Efficient schema matching algorithm based on pre-checking

2007 ◽  
Vol 38 (4) ◽  
pp. 84-92
Author(s):  
Kengo Kubo ◽  
Keizo Yamada ◽  
Kouichi Hirata ◽  
Masateru Harao
Keyword(s):  
Schema Matching ◽  
Matching Algorithm

XML SCHEMA MATCHING

2007 ◽  
Vol 17 (05) ◽  
pp. 575-597 ◽  
Author(s):  
JIANGUO LU ◽  
JU WANG ◽  
SHENGRUI WANG
Keyword(s):  
Data Integration ◽  
Software Reuse ◽  
Edit Distance ◽  
Xml Schema ◽  
Schema Matching ◽  
Schema Evolution ◽  
Tree Edit Distance ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Unordered Trees

XML Schema matching problem can be formulated as follows: given two XML Schemas, find the best mapping between the elements and attributes of the schemas, and the overall similarity between them. XML Schema matching is an important problem in data integration, schema evolution, and software reuse. This paper describes a matching system that can find accurate matches and scales to large XML Schemas with hundreds of nodes. In our system, XML Schemas are modeled as labeled and unordered trees, and the schema matching problem is turned into a tree matching problem. We proposed Approximate Common Structures in trees, and developed a tree matching algorithm based on this concept. Compared with the traditional tree edit-distance algorithm and other schema matching systems, our algorithm is faster and more suitable for large XML Schema matching.

A LEXICAL DECISION TREE SCHEME FOR SUPPORTING SCHEMA MATCHING

2011 ◽  
Vol 10 (03) ◽  
pp. 519-537 ◽  
Author(s):  
BEEN-CHIAN CHIEN ◽  
SHIANG-YI HE
Keyword(s):  
Decision Tree ◽  
Lexical Decision ◽  
Structural Similarity ◽  
Schema Matching ◽  
Similarity Matching ◽  
Matching Method ◽  
Matching Algorithm ◽  
Essential Step ◽  
Structure Similarity ◽  
Two Phases

To manipulate semantic web and integrate different data sources efficiently, automatic schema matching plays a key role. A generic schema matching method generally includes two phases: the linguistic similarity matching phase and the structural similarity matching phase. Since linguistic matching is an essential step for effective schema matching, developing a high accurate linguistic similarity matching scheme is required. In this paper, a schema matching approach called Similarity Yield Matcher (SYM) is proposed. In SYM, a lexical decision tree is presented to determine the linguistic similarity matching of the first phase. A structural matching algorithm is then proposed to find the structure similarity between two tree schemas. The proposed schema matching approach was evaluated by testing on several benchmarks of real schemas and comparing with other methods. The experimental results show that the proposed lexical decision tree substantially improves the linguistic similarity matching effectively and efficiently. The proposed SYM algorithm also performs high effectiveness on 1–1 schema matching.

scholarly journals Hybrid Schema Matching (HSM): Schema Matching Algorithm for Integrating Geographic Information

2013 ◽  
Vol 2 (3) ◽  
pp. 173-186
Author(s):  
Jiyoon Lee ◽  
Sukhoon Lee ◽  
Jangwon Kim ◽  
Dongwon Jeong ◽  
Doo-Kwon Baik
Keyword(s):  
Geographic Information ◽  
Schema Matching ◽  
Matching Algorithm
Sign in / Sign up

Export Citation Format

Share Document