Representation and reasoning of context-dependant knowledge in distributed fuzzy ontologies

Jiang, Yuncheng; Tang, Yong; Ju, Wang; Tang, Suqin

doi:10.1016/j.eswa.2010.02.122

Cited by 10 publications

(10 citation statements)

References 49 publications

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“…In fact, distributed ontology techniques [42][43][44] are presented and discussed in recent years. But they just focus on web ontology integration, mapping, and distributed reasoning.…”

Section: Related Workmentioning

confidence: 99%

Structured peer-to-peer-based publication and sharing of ontologies to automatically process SPARQL query on a semantic sensor network

Zheng

et al. 2018

International Journal of Distributed Sensor Networks

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In recent years, semantic sensor networks are proposed, which apply ontologies to provide query capabilities and data access and allow users to express their needs at a conceptual level. In such sensor networks, a large amount of web ontologies are separately created by sensors to represent their own knowledge. These ontologies are distributed in different sensors and provide knowledge for semantic queries. It has become a very pressing issue to locate the desired ontologies for a given semantic query. To address this issue, we propose an approach based on structured peer-to-peer protocol to publish shareable ontologies on different sensors and automatically discover the ontologies useful for a given SPARQL query. Therefore, if a SPARQL query is given, our approach can locate ontologies desired and further send the query to them to find out solutions for the query. In addition, if solutions can be found out from a web ontology published, our approach makes sure to discover the ontology and get the solutions from it for the query. We conduct three experiments to evaluate the approach, the results of which demonstrate that our approach is effective and efficient.

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“…In fact, distributed ontology techniques [42][43][44] are presented and discussed in recent years. But they just focus on web ontology integration, mapping, and distributed reasoning.…”

Section: Related Workmentioning

confidence: 99%

Structured peer-to-peer-based publication and sharing of ontologies to automatically process SPARQL query on a semantic sensor network

Zheng

et al. 2018

International Journal of Distributed Sensor Networks

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“…In addition, in the works [35][36][37][38], distributed ontology techniques are discussed. But, they just focus on mapping, integration, and reason of distributed ontology.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Sharing in Virtual Community Based on RDF Triple Publication and Retrieving To Process SPQRQL Query

Zhao

Lang

2014

JSW

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Web ontologies, which usually appear as RDF graphs, are used to represent knowledge in web. In recent years, with the wide application of Semantic Web, large numbers of such web ontologies have appeared on the Internet, especially, in some virtual knowledge communities. These web ontologies are usually distributed in different sites and provide an amount of knowledge to query. But, it has become a pressing issue that, given a semantic query, how to efficiently gather the related knowledge from these web ontologies located in different sites to process it. To address this issue, in this paper, we propose a P2P-based approach to publish RDF triples in sharable web ontologies and freely sharing them in an open distributed environment. Given a query of SPARQL (Simple Protocol and RDF Query Language), this approach can automatically gather published RDF triples related to the query to process the query. As a knowledge sharing approach, our approach can directly share RDF triples coming from different web ontologies on different nodes. It overcomes limitations of those approaches which focus on how to locate related ontologies for a query. We also conducted three experiments to evaluate the effectiveness and the efficiency of our approach. The experimental results demonstrated that it is effective and efficient.

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“…Nowadays, properties and semantics of ontology constructs mainly are determined by Description Logics (DLs) [35], a family of logics for representing structured knowledge which have proved to be very useful as ontology languages [42,38]. Formally, an ontology is a triple O = ⟨RB, T B, AB⟩, where RB (the Role Box or RBox) and T B (the Terminological Box or TBox) comprise the intensional knowledge, i.e., general knowledge about the world to be described (statements about roles and concepts, respectively), and AB (the Assertional Box or ABox) the extensional knowledge, i.e., particular knowledge about a specific instantiation of this world (statements about individuals in terms of concepts and roles) [42].…”

Section: Ontologies and Description Logicsmentioning

confidence: 99%

“…In other words, each parameter is only a word or a sentence, and expressive (or complex) parameters are not considered in soft sets [34]. In order to extend the expressive power of soft sets, Jiang et al [34] used the concepts of Description Logics (DLs) [35][36][37][38] to act as the parameters of soft sets. That is, an extended soft set theory based on DLs was presented in [34].…”

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confidence: 99%

Semantic decision making using ontology-based soft sets

Jiang

Liu

Tang

et al. 2011

Mathematical and Computer Modelling

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a b s t r a c tMolodtsov initiated the concept of soft set theory, which can be used as a generic mathematical tool for dealing with uncertainty. Description Logics (DLs) are a family of knowledge representation languages which can be used to represent the terminological knowledge of an application domain in a structured and formally well-understood way. To extend the expressive power of soft sets, ontology-based soft sets are presented by using the concepts of DLs to act as the parameters of soft sets. In this paper, we investigate soft set based decision making problems more deeply. Concretely, we first point out that the traditional approaches to (fuzzy) soft set based decision making are not fit to solve decision making problems involving user queries through some motivating examples. Furthermore, we present a novel approach to semantic decision making by using ontology-based soft sets and ontology (i.e., DL) reasoning. Lastly, the implementation method of semantic decision making is also discussed.

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Representation and reasoning of context-dependant knowledge in distributed fuzzy ontologies

Cited by 10 publications

References 49 publications

Structured peer-to-peer-based publication and sharing of ontologies to automatically process SPARQL query on a semantic sensor network

Structured peer-to-peer-based publication and sharing of ontologies to automatically process SPARQL query on a semantic sensor network

Knowledge Sharing in Virtual Community Based on RDF Triple Publication and Retrieving To Process SPQRQL Query

Semantic decision making using ontology-based soft sets

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