Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
Using Schema Matching to Simplify Heterogeneous Data Translation
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Schema Mapping as Query Discovery
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Data extraction and label assignment for web databases
WWW '03 Proceedings of the 12th international conference on World Wide Web
Statistical schema matching across web query interfaces
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
An interactive clustering-based approach to integrating source query interfaces on the deep Web
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Understanding Web query interfaces: best-effort parsing with hidden syntax
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Automatic integration of Web search interfaces with WISE-Integrator
The VLDB Journal — The International Journal on Very Large Data Bases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
HePToX: marrying XML and heterogeneity in your P2P databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Information retrieval and machine learning for probabilistic schema matching
Information Processing and Management: an International Journal
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
GoodRelations: An Ontology for Describing Products and Services Offers on the Web
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
Learning to extract form labels
Proceedings of the VLDB Endowment
An empirical study on using hidden markov model for search interface segmentation
Proceedings of the 18th ACM conference on Information and knowledge management
A hierarchical approach to model web query interfaces for web source integration
Proceedings of the VLDB Endowment
Understanding deep web search interfaces: a survey
ACM SIGMOD Record
Automatically mapping and integrating multiple data entry forms into a database
ER'11 Proceedings of the 30th international conference on Conceptual modeling
Constructing complex semantic mappings between XML data and ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Mapping discovery for XML data integration
OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, COA, and ODBASE - Volume Part II
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
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In order to realize the Semantic Web, various structures on the Web including Web forms need to be annotated with and mapped to domain ontologies. We present a machine learning-based automatic approach for discovering complex mappings from Web forms to ontologies. A complex mapping associates a set of semantically related elements on a form to a set of semantically related elements in an ontology. Existing schema mapping solutions mainly rely on integrity constraints to infer complex schema mappings. However, it is difficult to extract rich integrity constraints from forms. We show how machine learning techniques can be used to automatically discover complex mappings between Web forms and ontologies. The challenge is how to capture and learn the complicated knowledge encoded in existing complex mappings. We develop an initial solution that takes a naive Bayesian approach. We evaluated the performance of the solution on various domains. Our experimental results show that the solution returns the expected mappings as the top-1 results usually among several hundreds candidate mappings for more than 80% of the test cases. Furthermore, the expected mappings are always returned as the top-k results with k