5 citations · 19 across the 11 of their papers we have counts for
5 papers · 1 filter
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions
Russa Biswas, Jan Portisch, Heiko Paulheim +2
The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task o…
The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings
Jan Portisch, Heiko Paulheim
Knowledge graph embedding is a representation learning technique that projects entities and relations in a knowledge graph to continuous vector spaces. Embeddings have gained a lot…
KERMIT -- A Transformer-Based Approach for Knowledge Graph Matching
Sven Hertling, Jan Portisch, Heiko Paulheim
One of the strongest signals for automated matching of knowledge graphs and ontologies are textual concept descriptions. With the rise of transformer-based language models, text co…
Ontology Matching Through Absolute Orientation of Embedding Spaces
Jan Portisch, Guilherme Costa, Karolin Stefani +3
Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledg…
Walk this Way! Entity Walks and Property Walks for RDF2vec
Jan Portisch, Heiko Paulheim
RDF2vec is a knowledge graph embedding mechanism which first extracts sequences from knowledge graphs by performing random walks, then feeds those into the word embedding algorithm…