Abstract
The open nature of Knowledge Graphs (KG) often implies that they are incomplete. Link prediction consists in inferring new links between the entities of a KG based on existing links. Most existing approaches rely on the learning of latent feature vectors for the encoding of entities and relations. In general however, latent features cannot be easily interpreted. Rule-based approaches offer interpretability but a distinct ruleset must be learned for each relation, and computation time is difficult to control. We propose a new approach that does not need a training phase, and that can provide interpretable explanations for each inference. It relies on the computation of Concepts of Nearest Neighbours (CNN) to identify similar entities based on common graph patterns. Dempster-Shafer theory is then used to draw inferences from CNNs. We evaluate our approach on FB15k-237, a challenging benchmark for link prediction, where it gets competitive performance compared to existing approaches.
This research is supported by ANR project PEGASE (ANR-16-CE23-0011-08).
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Notes
- 1.
Companion page: http://www.irisa.fr/LIS/ferre/pub/link_prediction/.
- 2.
We also ran it with advanced parameters on a 8-core server under AMIE+’s authors guidance. That led to many more rules but did not improve the results.
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I warmly thank Luis Galárraga for his support about AMIE+.
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Ferré, S. (2019). Link Prediction in Knowledge Graphs with Concepts of Nearest Neighbours. In: Hitzler, P., et al. The Semantic Web. ESWC 2019. Lecture Notes in Computer Science(), vol 11503. Springer, Cham. https://doi.org/10.1007/978-3-030-21348-0_6
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