Abstract
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/.
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Acknowledgements
This work has been supported by the German Federal Ministry of Education and Research (BMBF) within the project DAIKIRI under grant number 01IS19085B and the German Federal Ministry for Economic Affairs and Climate Action (BMWK) within the project RAKI under grant number 01MD19012B.
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Zahera, H.M. et al. (2022). Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings. In: Groth, P., et al. The Semantic Web: ESWC 2022 Satellite Events. ESWC 2022. Lecture Notes in Computer Science, vol 13384. Springer, Cham. https://doi.org/10.1007/978-3-031-11609-4_9
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DOI: https://doi.org/10.1007/978-3-031-11609-4_9
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