By Li Yan, Zongmin Ma, Fu Zhang, Jingwei Cheng
This e-book is going to nice intensity in regards to the speedy transforming into subject of applied sciences and techniques of fuzzy good judgment within the Semantic net. the subjects of this e-book contain fuzzy description logics and fuzzy ontologies, queries of fuzzy description logics and fuzzy ontology wisdom bases, extraction of fuzzy description logics and ontologies from fuzzy facts types, garage of fuzzy ontology wisdom bases in fuzzy databases, fuzzy Semantic internet ontology mapping, and fuzzy ideas and their interchange within the Semantic net. The e-book goals to supply a unmarried checklist of present study within the fuzzy wisdom illustration and reasoning for the Semantic internet. the target of the publication is to supply the cutting-edge details to researchers, practitioners and graduate scholars of the internet intelligence and while serve the data and knowledge engineering specialist confronted with non-traditional purposes that make the appliance of traditional ways tough or most unlikely.
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Additional info for Fuzzy Knowledge Management for the Semantic Web (Studies in Fuzziness and Soft Computing)
U(FS) ` u(FC1) Y u(FC2) and u(FS) ` u(FC2) Y u(FC1). although, for the case (2) of Definition five. sixteen, i. e. , whilst periods should not have subclass/superclass courting yet they've got comparable items 5. 2 Fuzzy Description common sense Extraction 137 with varied club levels, it can't be dealt with without delay through checking even if u(FS) ` u(FC1) u(FC2). for that reason, the constraint that sessions should not have subclass/superclass dating might be reasoned through checking no matter if u(FS) 2 u(FC1) Y u(FC2) and u(FS) 2 u(FC2) Y u(FC1). moreover, no matter if periods include comparable gadgets is also checked by means of detecting no matter if thoughts u(FC1) and u(FC2) have a similar contributors as pointed out above. additionally, Theorem five. 14 is the easy results of Definitions five. sixteen, and the evidence is hereby passed over the following. five. three Fuzzy Ontology Extraction from Fuzzy information types Ontology extraction (i. e. , development of ontologies) is a crucial factor within the context of the Semantic net. The Semantic net goals at growing ontologybased and machine-processable web pages, and hence the luck and proliferation of the Semantic internet mostly depends upon developing ontologies (Berners-Lee et al. 2001). To this finish, forms of techniques and instruments were constructed to build ontologies from a number of information assets reminiscent of textual content, Dictionary, XML records, database versions, and so on. (Corcho et al. 2003; Maedche and Steffen 2001). in spite of the fact that, classical ontology building ways aren't enough for dealing with vague and unsure details that's in general present in many program domain names. accordingly, nice efforts on building of fuzzy ontologies were made lately. developing fuzzy ontologies could facilitate the bushy ontology improvement and the knowledge sharing within the context of the Semantic internet, and likewise act as a gap-bridge of the belief of semantic interoperations among the present purposes and the Semantic net. For this goal, a number of ways were built for developing fuzzy ontologies in accordance with the Formal proposal research (FCA) conception (Chen et al. 2009; De Maio et al. 2009; Quan et al. 2006). a few ways target at developing fuzzy ontologies from the knowledge resources, corresponding to fuzzy narrower phrases (Widyantoro and Yen 2001), fuzzy relatives (Gu et al. 2007), and and so on. lately, developing fuzzy ontologies from fuzzy database versions has obtained a lot awareness. specifically, as pointed out in Chap. three, because the early Nineteen Eighties, Zadeh’s fuzzy common sense (Zadeh 1965) has been used to increase a variety of info types. This ended in various contributions, in general with recognize to the preferred fuzzy information types corresponding to fuzzy ER versions, fuzzy UML types, fuzzy relational database versions, and fuzzy object-oriented database versions. even though, such details and data in fuzzy information types can't perform the Semantic net without delay. To this finish, Ma et al. (2010) and Zhang et al. (2008a) awarded a fuzzy ontology development method from fuzzy ER version.