User validation is one of the challenges facing the ontology alignment community, as there are limits to the quality of the alignments produced by automated alignment algorithms. In …
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model …
In this paper, we present MELT-ML, a machine learning extension to the Matching and EvaLuation Toolkit (MELT) which facilitates the application of supervised learning for …
Popular cross-domain knowledge graphs, such as DBpedia and YAGO, are built from Wikipedia, and therefore similar in coverage. In contrast, Wikifarms like Fandom contain …
POMap++ is a novel ontology matching system based on a machine learning approach. This year is the second participation of POMap++ in the Ontology Alignment Evaluation Initiative …
Ontology alignment has been studied for over a decade, and over that time many alignment systems have been developed by researchers in order to find simple 1-to-1 equivalence …
Matching learning corresponds to the combination of ontology matching and machine learning techniques. This strategy has gained increasing attention in recent years. However …
Formal Concept Analysis (FCA) is a well-developed mathematical model for clustering individuals and structuring concepts. In one of our previous studies, we proposed to …
U Serles, D Fensel - An Introduction to Knowledge Graphs, 2024 - Springer
Another important quality dimension for knowledge graphs is completeness. We explain how we can enrich an existing knowledge graph with new knowledge. First, we present …