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Application of first-order logic selection techniques to knowledge graphs

When answering queries on large predicate logical knowledge bases, automated theorem provers are often unable to process the entire knowledge base. Even loading a very large knowledge base can overwhelm an automated theorem prover. However, normally only a very small part of the knowledge base is needed to construct a proof.
For this reason, selection techniques have become established in this area that try to extract the part of the knowledge base that is needed for a proof for a given query. A very successful selection technique is SInE [1], which is used by many proofs.
The topic of this bachelor thesis is to transfer the idea of the SInE selection is to knowledge graphs. The developed selection method shall be implemented and evaluated on realistic knowledge graphs. Furthermore, it should be investigated, if the developed selection technique is suitable to exact for a given topic a subgraph containing information on this topic.

1. K. Hoder and A. Voronkov. Sine qua non for large theory reasoning. In N. Bjørner and V. Sofronie-Stokkermans, editors, Automated Deduction – CADE-23, volume 6803 of Lec- ture Notes in Computer Science, pages 299–314. Springer Berlin Heidelberg, 2011.