Training Neural Language Models with SPARQL queries for Semi-Automatic Semantic Mapping

Knowledge graphs are labeled and directed multi-graphs that encode information in the form of entities and relationships. They are gaining attention in different areas of computer science: from the improvement of search engines to the development of virtual personal assistants. Currently, an open challenge in building large-scale knowledge graphs from structured data available on the Web (HTML tables, CSVs, JSONs) is the semantic integration of heterogeneous data sources. In fact, such diverse and scattered information rarely provide a formal description of metadata that is required to accomplish the integration task. In this paper we propose an approach based on neural networks to reconstruct the semantics of data sources to produce high quality knowledge graphs in terms of semantic accuracy. We developed a neural language model trained on a set of SPARQL queries performed on knowledge graphs. Through this model it is possible to semi-automatically generate a semantic map between the attributes of a data source and a domain ontology.