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20122020
most citedReinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion

31 citations · 34 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.AI20241 cited

Reevaluation of Inductive Link Prediction

Simon Ott, Christian Meilicke, Heiner Stuckenschmidt

Within this paper, we show that the evaluation protocol currently used for inductive link prediction is heavily flawed as it relies on ranking the true entity in a small set of ran…

cs.AI2020

xRAI: Explainable Representations through AI

Christiann Bartelt, Sascha Marton, Heiner Stuckenschmidt

We present xRAI an approach for extracting symbolic representations of the mathematical function a neural network was supposed to learn from the trained network. The approach is ba…

cs.AI202031 cited

Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion

Christian Meilicke, Melisachew Wudage Chekol, Manuel Fink +1

Most of todays work on knowledge graph completion is concerned with sub-symbolic approaches that focus on the concept of embedding a given graph in a low dimensional vector space.…

cs.AI2015

Towards Log-Linear Logics with Concrete Domains

Melisachew Wudage Chekol, Jakob Huber, Heiner Stuckenschmidt

We present (M denotes Markov logic networks) an extension of the log-linear description logics -LL with concrete domains, nominals, and inst…

cs.AI20123 cited

Evaluating Ontology Matching Systems on Large, Multilingual and Real-world Test Cases

Christian Meilicke, Ondrej Sváb-Zamazal, Cássia Trojahn +4

In the field of ontology matching, the most systematic evaluation of matching systems is established by the Ontology Alignment Evaluation Initiative (OAEI), which is an annual camp…