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Predicting Human Associations with Graph Patterns Learned from Linked Data

Jörn Hees; Rouven Bauer; Joachim Folz; Damian Borth; Andreas Dengel
In: ISWC Poster & Demos. International Semantic Web Conference (ISWC-2017), October 21-25, Vienna, Austria, CEUR-WS proceedings, 2017.


The datasets provided by the Linked Data community currently form the world's largest, freely available, decentralised and interlinked knowledge bases. However, to be able to benefit from this knowledge in a specific use-case, one typically needs to understand the modelling of the knowledge and formulate appropriate SPARQL queries. In order to ease this process, we developed an evolutionary algorithm that learns such SPARQL queries (graph patterns) for pairwise relations between source and target entities. Given a training list of source-target-pairs, our algorithm learns a predictive model, which given a new source entity predicts target entities analogously to the training examples. In this demo paper we present a high level overview over our graph pattern learner and show its application to simulate human associations (e.g., "fish - water"). In the demo users can choose a semantic entity (e.g., dbr:Fish) as stimulus and let the learned model predict human-like responses (e.g., dbr:Water).


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