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pro vyhledávání: '"Meilicke, Christian"'
Publikováno v:
In: Rules and Reasoning. RuleML+RR 2024. Lecture Notes in Computer Science, vol 15183. Springer, Cham (2024)
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 randomly sampled negative entities. Due to the limited size of the set of
Externí odkaz:
http://arxiv.org/abs/2409.20130
Autor:
Gastinger, Julia, Meilicke, Christian, Errica, Federico, Sztyler, Timo, Schuelke, Anett, Stuckenschmidt, Heiner
Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evaluation protocols and rigorous comparison across TKG models are availabl
Externí odkaz:
http://arxiv.org/abs/2404.16726
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
Externí odkaz:
http://arxiv.org/abs/2309.00306
Publikováno v:
3rd Conference on Automated Knowledge Base Construction (AKBC 2021)
Neural embedding-based machine learning models have shown promise for predicting novel links in knowledge graphs. Unfortunately, their practical utility is diminished by their lack of interpretability. Recently, the fully interpretable, rule-based al
Externí odkaz:
http://arxiv.org/abs/2109.08002
Neural embedding-based machine learning models have shown promise for predicting novel links in biomedical knowledge graphs. Unfortunately, their practical utility is diminished by their lack of interpretability. Recently, the fully interpretable, ru
Externí odkaz:
http://arxiv.org/abs/2012.05750
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. Against this trend, we propose an approach called AnyBURL that is root
Externí odkaz:
http://arxiv.org/abs/2004.04412
Knowledge bases contribute to many web search and mining tasks, yet they are often incomplete. To add missing facts to a given knowledge base, various embedding models have been proposed in the recent literature. Perhaps surprisingly, relatively simp
Externí odkaz:
http://arxiv.org/abs/1810.07180
Publikováno v:
In Information Systems September 2022 108
Autor:
Schoenfisch, Joerg, von Stulpnagel, Janno, Ortmann, Jens, Meilicke, Christian, Stuckenschmidt, Heiner
IT infrastructure is a crucial part in most of today's business operations. High availability and reliability, and short response times to outages are essential. Thus a high amount of tool support and automation in risk management is desirable to dec
Externí odkaz:
http://arxiv.org/abs/1511.05719
Autor:
Meilicke, Christian, Sváb-Zamazal, Ondrej, Trojahn, Cássia, Jiménez-Ruiz, Ernesto, Aguirre, José-Luis, Stuckenschmidt, Heiner, Grau, Bernardo Cuenca
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 campaign for evaluating ontology matching systems organized by different g
Externí odkaz:
http://arxiv.org/abs/1208.3148