5 citations · 19 across the 9 of their papers we have counts for
13 papers
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…
Matching with Transformers in MELT
Sven Hertling, Jan Portisch, Heiko Paulheim
One of the strongest signals for automated matching of ontologies and knowledge graphs are the textual descriptions of the concepts. The methods that are typically applied (such as…
Putting RDF2vec in Order
Jan Portisch, Heiko Paulheim
The RDF2vec method for creating node embeddings on knowledge graphs is based on word2vec, which, in turn, is agnostic towards the position of context words. In this paper, we argue…
Background Knowledge in Schema Matching: Strategy vs. Data
Jan Portisch, Michael Hladik, Heiko Paulheim
The use of external background knowledge can be beneficial for the task of matching schemas or ontologies automatically. In this paper, we exploit six general-purpose knowledge gra…