2007), reasoning on FPNs is implemented.…”

“…On the other hand, applying the modeling and analyzing techniques of Petri nets to represent ontologies will also attract much attention. The implementation of more expressive ontology reasoners or the enhancement of the reasoning ability of Petri nets with ontologies will be investigated in depth. As shown in this paper, there have been several existing reasoning techniques such as DL (Lu et al ., 2007), SHOIN (Hustadt et al ., 2004), SWRL (Zhang et al ., 2006) to reason about information that is contained in Petri nets, and some reasoning tasks may be done automatically by means of the existing ontology reasoners DeLoean (Bobillo & Delgado, 2007), and FLORA-2 (Yang et al ., 2003). But, most of them suffer some limitations and are not deep enough. The implementation of Petri net execution in the context of Semantic Web service.…”