Ontology-based schema matching method in web query interface integration

Deep web data integration needs to do schema matching on web query interfaces and obtain the mapping table.By introducing semantic conflicts into web query interface integration and discussing the origins and categories of the semantic conflicts,an ontology-based schema matching method is proposed.The process of the method is explained in detail using the example of web query interface integration in house domain.Conflicts can be detected automatically by checking semantic relevance degree,then the categories of the conflicts are identified and messages are sent to the conflict solver,which eliminates the conflicts and obtains the mapping table using conflict solving rules.The proposed method is simple,easy to implement and can be flexibly reused by extending the ontology to different domains.

A Deep Web Query Interfaces Classification Method Based on RBF Neural Network

This paper proposes a new approach for classification for query interfaces of Deep Web, which extracts features from the form＇s text data on the query interfaces, assisted with the synonym library, and uses radial basic function neural network （RBFNN） algorithm to classify the query interfaces. The applied RBFNN is a kind of effective feed-forward artificial neural network, which has a simple networking structure but features with strength of excellent nonlinear approximation, fast convergence and global convergence. A TEL_8 query interfaces＇ data set from UIUC on-line database is used in our experiments, which consists of 477 query interfaces in 8 typical domains. Experimental results proved that the proposed approach can efficiently classify the query interfaces with an accuracy of 95.67%.

A Query Interface Matching Approach Based on Extended Evidence Theory for Deep Web

Matching query interfaces is a crucial step in data integration across multiple Web databases. Different types of information about query interface schemas have been used to match attributes between schemas. Relying on a single aspect of information is not sufficient and the matching results of individual matchers are often inaccurate and uncertain. The evidence theory is the state-of-the-art approach for combining multiple sources of uncertain information. However, traditional evidence theory has the limitations of treating individual matchers in different matching tasks equally for query interface matching, which reduces matching performance. This paper proposes a novel query interface matching approach based on extended evidence theory for Deep Web. Our approach firstly introduces the dynamic prediction procedure of different matchers＇ credibilities. Then, it extends traditional evidence theory with the credibilities and uses exponentially weighted evidence theory to combine the results of multiple matchers. Finally, it performs matching decision in terms of some heuristics to obtain the final matches. Our approach overcomes the shortage of traditional method and can adapt to different matching tasks. Experimental results demonstrate the feasibility and effectiveness of our proposed approach.