66 citations · 80 across the 26 of their papers we have counts for
26 papers
Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Hamid Ahmad, Heiko Paulheim, Rita T. Sousa
Knowledge graphs and ontologies represent entities and their relationships in a structured way, having gained significance in the development of modern AI applications. Integrating…
gpuRDF2vec -- Scalable GPU-based RDF2vec
Martin Böckling, Heiko Paulheim
Generating Knowledge Graph (KG) embeddings at web scale remains challenging. Among existing techniques, RDF2vec combines effectiveness with strong scalability. We present gpuRDF2ve…
ExeKGLib: A Platform for Machine Learning Analytics based on Knowledge Graphs
Antonis Klironomos, Baifan Zhou, Zhipeng Tan +4
Nowadays machine learning (ML) practitioners have access to numerous ML libraries available online. Such libraries can be used to create ML pipelines that consist of a series of st…
Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks
Martin Böckling, Heiko Paulheim, Andreea Iana
Large Language Models (LLMs) have showcased impressive reasoning abilities, but often suffer from hallucinations or outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augment…
GeoRDF2Vec Learning Location-Aware Entity Representations in Knowledge Graphs
Martin Boeckling, Heiko Paulheim, Sarah Detzler
Many knowledge graphs contain a substantial number of spatial entities, such as cities, buildings, and natural landmarks. For many of these entities, exact geometries are stored wi…
ReaLitE: Enrichment of Relation Embeddings in Knowledge Graphs using Numeric Literals
Antonis Klironomos, Baifan Zhou, Zhuoxun Zheng +3
Most knowledge graph embedding (KGE) methods tailored for link prediction focus on the entities and relations in the graph, giving little attention to other literal values, which m…