5 papers
CQD-SHAP: Explainable Complex Query Answering via Shapley Values
Parsa Abbasi, Stefan Heindorf
Complex query answering (CQA) goes beyond the widely studied link prediction task by addressing more sophisticated queries that require multi-hop reasoning over incomplete knowledg…
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…
Ontolearn-A Framework for Large-scale OWL Class Expression Learning in Python
Caglar Demir, Alkid Baci, N'Dah Jean Kouagou +6
In this paper, we present Ontolearn-a framework for learning OWL class expressions over large knowledge graphs. Ontolearn contains efficient implementations of recent stateof-the-a…
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…
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…