Formal and relational concept analysis for fuzzy-based automatic semantic annotation.
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| Title: | Formal and relational concept analysis for fuzzy-based automatic semantic annotation. |
|---|---|
| Authors: | Maio, C.1, Fenza, G.1, Gallo, M.1, Loia, V.1, Senatore, S.1 ssenatore@unisa.it |
| Source: | Applied Intelligence. Jan2014, Vol. 40 Issue 1, p154-177. 24p. |
| Subjects: | Semantic Web, Computer network resources -- Management, Ontology, Data analysis, Mathematical models |
| Abstract: | Semantic annotation is at the core of Semantic Web technology: it bridges the gap between legacy non-semantic web resource descriptions and their elicited, formally specified conceptualization, converting syntactic structures into knowledge structures, i.e., ontologies. Most existing approaches and tools are designed to deal with manual or semi-/automatic semantic annotation that exploits available ontologies through the pattern-based discovery of concepts. This work aims to generate the automatic semantic annotation of web resources, without any prefixed ontological support. The novelty of our approach is that, starting from web resources, content with a high-level of abstraction is obtained: concepts, connections between concepts, and instance-population are identified and arranged into an ex-novo ontology. The framework is designed to process resources from different sources (textual information, images, etc.) and generate an ontology-based annotation. A data-driven analysis reveals the data and their intrinsic relationships (in the form of triples) extracted from the resource content. On the basis of the discovered semantics, corresponding concepts and properties are modeled, allowing an ad hoc ontology to be built through an OWL-based coding annotation. The benefit of this approach is the generation of knowledge structured in a quite automatic way (i.e., the human support is restricted to the configuration of some parameters). The approach exploits a fuzzy extension of the mathematical modeling of Formal Concept Analysis and Relational Concept Analysis to generate the ontological structure of data resources. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 93629371 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10489-013-0451-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 154 Subjects: – SubjectFull: Semantic Web Type: general – SubjectFull: Computer network resources -- Management Type: general – SubjectFull: Ontology Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Mathematical models Type: general Titles: – TitleFull: Formal and relational concept analysis for fuzzy-based automatic semantic annotation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Maio, C. – PersonEntity: Name: NameFull: Fenza, G. – PersonEntity: Name: NameFull: Gallo, M. – PersonEntity: Name: NameFull: Loia, V. – PersonEntity: Name: NameFull: Senatore, S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 0924669X Numbering: – Type: volume Value: 40 – Type: issue Value: 1 Titles: – TitleFull: Applied Intelligence Type: main |
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