A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
This work addresses the choice between symbolic and distributional methods for relational learning, providing insights for researchers and practitioners in AI and data science, though it is incremental as it builds on existing paradigms.
The study compared symbolic and distributional paradigms for relational learning on classification and clustering tasks, analyzing rule complexity to identify indicators for selecting the best approach for specific knowledge graphs.
Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches, recent methods in (deep) representation learning has shown promising results for specialized tasks such as knowledge base completion. These approaches abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare representation learning and relational learning on various relational classification and clustering tasks and analyse the complexity of the rules used implicitly by these approaches. Preliminary results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs.