20 citations · 34 across the 10 of their papers we have counts for
6 papers · 1 filter
Neural Reasoning for Robust Instance Retrieval in
Louis Mozart Kamdem Teyou, Luke Friedrichs, N'Dah Jean Kouagou +4
Concept learning exploits background knowledge in the form of description logic axioms to learn explainable classification models from knowledge bases. Despite recent breakthroughs…
Universal Knowledge Graph Embeddings
N'Dah Jean Kouagou, Caglar Demir, Hamada M. Zahera +4
A variety of knowledge graph embedding approaches have been developed. Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction sett…
LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals
Caglar Demir, Michel Wiebesiek, Renzhong Lu +2
Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete. Answering queries on such incomplete graphs is an important, but challenging problem. Recent…
Explaining Concept Descriptions through Counterfactual Reasoning
Leonie Nora Sieger, Stefan Heindorf, Yasir Mahmood +2
Knowledge bases are widely used for information management, enabling high-impact applications such as web search, question answering, and natural language processing. They also ser…
Neural Class Expression Synthesis
N'Dah Jean Kouagou, Stefan Heindorf, Caglar Demir +1
Many applications require explainable node classification in knowledge graphs. Towards this end, a popular ``white-box'' approach is class expression learning: Given sets of positi…
EvoLearner: Learning Description Logics with Evolutionary Algorithms
Stefan Heindorf, Lukas Blübaum, Nick Düsterhus +4
Classifying nodes in knowledge graphs is an important task, e.g., for predicting missing types of entities, predicting which molecules cause cancer, or predicting which drugs are p…