A survey of approaches to automatic schema matching

By Erhard Rahm, Philip A. Bernstein. The VLDB journal, 2001

3,975 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/580221d63ae75bdc7d68829916cf608e44a56b27

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Citations in the corpus

Crowdsourced Data Management: A Survey (2016, background): “Existing works [98] develop automatic algorithms to generate a set of possible matchings, by considering linguistic and structural information.”
The PROMPT suite: interactive tools for ontology merging and mapping (2003, neutral): “Rahm and Bernstein (Rahm and Bernstein, 2001) survey schema-integration approaches.”
Distributed Description Logics: Assimilating Information from Peer Sources (2003, background): “Rahm and Bernstein [28] provide a survey of recent solutions, while [16] is an early paper on the integration problem in the context of federated databases.”
Promptdiff: a fixed-point algorithm for comparing ontology versions (2002, background): “Rahm and Bernstein (2001) survey the approaches that use linguistic techniques to look for synonyms, machine-learning techniques to propose matches based on instance data, information-retrieval techniques to compare information about attributes, and so on.”
Comparison of Schema Matching Evaluations (2002, methodology): “schema elements (element-level matches [20]) and use similarity values between 0 (strong dissimilarity) and 1 (strong similarity) to indicate the plausibility of the correspondences.”
Similarity-Based Ontology Alignment in OWL-Lite (2004, background): “There has been important background work that can be used for ontology alignment: in discrete mathematics for matching graphs and trees [7, 11], in databases for reconciling and merging schemas [12], in machine learning for clustering compound objects described in a restricted ﬁrst order logic [1].”
A cost-based model and effective heuristic for repairing constraints by value modification (2005, background): “, [23] for a survey) in the context of schema integration.”
MAFRA - A MApping FRAmework for Distributed Ontologies (2002, background): “Both ontologies must be normalized to a uniform representation, in our case RDF(S), thus eliminating syntax differences and making semantics differences between the source and the target ontology more apparent [14].”
Statistical schema matching across web query interfaces (2003, methodology): “How can our framework bene(cid:12)t from existing techniques (as [16] surveys)?”
Data X-Ray: A Diagnostic Tool for Data Errors (2015, background): “Data management research has supplied a variety of tools to deal with errors originating from integrating data from multiple sources [1,5,47,49], identifying data items that refer to the same entity [33,37], resolving conflicts [22, 58, 59], and providing language extensions for cleaning [28].”
Closure-Tree: An Index Structure for Graph Queries (2006, neutral): “Graph data models have been studied in the database community for semantic data modeling, hypertext, geographic information systems, XML, multimedia [1], and schema matching and integration [2].”
Simlarity Search for Web Services (2004, background): “Schema matching: The database community has considered the problem of automatically matching schemas [ 24 , 12, 13, 22].”
Schema and ontology matching with COMA++ (2005, methodology): “To reduce the manual effort required, many techniques and prototypes have been developed to semi-automatically solve the match problem [6], [11].”
Overview and Framework for Data and Information Quality Research (2009, methodology): “Schema matching research[Rahm and Bernstein2001; Doan andHalevy2005]develops techniques to automatically or semiautomatically matchdata schemas.”
Ontology change: classification and survey (2008, background): “A detailed classiﬁcation appears in ﬁgure 2 (Manakanatas & Plexousakis 2006), which is an extended and improved version of a ﬁgure appearing in (Rahm & Bernstein 2001).”
An API for Ontology Alignment (2004, methodology): “There are some attempts at defining ontology alignment or ontology mapping either very close to a particular use [5,11] or instead a very abstract definition which is neither immediately implementable nor sharable [7].”