Abstract
AbstractThis paper introduces Wikipedia as an extensive knowledge base which provides additional information about a great number of web resources in the semantic web, and shows how RDF web resources in the web of data can be linked to this encyclopedia. Given an input web resource, the designed system identifies the topic of the web resource and links it to the corresponding Wikipedia article. To perform this task, we use the core labeling properties in web of data to specify the candidate Wikipedia articles for a web resource. Finally, a knowledge based approach is used to identify the most appropriate article in Wikipedia database. Evaluation of the system shows the high performance of the designed system.
Highlights
In recent years, the web has evolved from a global information space of linked documents to a space where both documents and data are linked [1]
This paper introduces Wikipedia as an extensive knowledge base which provides additional information about a great number of web resources in the semantic web, and shows how RDF web resources in the web of data can be linked to this encyclopedia
The last parts of the URL addresses of Wikipedia articles are used to identify the title of the web resource
Summary
The web has evolved from a global information space of linked documents to a space where both documents and data are linked [1]. Various datasets have been published in the web of data following linked data principles [2] including DBPedia, DBLP, Freebase, GeoSpecies, etc. The RDF links in linked Data generate triples where the subject and object resources are URI references in the namespace of initial dataset and target dataset respectively [8] and the predicate part shows the relationship between them. These predicates are semantic terms which are selected from ontologies. Ontologies introduce different terms to represent various meanings; for example, owl:sameAs, and rdf:type show the equality relationships and type relationship between two entities respectively
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