Abstract
Knowledge graphs (KGs) are key tools in many AI-related tasks such as reasoning or question answering. This has, in turn, propelled research in link prediction in KGs, the task of predicting missing relationships from the available knowledge. Solutions based on KG embeddings have shown promising results in this matter. On the downside, these approaches are usually unable to explain their predictions. While some works have proposed to compute post-hoc rule explanations for embedding-based link predictors, these efforts have mostly resorted to rules with unbounded atoms, e.g., \(\textit{bornIn}(x,y) \Rightarrow \textit{residence}(x,y)\), learned on a global scope, i.e., the entire KG. None of these works has considered the impact of rules with bounded atoms such as \(\textit{nationality}(x,\textit{England}) \Rightarrow \textit{speaks}(x, \textit{English})\), or the impact of learning from regions of the KG, i.e., local scopes. We therefore study the effects of these factors on the quality of rule-based explanations for embedding-based link predictors. Our results suggest that more specific rules and local scopes can improve the accuracy of the explanations. Moreover, these rules can provide further insights about the inner-workings of KG embeddings for link prediction.
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Notes
- 1.
Most methods embed the entities in real spaces, i.e., in \(\mathbb {R}^k\), but a few, e.g.,[31] resort to vectors of complex numbers.
- 2.
\(\oplus \) denotes concatenation; sub-contexts are corrupted to obtain counter-examples.
- 3.
These are safe rules where each variable occurs in at least 2 atoms.
- 4.
Rule (8), on the other hand, refers to a women’s football team.
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Acknowledgment
This research was supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No. 952215.
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Galárraga, L. (2023). Effects of Locality and Rule Language on Explanations for Knowledge Graph Embeddings. In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_12
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