Using Node Embeddings to Generate Recommendations for Semantic Model Creation

With the ongoing digitalization and the resulting growth in digital heterogeneous data, it is becoming increasingly important for enterprises to manage and control this data. An approach that has established itself over the past years for managing heterogeneous data is the creation and use of knowledge graphs. However, creating a knowledge graph requires the generation of a semantic mapping in the form of a semantic model between datasets and a corresponding ontology. Even though the creation of semantic models can be partially automated nowadays, manual adjustments to the created models are often required, as otherwise no reliable results can be achieved in many real-world use cases. In order to support the user in the refinement of those automatically created models, we propose a content-based recommender system that, based on the present semantic model, automatically suggests concepts that reasonably complement or complete the present semantic model. The system utilizes node embeddings to extract semantic concepts from a set of existing semantic models and utilize these in the recommendation. We evaluate accuracy and usability of our approach by performing synthetic modeling steps upon selected datasets. Our results show that our recommendations are able to identify additional concepts to improve auto-generated semantic models.