S-Match: an Algorithm and an Implementation of Semantic Matching

By Fausto Giunchiglia, Pavel Shvaiko, Mikalai Yatskevich. ESWS, 2004

619 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/416e3607ebd972ad794974315eac3d44b5470f01

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

1. Pavel Shvaiko
2. Jérôme Euzenat
3. Ihab F. Ilyas
4. Jun Xu
5. Nan Tang
6. Hang Li
7. Sanjay Krishnan
8. Mourad Ouzzani

In Computer Networks and Communications

1. Pavel Shvaiko
2. Jérôme Euzenat
3. Ihab F. Ilyas
4. Fausto Giunchiglia
5. M. Stonebraker
6. Grigoris Antoniou
7. Giorgos Flouris
8. Dimitris Plexousakis

In Information Systems

1. Pavel Shvaiko
2. Jérôme Euzenat
3. Ihab F. Ilyas
4. Jun Xu
5. Hang Li
6. Nan Tang
7. Fausto Giunchiglia
8. Mourad Ouzzani

Citations in the corpus

• Ontology Matching (2007, methodology): “re-implementation of CtxMatch with a few added functionalities (Giunchiglia et al. 2004).”
• Ten Challenges for Ontology Matching (2008, background): “Once it has been realized that the approach is promising (based on the preliminary evaluation in [34]), the efficiency problems have been tackled.”
• LogMap: Logic-Based and Scalable Ontology Matching (2011, background): “, S-Match [10]), in practice reasoning is known to aggravate the scalability problem (e.”
• Semantic Matching in Search (2014, background): “, two database schemas, semantic matching is defined as an operator that identifies the nodes in the two structures which semantically correspond to each other [73].”
• Ontology change: classification and survey (2008, methodology): “To the best of our knowledge, there is only one intensional method considering asymmetric relations, namely S-MATCH (Giunchiglia et al. 2004); S-MATCH identifies equivalence, more general, less general, mismatch and overlapping relations between concepts and uses a lot of single matchers: 13 linguist”
• Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery (2018, methodology): “Tools such as COMA [32], Harmony [33], S-Match [34] and CLIO [35] have been developed for schema matching.”
• A Survey of Schema-Based Matching Approaches (2005, neutral): “Taxonomy-based techniques are also graph algorithms which consider only the specialization relation.”
• Semantic Matching: Algorithms and Implementation (2007, methodology): “A vision of the semantic matching approach and some of its implementation were reported in [20,21,25].”