LinkGen: Multipurpose Linked Data Generator

Joshi, Amit Krishna; Hitzler, Pascal; Dong, Guozhu

doi:10.1007/978-3-319-46547-0_12

Cited by 7 publications

(1 citation statement)

References 16 publications

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“…Each pair of the resource is compared two times dealing with the bidirectional similarity degrees. In [13], the authors provide an option for interlinking resources using the manual approach. With this approach, the entities from real-world datasets have to be entered by the user.…”

Section: Related Workmentioning

confidence: 99%

RDF Link Generation by Exploring Related Links on the Web of Data

Sharma¹,

Marjit²,

Biswas³

2018

IJITCS

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Interlinking RDF resources is a vital aspect of the Semantic Web technology. It is the basis of Linked Data that provides interlinked datasets on the web. One of the principles of Linked Data is interlinking resources from different data sources on the web. Data interlinking is a critical and challenging problem that every Linked Data generation applications face. Various approaches have been evolved for resolving this problem, but, for more massive datasets, it becomes almost indefinite time while linking similar or related resources. Linking RDF resources is like the problem of entity matching, record matching or duplicate resource detection. More or less they attempt to point to the same problem, but the RDF link generation is the task of finding related resources on the web. In this article, we present an approach for generating RDF links using the similarity measure between two RDF resources and by exploring associated relationships of the matched resources. The idea is to find related resources and link them with an RDF resource that is being generated.

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Section: Related Workmentioning

confidence: 99%