A Pattern-Based Framework for Uncertainty Representation in Ontologies

Vacura, Miroslav; Svátek, Vojtěch; Smrž, Pavel

doi:10.1007/978-3-540-87391-4_30

Cited by 9 publications

(8 citation statements)

References 4 publications

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“…A pattern for uncertainty representation in ontologies has also been presented [22]. However, it relies in OWL Full, thus not making possible for instance to check the syntactic correctness of the fuzzy ontology.…”

Section: Discussion and Related Workmentioning

confidence: 99%

An OWL Ontology for Fuzzy OWL 2

Bobillo

Straccia

2009

Lecture Notes in Computer Science

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Abstract. The need to deal with vague information in Semantic Web languages is rising in importance and, thus, calls for a standard way to represent such information. We may address this issue by either extending current Semantic Web languages to cope with vagueness, or by providing an ontology describing how to represent such information within Semantic Web languages. In this work, we follow the latter approach and propose and discuss an OWL ontology to represent important features of fuzzy OWL 2 statements.

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Section: Discussion and Related Workmentioning

confidence: 99%

An OWL Ontology for Fuzzy OWL 2

Bobillo

Straccia

2009

Lecture Notes in Computer Science

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“…A pattern for uncertainty representation in ontologies has been presented in [20]. However, it is restricted to a subset of our case 7, only for axioms (A1).…”

Section: Related Workmentioning

confidence: 99%

Representing fuzzy ontologies in OWL 2

Bobillo

Straccia

2010

International Conference on Fuzzy Systems

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The need to deal with vague information in Semantic Web languages is rising in importance and, thus, calls for a standard way to represent such information. We may address this issue by either extending current Semantic Web languages to cope with vagueness, or by providing a procedure to represent such information within current Semantic Web languages. In this work, we follow the latter approach, by identifying the syntactic differences that a fuzzy ontology language has to cope with, and by proposing a concrete methodology to represent fuzzy ontologies using OWL 2 annotation properties. I. INTRODUCTIONIt is widely agreed that classical ontology languages are not appropriate to deal with vagueness or imprecision in the knowledge, which is inherent to most of the real world application domains [19]. Since fuzzy set theory and fuzzy logic [21] are suitable formalisms to handle these types of knowledge, fuzzy ontologies emerge as useful in several applications, ranging from (multimedia) information retrieval to image interpretation, ontology mapping, matchmaking, decision making, or the Semantic Web.Description Logics (DLs) are the basis of several ontology languages. The current standard for ontology representation is OWL (Web Ontology Language), which comprises three sublanguages (OWL Lite, OWL DL and OWL Full). OWL 2 is a recent W3C recommendation [11]. The logical counterparts of OWL Lite, OWL DL and OWL 2 are the DLs SHIF(D), SHOIN (D), and SROIQ(D), respectively.Several fuzzy extensions of DLs can be found in the literature (see the survey in [10]) and some fuzzy DL reasoners have been implemented, such as FUZZYDL [4], DELOREAN [1] and FIRE [12]. Not surprisingly, each reasoner uses its own fuzzy DL language for representing fuzzy ontologies and, thus, there is a need for a standard way to represent such information.In this work, as we do not expect a fuzzy OWL extension to become a W3C proposed standard in the near future, we identify the syntactic differences that a fuzzy ontology language has to cope with, and propose to use OWL 2 itself to represent fuzzy ontologies. More precisely, we use OWL 2 annotation properties to encode fuzzy SROIQ(D) ontologies. The use of annotation properties makes possible (i) to use current OWL 2 editors for fuzzy ontology representation, and (ii) that OWL 2 reasoners discard the fuzzy part of a fuzzy ontology, producing the same results as if would not exist.The remainder of this paper is organized as follows. In Section II we present a fuzzy extension of DL SROIQ(D),

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“…For an extensive overview the reader is referred to [133]. Further relevant proposals include the pattern-based approach for representing and reasoning with fuzzy knowledge [145], and the generic, formalised approach for managing uncertainty proposed by Helaoui et al [63] use log-linear DLs to develop a probabilistic ontological framework to hierarchically recognise multi-user complex activities. Recent works have started exploring the applicability of such initiatives in the domain of pervasive applications; an example is the approach presented in [114], where fuzzy ontologies are applied to human activity recognition to deal with sensor unreliability and activity specification imprecision.…”

Section: Imperfect Knowledgementioning

confidence: 99%

Semantic web technologies in pervasive computing: A survey and research roadmap

Dasiopoulou

Stevenson

et al. 2015

Pervasive and Mobile Computing

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Pervasive and sensor-driven systems are by nature open and extensible, both in terms of input and tasks they are required to perform. Data streams coming from sensors are inherently noisy, imprecise and inaccurate, with di↵ering sampling rates and complex correlations with each other. These characteristics pose a significant challenge for traditional approaches to storing, representing, exchanging, manipulating and programming with sensor data. Semantic Web technologies provide a uniform framework for capturing these properties. O↵ering powerful representation facilities and reasoning techniques, these technologies are rapidly gaining attention towards facing a range of issues such as data and knowledge modelling, querying, reasoning, service discovery, privacy and provenance. This article reviews the application of the Semantic Web to pervasive and sensor-driven systems with a focus on information modelling and reasoning along with streaming data and uncertainty handling. The strengths and weaknesses of current and projected approaches are analysed and a roadmap is derived for using the Semantic Web as a platform, on which open, standard-based, pervasive, adaptive and sensor-driven systems can be deployed.

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A Pattern-Based Framework for Uncertainty Representation in Ontologies

Cited by 9 publications

References 4 publications

An OWL Ontology for Fuzzy OWL 2

An OWL Ontology for Fuzzy OWL 2

Representing fuzzy ontologies in OWL 2

Semantic web technologies in pervasive computing: A survey and research roadmap

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