1.8k citations · 2.1k across the 54 of their papers we have counts for
58 papers
Contextual Augmentation for Entity Linking using Large Language Models
Daniel Vollmers, Hamada M. Zahera, Diego Moussallem +1
Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. Traditional methods use a two-step process with separate models for en…
Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues
Leandra Fichtel, Maximilian Spliethöver, Eyke Hüllermeier +9
The ability to generate explanations that are understood by explainees is the quintessence of explainable artificial intelligence. Since understanding depends on the explainee's ba…
Dung's Argumentation Framework: Unveiling the Expressive Power with Inconsistent Databases
Yasir Mahmood, Markus Hecher, Axel-Cyrille Ngonga Ngomo
The connection between inconsistent databases and Dung's abstract argumentation framework has recently drawn growing interest. Specifically, an inconsistent database, involving cer…
Aligning Generalisation Between Humans and Machines
Filip Ilievski, Barbara Hammer, Frank van Harmelen +22
Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt…
Native Execution of GraphQL Queries over RDF Graphs Using Multi-way Joins
Nikolaos Karalis, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo
Purpose: The query language GraphQL has gained significant traction in recent years. In particular, it has recently gained the attention of the semantic web and graph database comm…
HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs
Umair Qudus, Michael Roeder, Muhammad Saleem +1
We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have…
LOLA -- An Open-Source Massively Multilingual Large Language Model
Nikit Srivastava, Denis Kuchelev, Tatiana Moteu Ngoli +5
This paper presents LOLA, a massively multilingual large language model trained on more than 160 languages using a sparse Mixture-of-Experts Transformer architecture. Our architect…
MST5 -- Multilingual Question Answering over Knowledge Graphs
Nikit Srivastava, Mengshi Ma, Daniel Vollmers +3
Knowledge Graph Question Answering (KGQA) simplifies querying vast amounts of knowledge stored in a graph-based model using natural language. However, the research has largely conc…
Improving rule mining via embedding-based link prediction
N'Dah Jean Kouagou, Arif Yilmaz, Michel Dumontier +1
Rule mining on knowledge graphs allows for explainable link prediction. Contrarily, embedding-based methods for link prediction are well known for their generalization capabilities…
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…
Computing Repairs Under Functional and Inclusion Dependencies via Argumentation
Yasir Mahmood, Jonni Virtema, Timon Barlag +1
We discover a connection between finding subset-maximal repairs for sets of functional and inclusion dependencies, and computing extensions within argumentation frameworks (AFs). W…
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…
RED: a Filtered and Multilingual Relation Extraction Dataset
Pere-Lluís Huguet Cabot, Simone Tedeschi, Axel-Cyrille Ngonga Ngomo +1
Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural langu…
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…
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…
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…
Semantic Answer Type and Relation Prediction Task (SMART 2021)
Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo +5
Each year the International Semantic Web Conference organizes a set of Semantic Web Challenges to establish competitions that will advance state-of-the-art solutions in some proble…
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…