@article{FUTIA2020100516, title = "SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks", journal = "SoftwareX", volume = "12", pages = "100516", year = "2020", issn = "2352-7110", doi = "https://doi.org/10.1016/j.softx.2020.100516", url = "http://www.sciencedirect.com/science/article/pii/S2352711019302626", author = "Giuseppe Futia and Antonio Vetrò and Juan Carlos [De Martin]", keywords = "Knowledge Graphs, Semantic Modeling, Graph neural networks", abstract = "SeMi (SEmantic Modeling machIne) is a tool to semi-automatically build large-scale Knowledge Graphs from structured sources such as CSV, JSON, and XML files. To achieve such a goal, SeMi builds the semantic models of the data sources, in terms of concepts and relations within a domain ontology. Most of the research contributions on automatic semantic modeling is focused on the detection of semantic types of source attributes. However, the inference of the correct semantic relations between these attributes is critical to reconstruct the precise meaning of the data. SeMi covers the entire process of semantic modeling: (i) it provides a semi-automatic step to detect semantic types; (ii) it exploits a novel approach to inference semantic relations, based on a graph neural network trained on background linked data. At the best of our knowledge, this is the first technique that exploits a graph neural network to support the semantic modeling process. Furthermore, the pipeline implemented in SeMi is modular and each component can be replaced to tailor the process to very specific domains or requirements. This contribution can be considered as a step ahead towards automatic and scalable approaches for building Knowledge Graphs." }