Prof. Dr. Felix Naumann

Data Driven Ontology Engineering and Knowledge Graph Construction

Knowledge Graphs (KGs) are widely used for systematic representation of real world data in form of entities and relations.  KGs rely on an underlying schema or ontology that consists of the concepts (that the entities are instances of) and their connections with each other. This ontology lays the ground rules for populating the KG with real world data. Ontologies encapsulate the necessary data semantics that can enable machine understanding of KG facts. Ontology design and construction is one of the first and most important steps for KG construction, yet it has largely remained a manual task, particularly for new domains. This project explores data-driven techniques for ontology learning and expansion that can in turn address the issue of lack of coverage of facts in KGs.


Learning Fine-Grained Semantics for Multi-Relational Data

The semantics of relations play a central role in the understanding and analysis of multi-relational data. Real-world relational datasets represented by knowledge graphs often contain polysemous relations between different types of entities, that represent multiple semantics. In this work, we present a data-driven method that can automatically discover the distinct semantics associated with high-level relations and derive an optimal number of sub-relations having fine-grained meaning. To this end, we perform clustering over vector representations of entities and relations obtained from knowledge graph embedding models.