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An Embedding-based Approach to Rule Learning from Knowledge Graphs


Ho,  Vinh Thinh
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International max planck research school, MPI for Informatics, Max Planck Society;


Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;


Stepanova,  Daria
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Ho, V. T. (2018). An Embedding-based Approach to Rule Learning from Knowledge Graphs. Thesis, Universität des Saarlandes, Saarbrücken.

Cite as: http://hdl.handle.net/21.11116/0000-0001-DE06-F
Knowledge Graphs (KGs) play an important role in various information systems and have application in many fields such as Semantic Web Search, Question Answering and Information Retrieval. KGs present information in the form of entities and relationships between them. Modern KGs could contain up to millions of entities and billions of facts, and they are usually built using automatic construction methods. As a result, despite the huge size of KGs, a large number of facts between their entities are still missing. That is the reason why we see the importance of the task of Knowledge Graph Completion (a.k.a. Link Prediction), which concerns the prediction of those missing facts. Rules over a Knowledge Graph capture interpretable patterns in data and various methods for rule learning have been proposed. Since KGs are inherently incomplete, rules can be used to deduce missing facts. Statistical measures for learned rules such as confidence reflect rule quality well when the KG is reasonably complete; however, these measures might be misleading otherwise. So, it is difficult to learn high-quality rules from the KG alone, and scalability dictates that only a small set of candidate rules is generated. Therefore, the ranking and pruning of candidate rules are major problems. To address this issue, we propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, we iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and optionally external information sources including text corpora. The contributions of this thesis are as follows: • We introduce a framework for rule learning guided by external sources. • We propose a concrete instantiation of our framework to show how to learn high- quality rules by utilizing feedback from a pretrained embedding model. • We conducted experiments on real-world KGs that demonstrate the effectiveness of our novel approach with respect to both the quality of the learned rules and fact predictions that they produce.