Generic Schema Matching with Cupid

By Jayant Madhavan, Philip A. Bernstein, Erhard Rahm. Very Large Data Bases Conference, 2001

1,653 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7ff9bf4d58358fc008b059028a3e33919d12b335

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Citations in the corpus

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].”
COMA - A System for Flexible Combination of Schema Matching Approaches (2002, methodology): “Cupid [ 12] represents a sophisticated hybrid match approach combining a name matcher with a structural match algorithm, which derives the similarity of elements based on the similarity of their components hereby emphasizing the name and data type similarities present at the finest level of…”
Ontology Matching: A Machine Learning Approach (2004, background): “Several works [25, 20, 24] have exploited variations of the general heuristic “two nodes match if nodes in their neighborhood also match”, but in an isolated fashion, and not in the same general framework we have in GLUE.”
InfoGather: entity augmentation and attribute discovery by holistic matching with web tables (2012, methodology): “One way to address the coverage issue is to use synonyms of the augmenting attribute [18, 16].”
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].”
Ontology Alignment Evaluation Initiative: Six Years of Experience (2011, background): “Instead, the activities were somewhat fragmented, such as those of Cupid [45] and iMAP [ 15 ] .”
Corpus-based schema matching (2005, methodology): “This matrix can be used directly to predict matches, to boot-strap graph matching algorithms such as [Madhavan et al., 2001; Melnik et al., 2002], or to be combined with results from other matchers in an extensible framework of systems such as [Doan et al.”
Semantic E-Workflow Composition (2003, methodology): “This assumption can be further relaxed by considering work in schematic heterogeneity (Kashyap and Sheth, 1996) and schema mapping (Madhavan et al., 2001).”
Generic schema matching, ten years later (2011, methodology): “As the references in [45] attest, we were by no means the first to work on schema matching.”
S-Match: an Algorithm and an Implementation of Semantic Matching (2004, methodology): “We have compared S-Match with three state of the art, schema based, matching systems, namely Cupid [11], COMA [1], and Similarity Flooding (SF) [14] as implemented within the Rondo system [13].”
Ontology change: classification and survey (2008, methodology): “On the contrary, Cupid (Madhavan et al. 2001) is a hybrid schema matcher that combines a name matcher and a structure-based matcher.”
Ontology Matching (2007, methodology): “Cupid (Madhavan et al. 2001) implements an algorithm comprising linguistic and structural schema matching techniques and computing similarity coefficients with the assistance of domain-specific thesauri.”
WebTables: exploring the power of tables on the web (2008, methodology): “used a synonym-set from a hand-made thesaurus [17, 24].”
Ontology Matching: State of the Art and Future Challenges (2013, methodology): “Overcoming semantic heterogeneity is typically achieved in two steps, namely: ( i ) matching entities to determine an alignment, i.e., a set of correspondences, and ( ii ) interpreting an alignment according to application needs, such as data translation or query answering.”
Eliminating Fuzzy Duplicates in Data Warehouses (2002, methodology): “Our notion of co-occurrence between tuples is similar to that used for clustering categorical data [e.g., GKR98, GRS99, GGR99] and that for matching schema [ MBR01 ].”
MAFRA - A MApping FRAmework for Distributed Ontologies (2002, background): “Furthermore, mapping approaches can mainly be distinguished along the following three categories: discovery, [14, 3, 5, 10, 1], mapping representation [9, 1, 11, 13] and execution [4, 11].”
Data integration: the teenage years (2006, background): “Hence, a significant branch of the research community focused on semi-automatically generating schema mappings [12, 21, 31, 32, 33, 56, 63, 75, 76, 82, 84, 88, 89, 96, 110].”
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].”