On the aggregation of rules for knowledge graph completion

Betz, Patrick ; Lüdtke, Stefan ; Meilicke, Christian ; Stuckenschmidt, Heiner

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URL: https://arxiv.org/abs/2309.00306v1
Additional URL: https://icml.cc/virtual/2023/27188
URN: urn:nbn:de:bsz:180-madoc-651458
Document Type: Conference presentation
Year of publication: 2023
Conference title: Knowledge and Logical Reasoning in the Era of Data-driven Learning Workshop@ICML 2023
Location of the conference venue: Honolulu, HI
Date of the conference: 28.07.2023
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
License: CC BY 4.0 Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Abstract: Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding one plausibility score for a candidate fact which was simultaneously predicted by multiple rules. Although the problem is ubiquitous, as data-driven rule learning can result in noisy and large rule sets, it is underrepresented in the literature and its theoretical foundations have not been studied before in this context. In this work, we demonstrate that existing aggregation approaches can be expressed as marginal inference operations over the predicting rules. In particular, we show that the common Max-aggregation strategy, which scores candidates based on the rule with the highest confidence, has a probabilistic interpretation. Finally, we propose an efficient and overlooked baseline which combines the previous strategies and is competitive to computationally more expensive approaches.

Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.

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