Learning with Knowledge Graphs

In recent years a number of large-scale triple-oriented knowledge graphs have been generated. They are being used in research and in applications to support search, text understanding and question answering. Knowledge graphs pose new challenges for machine learning, and research groups have developed novel statistical models that can be used to compress knowledge graphs, to derive implicit facts, and to detect errors in the knowledge graph. In this paper we decribe the concept of triple-oriented knowledge graphs and corresponding learning approaches. We also discuss episodic knowledge graphs which are able to represent temporal data; learning with episodic data can be the basis for decision support systems, e.g. in a clinical context. Finally we discuss how knowledge graphs can support perception, by mapping subsymbolic sensory inputs, such as images, to semantic triples. A particular feature of our approach would be that perception, episodic memory and semantic memory are highly interconnected and that, in a cognitive interpretation, all rely on the same brain structures. 1 Semantic Knowledge Graphs A technical realization of a semantic memory is a knowledge graph (KG) which is a triple-oriented knowledge representation: A labelled link implies a (subject, predicate, object) statement where subject and object are entities that are represented as the nodes in the graph and where the predicate labels the link from subject to object. Large KGs have been developed that support search, text understanding and question answering [8]. A KG can be represented as a tensor which maps indices to true or false