Reconciling schemas of disparate data sources: a machine-learning approach

By AnHai Doan, Pedro M. Domingos, Alon Y. Halevy. ACM SIGMOD Conference, 2001

923 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/9011405b759b492b1132aea7b6165c9d1b0513e7

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Citations in the corpus

Learning to match ontologies on the Semantic Web (2003, positive): “Since different learners are better at utilizing different types of information, GLUE follows [12] and takes a multistrategy learning approach.”
Web Service Semantics - WSDL-S (2005, methodology): “The implementation of such can be classified as being ru le based MZ98b, MWJ99, MBR01, MGR02] or learner based techniques [WCL00, DDH01 , DLDHD04, NHTHM02, BM01a, BM01b, OTSV04].”
Statistical schema matching across web query interfaces (2003, methodology): “Second, techniques : Based on our observation of the deep Web sources (Section 3), we develop a statistical framework, which contrasts with existing techniques such as machine learning [8], constraint-based [11], and hybrid approaches [13].”
Merging Models Based on Given Correspondences (2003, background): “Finding correspondences is called schema matching; it is a major topic of ongoing research and is not covered here [8-11].”
Simlarity Search for Web Services (2004, background): “Schema matching: The database community has considered the problem of automatically matching schemas [24] [12] [13] [22].”
Meteor-s web service annotation framework (2004, background): “There are many machine learning techniques [29][30][31] where some matching rules are fed to the match algorithm and then it guesses the new matches.”
Google's Deep Web crawl (2008, methodology): “We can use such a list of input names, either manually provided or learned over time (e.g., as in [6]), to select candidate inputs to apply our in-formativeness tests on.”
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].”
WebTables: exploring the power of tables on the web (2008, background): “whether to match schemas [9, 10], to find dependencies [3,”
Corpus-based schema matching (2005, methodology): “Most prior work has used only generic constraints, and when domain constraints have been used, they have been provided manually and only in the context where there is a single mediated schema for the domain [4].”
COMA - A System for Flexible Combination of Schema Matching Approaches (2002, methodology): “Numerous researchers have addressed the schema matching problem either for specific applications [ 1, 4, 5, 7, 8, 9, 11, 15, 16] or in a more generic way for different applications and schema languages [ 12, 13, 14].”
Data Integration for the Relational Web (2009, result): “This clustering-based approach is roughly similar to data-sensitive schema-matching techniques [7].”
Similarity flooding: a versatile graph matching algorithm and its application to schema matching (2002, methodology): “A few promising approaches not only use schema-level but also instance-level information for schema matching [11, 6 ]. Unfortunately, their use of neural networks [11] or machine learning techniques [6] introduces additional preparing and training effort.”
Ontology Matching: A Machine Learning Approach (2004, methodology): “GLUE is related to our previous work on LSD [11], whose goal was to semiautomatically find schema mappings for data integration.”
Principles of dataspace systems (2006, background): “(In [20] the goal was to learn rules for mapping an arbitrary source into a fixed mediated schema).”
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework (2009, background): “For example, GLUE aims at automatically finding ontology alignment for data integration [10], [11].”