20 citations · 40 across the 26 of their papers we have counts for
13 papers · 1 filter
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…
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)…
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…
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…
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…
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…
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…
Out-of-Vocabulary Entities in Link Prediction
Caglar Demir, Axel-Cyrille Ngonga Ngomo
Knowledge graph embedding techniques are key to making knowledge graphs amenable to the plethora of machine learning approaches based on vector representations. Link prediction is…
A shallow neural model for relation prediction
Caglar Demir, Diego Moussallem, Axel-Cyrille Ngonga Ngomo
Knowledge graph completion refers to predicting missing triples. Most approaches achieve this goal by predicting entities, given an entity and a relation. We predict missing triple…
Convolutional Complex Knowledge Graph Embeddings
Caglar Demir, Axel-Cyrille Ngonga Ngomo
In this paper, we study the problem of learning continuous vector representations of knowledge graphs for predicting missing links. We present a new approach called ConEx, which in…