In this paper we introduce ODMiner, an automatic tool that enhances open datasets provided in heterogenous structured formats (e.g. JSON, CSV, XML, etc.) to Linked Open Data. ODMiner mines OD by recognising well known data types and formats (e.g., dates, emails, currencies, etc.) and by exploiting well known open linked datasets and vocabularies (e.g. DBpedia, WordNet, etc.) in order to extract named entities and relations between the open dataset elements. ODMiner is designed as modular and extensible software architecture and its process can be customised in order to address specific needs of final data representation and modelling. Finally, an evaluation of ODMiner with heterogenous multi-language OD datasets is provided in order to give evidence of its practical effectiveness.
Enhancing open data to Linked Open Data with ODMiner
Contributo in atti di convegno
M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen., Aachen, Germania
CEUR-WS.org, Aachen, DEU
Fourth International Workshop on Linked Data for Information Extraction co-located with 15th International Semantic Web Conference (ISWC 2016), pp. 44–50, Kobe, Japan, 18/10/2016