7 papers
Learning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs
Alkid Baci, Luke Friedrichs, Caglar Demir +2
Knowledge graph embedding (KGE) models perform well on link prediction but struggle with unseen entities, relations, and especially literals, limiting their use in dynamic, heterog…
Semantics-Aware Caching for Concept Learning
Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo
Concept learning is a form of supervised machine learning that operates on knowledge bases in description logics. State-of-the-art concept learners often rely on an iterative searc…
OWLAPY: A Pythonic Framework for OWL Ontology Engineering
Alkid Baci, Luke Friedrichs, Caglar Demir +1
In this paper, we introduce OWLAPY, a comprehensive Python framework for OWL ontology engineering. OWLAPY streamlines the creation, modification, and serialization of OWL 2 ontolog…
Parameter Averaging in Link Prediction
Rupesh Sapkota, Caglar Demir, Arnab Sharma +1
Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE)…
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…