2018/05/23 by Matthias Jurisch, Jurisch, Matthias, Bodo Igler +1 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Artificial Intelligence (cs.AI) #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Semantic Web and Ontologies #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.1805.09145
6 pages, accepted at Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies (DL4KGS)
arxiv created 2018/05/23 · openalex publication_date 2018/05/23 · arxiv updated 2018/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
When ontologies cover overlapping topics, the overlap can be represented using ontology alignments. These alignments need to be continuously adapted to changing ontologies. Especially for large ontologies this is a costly task often consisting of manual work. Finding changes that do not lead to an adaption of the alignment can potentially make this process significantly easier. This work presents an approach to finding these changes based on RDF embeddings and common classification techniques. To examine the feasibility of this approach, an evaluation on a real-world dataset is presented. In this evaluation, the best classifiers reached a precision of 0.8.