Paper Info
Title
Learning to discover complex mappings from web forms to ontologies
Abstract
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
Year
DOI
Venue
2012
10.1145/2396761.2398427
CIKM
Keywords
Field
DocType
complex mapping associate,web form,schema mapping solution,initial solution,semantically related element,expected mapping,semantic web,complex schema mapping,complex mapping,automatic approach,ontologies
Ontology (information science),Data mining,Ontology,Information retrieval,Naive Bayes classifier,Semantic mapping,Computer science,Semantic Web,Data integrity,Test case,Social Semantic Web
Conference
Citations 
PageRank 
References 
2
0.36
29
Authors
3
Name
Order
Citations
PageRank
Yuan An111714.51
Xiaohua Hu22819314.15
Il-Yeol Song32041301.54