Corpus-based schema matching

By Jayant Madhavan, Philip A. Bernstein, AnHai Doan, Alon Y. Halevy. IEEE International Conference on Data Engineering, 2005

448 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/3ad25d7ddaf1393198bfee58ccad6450532877c7

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In Artificial Intelligence

Citations in the corpus

Ten Challenges for Ontology Matching (2008, methodology): “These include: (i) declaring the missing axioms manually as a prematch effort [12, 54]; (ii) reusing previous match results [12]; (iii) querying the web [43]; (iv) using domain specific corpus [1, 52]; (v) using domain specific ontologies [1, 77]; and (vi) using ontologies available on the semantic ”
Google's Deep Web crawl (2008, methodology): “We began our surfacing eﬀort with a semi-automatic tool that employed state-of-the-art schema-matching techniques [6, 11].”
Ontology Matching with Semantic Verification (2009, background): “Corpus-based matching [28] uses domainspecific knowledge in the form of an external corpus of mappings which evolves over time.”
Semantic text similarity using corpus-based word similarity and string similarity (2008, background): “…text similarity can be used in schema matching to solve semantic heterogeneity, a key problem in any data sharing system whether it is a federated database, a data integration system, a message passing system, a web service, or a peer-to-peer data management system [Madhavan et al. 2005].”
A Survey of Schema-Based Matching Approaches (2005, unknown): “The first direction aims at taking into account some novel matching approaches which exploit schema-level information, e.g., [1, 14, 38, 45 , 54].”
Generic schema matching, ten years later (2011, background): “It was further extended in [46] to show that models can be trained from known mappings in a domain and applied to match two completely new schemas in the same domain.”
Ontology Matching (2007, background): “Madhavan and colleagues (Madhavan et al. 2005) proposed an approach to schema matching which, besides input information available from the schemas under consideration, also exploits some domain-specific knowledge via an external corpus of schemas and mappings.”
InfoGather: entity augmentation and attribute discovery by holistic matching with web tables (2012, methodology): “…uses several base techniques such as linguistic matching of attribute names and detecting overlap of data instances and combines them to determine the ﬁnal matchings; the base techniques as well as the combiner can either be machine learning-based techniques or non-learning methods [8, 15].”
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework (2009, background): “Much research work has addressed the schema matching problem [9], [11], [33], [34], [41], [42].”
Semantic Matching: Algorithms and Implementation (2007, background): “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.”
Schema and ontology matching with COMA++ (2005, methodology): “Reuse-oriented Matching. The reuse of existing schemas is addressed in [ 7 ], focusing on learning and using component statistics in a corpus of schemas for matching.”
Principles of dataspace systems (2006, background): “[42] showed how to analyze large numbers of schemas or web services to learn properties that enabled their systems to automatically guess mappings between unseen sources.”
Ontology change: classification and survey (2008, background): “…(Aumueller et al. 2005), (David et al. 2006), (Giunchiglia et al. 2006), (Hu & Qu 2006), (Shvaiko & Euzenat 2005), (Zhdanova & Shvaiko 2006), the term ontology (schema) matching is used to refer to ontology alignment and the two terms are used interchangeably (see also (Madhavan et al. 2005)).”
Web-Scale Data Integration: You can afford to Pay as You Go (2007, methodology): “The schema matching component in P AY G O builds on ideas proposed in earlier matching works such as [10, 22, 23, 26, 34].”
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].”
Ontology Matching: State of the Art and Future Challenges (2013, methodology): “Using domain specific corpora (of schemas and mappings) [107], [111] or schema covers [112]; .”
WebTables: exploring the power of tables on the web (2008, methodology): “The idea of leveraging a corpus of schemas was first considered in [16].”
Automatically refining the wikipedia infobox ontology (2008, methodology): “proposed a corpus-based matc hing approach which leverages a large set of schemata and mappings in a particular domain to improve robustness of matchi ng algorithms [23].”