Difference between revisions of "Fuzzy Semantic Similarity in Linked Data Using Wikipedia Infobox"
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| + | | title = Fuzzy Semantic Similarity in Linked Data Using Wikipedia Infobox | ||
| + | | date = 2013 | ||
| + | | authors = [[Parisa D. Hossein Zadeh]]<br />[[Marek Reformat]] | ||
| + | | doi = 10.1109/IFSA-NAFIPS.2013.6608433 | ||
| + | | link = http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6608433&punumber%3D6596206%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6608358%29%26pageNumber%3D3 | ||
| + | }} | ||
'''Fuzzy Semantic Similarity in Linked Data Using Wikipedia Infobox''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Parisa D. Hossein Zadeh]] and [[Marek Reformat]]. | '''Fuzzy Semantic Similarity in Linked Data Using Wikipedia Infobox''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Parisa D. Hossein Zadeh]] and [[Marek Reformat]]. | ||
== Overview == | == Overview == | ||
The problem of [[semantic similarity]] assessment arises in several applications, for example, knowledge management, information integration, and information discovery. In this article, authors present a new method that evaluates similarity between entities represented by Resource Description Framework (RDF) triples introduced in the context of the Semantic Web. At the beginning, approach identifies and groups properties according to their importance. It is done via exploiting the information presented in [[Wikipedia]] [[infoboxes]]. Then semantic similarity corresponding to each group is calculated using both the schema ([[ontology]] classes and properties) and RDF links discovered from different datasets (due to the open and distributed nature of data). Finally, the calculated similarity [[measures]] for all groups are aggregated using weights obtained from a specially designed fuzzy membership function. Experimental evaluations confirm the suitability of the proposed method. | The problem of [[semantic similarity]] assessment arises in several applications, for example, knowledge management, information integration, and information discovery. In this article, authors present a new method that evaluates similarity between entities represented by Resource Description Framework (RDF) triples introduced in the context of the Semantic Web. At the beginning, approach identifies and groups properties according to their importance. It is done via exploiting the information presented in [[Wikipedia]] [[infoboxes]]. Then semantic similarity corresponding to each group is calculated using both the schema ([[ontology]] classes and properties) and RDF links discovered from different datasets (due to the open and distributed nature of data). Finally, the calculated similarity [[measures]] for all groups are aggregated using weights obtained from a specially designed fuzzy membership function. Experimental evaluations confirm the suitability of the proposed method. | ||
Revision as of 12:28, 19 October 2019
Authors | Parisa D. Hossein Zadeh Marek Reformat |
|---|---|
Publication date | 2013 |
DOI | 10.1109/IFSA-NAFIPS.2013.6608433 |
Links | Original |
Fuzzy Semantic Similarity in Linked Data Using Wikipedia Infobox - scientific work related to Wikipedia quality published in 2013, written by Parisa D. Hossein Zadeh and Marek Reformat.
Overview
The problem of semantic similarity assessment arises in several applications, for example, knowledge management, information integration, and information discovery. In this article, authors present a new method that evaluates similarity between entities represented by Resource Description Framework (RDF) triples introduced in the context of the Semantic Web. At the beginning, approach identifies and groups properties according to their importance. It is done via exploiting the information presented in Wikipedia infoboxes. Then semantic similarity corresponding to each group is calculated using both the schema (ontology classes and properties) and RDF links discovered from different datasets (due to the open and distributed nature of data). Finally, the calculated similarity measures for all groups are aggregated using weights obtained from a specially designed fuzzy membership function. Experimental evaluations confirm the suitability of the proposed method.
