Merging Large Ontologies using BigData GraphDB

Nowaday the massive and growing production of digital data is the natural consequence of the use of ICT technologies in every aspects of human life. The benefits carried out by the digital revolution are innumerable both for users involved with the retrieval of useful and rich information and for companies which tries to have profits from data processing knowledge discovery. For this reason, data have a crucial role both from technology and economic-financial point of view. In fact, it is often not trivial to extract meaningful and reusable information from the large amount of available data for different reasons (e.g. unstructured data, lack of ability to process the size of data, standardless representation of data leading to heterogeneity). One of the most serious issues to consider in this context is heterogeneity, which can arise at different levels of abstraction, preventing a richer and more exhaustive retrieval of information. In our opinion, the matching and merging of various information sources become crucial process in order to create a comprehensive knowledge base smart applications. The use of pure formalisms, i.e. ontologies, is a good strategy to limit the gap originating from different sources. In this paper we propose a novel technique for matching and merging ontologies, which aims to create an integrated ontology based on standard representations of abstract concepts related to a particular domain. Then we present a real use of our methodology by means a use case implementing a graph-based knowledge base related to a complex domain related to cultural heritage.