activity
20222024
most citedLitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML2024

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…

cs.LG2024

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…

cs.AI20231 cited

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…

cs.LO2023

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…

cs.LG2022

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…