Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings

A large amount of data is generated every day by different systems and applications. In many cases, this data comes in a tabular format that lacks semantic representation and poses new challenges in data modelling. For semantic applications, it then becomes necessary to lift the data to a richer representation, such as a knowledge graph that adheres to a semantic ontology. We propose Tab2Onto, an unsupervised approach for learning ontologies from tabular data using knowledge graph embeddings, clustering, and a human in the loop. We conduct a set of experiments to investigate our approach on a benchmarking dataset from a medical domain and learn the ontology of diseases. Our code and datasets are provided at https://tab2onto.dice-research.org/.