Measuring Similarity between Ontologies

By Alexander Maedche, Steffen Staab. International Conference Knowledge Engineering and Knowledge Management, 2002

931 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9685f6acafdbd034d78ab4c0666aa9d07559fdca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

Citations in the corpus

Understanding web images by object relation network (2012, methodology): “Given that ORN is an ontology instantiation, we employ the ontology distance measurement in [16] to compute ORN distances.”
Methods and tools for ontology evolution (2004, background): “Moreover, the final ontology is more similar to the source ontology [75] which makes easier for an ontology engineer to comprehend all effect of a required change.”
Measuring Similarity between Semantic Business Process Models (2007, methodology): “In contrast to the edit distance method the syntactic similarity measure of ( Maedche & Staab 2002 ) takes into account the number of characters and is inverse to the edit distance.”
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis (2005, methodology): “An interesting work in these lines is the one presented by Maedche and Staab (2002) in which ontologies are compared along different levels: semiotic, syntactic and pragmatic. In particular, the authors present measures to compare the lexical and taxonomic overlap between two ontologies. Furthermore”
Ontology Matching (2007, methodology): “The similarity among relations in (Mädche and Staab 2002) is computed according to this principle.”
A SURVEY OF ONTOLOGY EVALUATION TECHNIQUES (2005, methodology): “…fall into one of the following categories: • those based on comparing the ontology to a " golden standard " (which may itself be an ontology; e.g. MAEDCHE AND STAAB, 2002); • those based on using the ontology in an application and evaluating the results (e.g. PORZEL & MALAKA, 2004); • those…”
A Survey of Schema-Based Matching Approaches (2005, neutral): “This category refers to the algorithms which do not pretend to ﬁnd the solution to a problem (usually they just measure the distance) but rather to provide reasonable indications to a user for selecting the actual alignment.”
GMO: A Graph Matching for Ontologies (2005, methodology): “In general, with these approaches and some others [9], entity features are setup based on labeled graphs or RDF graphs, and entity similarity is computed by counting feature-matches based on Tversky’s contrast model [16], and then entity mapping is established based on (aggregated) similarities comp”