20 citations · 40 across the 26 of their papers we have counts for
28 papers
Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs
Rupesh Sapkota, Louis Mozart Kamdem Teyou, Moshood Yekini +2
In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledge graphs often contain rich…
Syntactic Simplification of OWL Class Expressions
Alkid Baci, N'Dah Jean Kouagou, Caglar Demir +1
Class expression learning often produces complex OWL class expressions that are difficult to interpret and reason over. However, by following theoretically grounded simplification…
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…
Inference over Unseen Entities, Relations and Literals on Knowledge Graphs
Caglar Demir, N'Dah Jean Kouagou, Arnab Sharma +1
In recent years, knowledge graph embedding models have been successfully applied in the transductive setting to tackle various challenging tasks including link prediction, and quer…
Embedding Knowledge Graph in Function Spaces
Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo
We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing…
Performance Evaluation of Knowledge Graph Embedding Approaches under Non-adversarial Attacks
Sourabh Kapoor, Arnab Sharma, Michael Röder +2
Knowledge Graph Embedding (KGE) transforms a discrete Knowledge Graph (KG) into a continuous vector space facilitating its use in various AI-driven applications like Semantic Searc…
Adaptive Stochastic Weight Averaging
Caglar Demir, Arnab Sharma, Axel-Cyrille Ngonga Ngomo
Ensemble models often improve generalization performances in challenging tasks. Yet, traditional techniques based on prediction averaging incur three well-known disadvantages: the…
Embedding Knowledge Graphs in Degenerate Clifford Algebras
Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo
Clifford algebras are a natural generalization of the real numbers, the complex numbers, and the quaternions. So far, solely Clifford algebras of the form (i.e., algebra…
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…
Learning Permutation-Invariant Embeddings for Description Logic Concepts
Caglar Demir, Axel-Cyrille Ngonga Ngomo
Concept learning deals with learning description logic concepts from a background knowledge and input examples. The goal is to learn a concept that covers all positive examples, wh…
Hardware-agnostic Computation for Large-scale Knowledge Graph Embeddings
Caglar Demir, Axel-Cyrille Ngonga Ngomo
Knowledge graph embedding research has mainly focused on learning continuous representations of knowledge graphs towards the link prediction problem. Recently developed frameworks…
Kronecker Decomposition for Knowledge Graph Embeddings
Caglar Demir, Julian Lienen, Axel-Cyrille Ngonga Ngomo
Knowledge graph embedding research has mainly focused on learning continuous representations of entities and relations tailored towards the link prediction problem. Recent results…
Conformal Credal Self-Supervised Learning
Julian Lienen, Caglar Demir, Eyke Hüllermeier
In semi-supervised learning, the paradigm of self-training refers to the idea of learning from pseudo-labels suggested by the learner itself. Across various domains, corresponding…
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…
Learning Concept Lengths Accelerates Concept Learning in ALC
N'Dah Jean Kouagou, Stefan Heindorf, Caglar Demir +1
Concept learning approaches based on refinement operators explore partially ordered solution spaces to compute concepts, which are used as binary classification models for individu…
DRILL-- Deep Reinforcement Learning for Refinement Operators in
Caglar Demir, Axel-Cyrille Ngonga Ngomo
Approaches based on refinement operators have been successfully applied to class expression learning on RDF knowledge graphs. These approaches often need to explore a large number…
Convolutional Hypercomplex Embeddings for Link Prediction
Caglar Demir, Diego Moussallem, Stefan Heindorf +1
Knowledge graph embedding research has mainly focused on the two smallest normed division algebras, and . Recent results suggest that trilinear products of…