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Many real-world domains can be expressed as graphs and, more generally, asmulti-relational knowledge graphs. Though reasoning and learning with knowledgegraphs has traditionally been addressed by symbolic approaches, recent methodsin (deep) representation learning has shown promising results for specializedtasks such as knowledge base completion. These approaches abandon thetraditional symbolic paradigm by replacing symbols with vectors in Euclideanspace. With few exceptions, symbolic and distributional approaches are exploredin different communities and little is known about their respective strengthsand weaknesses. In this work, we compare representation learning and relationallearning on various relational classification and clustering tasks and analysethe complexity of the rules used implicitly by these approaches. Preliminaryresults reveal possible indicators that could help in choosing one approachover the other for particular knowledge graphs.

A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning