… an ontology in a vector space, and use the vector space … is utilized to model an ontology in
a vector space, and then, their … the similarity calculation and entity matching process. After that, …

… to perform string matching between class labels using character … best alignment. Our results
show that we obtain state-of-the-art performance on ontologies from the Ontology Alignment …

… vector space structure in a hyperbolic space, we have to map embeddings to the tangent
space, perform the aggregation in the tangent space… back to the hyperbolic space. Furthermore, …

… similar ontology alignment methods. Section 3 introduces the definition of ontology and ontology
alignment … The Vector Space Model method considers the transformation of text content …

… We show that adding ontological matching does not make reasoning of OGKs harder:
validation and implication problems are both NP-complete. We extended Chase to characterize …

… Ontology matching is an essential task for the management of semantic heterogeneity in …
ontology matching and ontology evolution. As defined in the survey presented in [127], ontology …

… The use of word embedding is also promising … alignment by jointly embedding ontologies
in a vector space [21]. An even more effective use of word embedding for ontology alignment is …

… , the vector spaces of the two to-be-aligned ontologies are … cross-ontology classes may still
have a large vector distance. … ., re-using known mappings from other aligned ontologies. For …

… [39] proposed a new similarity measure that modeled two ontologies in a vector space and
used the cosine distance to calculate two entities’ similarity, and on this basis, they used EA …

… The objective of such embeddings is to represent in a vector space KG components such
as entities and relations in a way that captures the structure of the graph. Various kinds of KG …