Publication | Closed Access
A survey of current Link Discovery frameworks
207
Citations
33
References
2016
Year
Network ScienceInformation RetrievalData ScienceData MiningLink Discovery FrameworksEngineeringRecord LinkageKnowledge DiscoveryData IntegrationDifferent FrameworksLink PredictionComputer ScienceLink AnalysisLink TypeSemantic WebLinked DataData ManagementLinked Data Cloud
Links build the backbone of the Linked Data Cloud. With the steady growth in size of datasets comes an increased need for end users to know which frameworks to use for deriving links between datasets. In this survey, we comparatively evaluate current Link Discovery tools and frameworks. For this pu rpose, we outline general requirements and derive a generic architecture of Link Discovery frameworks. Based on this generic architecture, we study and compare the features of state-of-the-art linking frameworks. We also analyze reported performance evaluations for the different frameworks. Finally, we derive insights pertaining to possible future developments in the domain of Link Discovery.
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