Document - Summarizing linked data RDF graphs using approximate graph pattern mining

2016

Contribution to conference Open Access

Summarizing linked data RDF graphs using approximate graph pattern mining

Zneika M., Lucchese C., Vodislav D., Kotzinos D.

Linked Open Data RDF Summarization Query Processing

The Linked Open Data (LOD) cloud brings together infor- mation described in RDF and stored on the web in (possi- bly distributed) RDF Knowledge Bases (KBs). The data in these KBs are not necessarily described by a known schema and many times it is extremely time consuming to query all the interlinked KBs in order to acquire the necessary in- formation. To tackle this problem, we propose a method of summarizing large RDF KBs using approximate RDF graph patterns and calculating the number of instances covered by each pattern. Then we transform the patterns to an RDF schema that describes the contents of the KB. Thus we can then query the RDF graph summary to identify whether the necessary information is present and if so its size, before deciding to include it in a federated query result.

Source: EDBT'16 - 19th International Conference on Extending Database Technology, pp. 684–685, Bordeaux, France, 15-18 March 2016

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@inproceedings{oai:it.cnr:prodotti:367079,
	title = {Summarizing linked data RDF graphs using approximate graph pattern mining},
	author = {Zneika M. and Lucchese C. and Vodislav D. and Kotzinos D.},
	doi = {10.5441/002/edbt.2016.86},
	booktitle = {EDBT'16 - 19th International Conference on Extending Database Technology, pp. 684–685, Bordeaux, France, 15-18 March 2016},
	year = {2016}
}