Identifying relevant sources for data linking using a semantic web index

Abstract

With more data repositories constantly being published on theWeb, choosing appropriate data sources to interlink with newly published datasets becomes a non-trivial problem. While catalogs of data repositories and meta-level descrip- Tors such as VoiD provide valuable information to take these decisions, more detailed information about the instances in- cluded into repositories is often required to assess the rel- evance of datasets and the part of the dataset to link to. However, retrieving and processing such information for a potentially large number of datasets is practically unfeasible. In this paper, we examine how using an existing semantic web index can help identifying candidate datasets for link- ing. We further apply ontology schema matching techniques to rank these candidate datasets and extract the sub-dataset to use for linking, in the form of classes with instances more likely to match the ones of the local dataset.