Open AccessMultiple data linkage using SSCCT for direct and semantic matching pair
Author(s) -
Mannar Mannan J,
J Jayave,
M. Sundarambal
Publication year - 2017
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i1.3.10665
Subject(s) - computer science , data mining , wordnet , cluster analysis , tree (set theory) , matching (statistics) , set (abstract data type) , data set , linkage (software) , semantic similarity , class (philosophy) , node (physics) , information retrieval , artificial intelligence , mathematics , mathematical analysis , biochemistry , statistics , chemistry , structural engineering , gene , engineering , programming language
Data Linking is a method of integrating multiple data items located on different sources and establishing links among entities of the same type or semantically relevant type. It is necessary to develop the data linkage techniques for different and semantically related items for interconnecting multiple data sources. In this paper, multiple data linkage is used to establish relation between matching entities of different types that are semantically related. The proposed method used Semantically Similar Class Clustering Tree (SSCCT) for implementing multiple data linkage. The SSCCT is built in such a way that it is easy to understand and can be transformed into association rules which are verified using WordNet ontology. The data source properties are represented as tree and the inner node, which consists of features from the first data set. The leaves of the tree represent features from the second data set that is matching with the first data set entities. The proposed method used semantic similarity estimation for pre-pruning process which is used to create Semantically Similar Class Clustering Tree effectively. Threshold value is used for decision making either the record pair is match or non-match.