Rule-based Knowledge Graph Completion with Canonical Models

Simon Ott (Autor:in und Vortragende:r), Patrick Betz, Daria Stepanova, Mohamed H. Gad-Elrab, Christian Meilicke, Heiner Stuckenschmidt

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung

Abstract

Rule-based approaches have proven to be an efficient and explainable method for knowledge base completion. Their predictive quality is on par with classic knowledge graph embedding models such as TransE or ComplEx, however, they cannot achieve the results of neural models proposed recently. The performance of a rule-based approach depends crucially on the solution of the rule aggregation problem, which is concerned with the computation of a score for a prediction that is generated by several rules. Within this paper, we propose a supervised approach to learn a reweighted confidence value for each rule to get an optimal explanation for the training set given a specific aggregation function. In particular, we apply our approach to two aggregation functions: We learn weights for a noisy-or multiplication and apply logistic regression, which computes the score of a prediction as a sum of these weights. Due to the simplicity of both models the final score is fully explainable. Our experimental results show that we can significantly improve the predictive quality of a rule-based approach. We compare our method with current state-of-the-art latent models that lack explainability, and achieve promising results.
OriginalspracheEnglisch
TitelProceedings of the 32nd ACM International Conference on Information and Knowledge Management
Seiten1971-1981
DOIs
PublikationsstatusVeröffentlicht - 21 Okt. 2023
VeranstaltungCIKM '23: The 32nd ACM International Conference on Information and Knowledge Management - Birmingham , United Kingdom , Birmingham , Großbritannien/Vereinigtes Königreich
Dauer: 21 Okt. 202325 Okt. 2023

Publikationsreihe

Name32nd ACM International Conference on Information and Knowledge Management
Herausgeber (Verlag)Association for Computing Machinery

Konferenz

KonferenzCIKM '23: The 32nd ACM International Conference on Information and Knowledge Management
Land/GebietGroßbritannien/Vereinigtes Königreich
StadtBirmingham
Zeitraum21/10/2325/10/23

Research Field

  • Ehemaliges Research Field - Data Science

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