COMA - A System for Flexible Combination of Schema Matching Approaches

By Hong Hai Do, Erhard Rahm. Very Large Data Bases Conference, 2002

1,347 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e6a7faf05f1284af3ecd925e3c1efd4c02f1989e

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Citations in the corpus

Ontology Matching: State of the Art and Future Challenges (2013, background): “Another worthwhile direction to avoid exhaustive pair-wise comparisons, which appears to be particulary promising when handling large ontologies, is based on segment-based approaches, e.g., COMA++ [79] and Anchor-Flood ( § 4.6), thus targeting at matching only the similarly enough segments.”
Semantic Matching: Algorithms and Implementation (2007, methodology): “Many diverse solutions of match have been proposed so far, see [43,12,40,42] for recent surveys, while some examples of individual approaches addressing the matching problem can be found in [1,2,5,6,10,11,13,16,30,32,33,35,39]1.”
Ontology Matching (2007, methodology): “The most prominent criteria are precision and recall originating from information retrieval (van Rijsbergen 1975) and adapted to ontology matching (Do et al. 2002).”
Ontology matching: A literature review (2015, methodology): “GOMMA (Hartung, Kolb, Groß, & Rahm, 2013) uses several matchers to evaluate both the lexical and structural similarity of the entities from the input ontologies.”
Translating Web Data (2002, methodology): “In addition, automated techniques for schema matching have proven to be very successful in extracting such correspondences (see the recent work of [6] as well as [18] for a survey of techniques including CUPID, LSD and DIKE).”
Meteor-s web service annotation framework (2004, methodology): “Rather, we focus on two of the more relevant schema matching techniques and their relationship to our work, namely, COMA [32] and Cupid [33].”
Data integration: the teenage years (2006, background): “Second, based on the observation that none of the above techniques is foolproof, the next development involved systems that combined a set of individual techniques to create mappings [31, 32].”
Simlarity Search for Web Services (2004, background): “Schema matching: The database community has considered the problem of automatically matching schemas [24] [12] [13] [22].”
Corpus-based schema matching (2005, background): “They either use previous mappings to map multiple data sources to a single mediated schema [4], or compose known mappings to a common schema [3].”
A Survey of Schema-Based Matching Approaches (2005, positive): “Distinct features of the COMA tool in respect to Cupid, are a more ﬂexible architecture and a possibility of performing iterations in the matching process.”
iMAP: discovering complex semantic matches between database schemas (2004, background): “As examples of searchers, a text searcher may consider only matches that are concatenations of text attributes, while a numeric searcher considers combining attributes with arithmetic expressions.”
LinkedGeoData: A core for a web of spatial open data (2012, background): “Tools like COMA [8] provide rich support for various matching operations between databases as well as between RDF knowledge bases.”
Ontology change: classification and survey (2008, methodology): “Evaluations of such works appear in (Avesani et al. 2005), (Do et al. 2002), (Svab et al. 2007), (Yatskevich 2003).”
Generic schema matching, ten years later (2011, methodology): “For example:  Workflow-like strategies to independently or sequentially execute matchers and to combine their results [12][19][67].”
An interactive clustering-based approach to integrating source query interfaces on the deep Web (2004, methodology): “Schema and Interface Matching: There is a large body of works on schema matching and integration [6, 7, 9, 10, 11, 14, 16, 18, 19, 23, 29].”