Semantic data mapping technology to solve semantic data problem on heterogeneity aspect

Abstract

The diversity of applications developed with different programming languages, application/data architectures, database systems and representation of data/information leads to heterogeneity issues. One of the problem challenges in the problem of heterogeneity is about heterogeneity data in term of semantic aspect. The semantic aspect is about data that has the same name with different meaning or data that has a different name with the same meaning. The semantic data mapping process is the best solution in the current days to solve semantic data problem. There are many semantic data mapping technologies that have been used in recent years. This research aims to compare and analyze existing semantic data mapping technology using five criteria’s. After comparative and analytical process, this research provides recommendations of appropriate semantic data mapping technology based on several criteria’s. Furthermore, at the end of this research we apply the recommended semantic data mapping technology to be implemented with the real data in the specific application. The result of this research is the semantic data mapping file that contains all data structures in the application data source. This semantic data mapping file can be used to map, share and integrate with other semantic data mapping from other applications and can also be used to integrate with the ontology language.

The performance of text similarity algorithms

“…Lexical similarity a degree of two given string are similar in its character sequence. While the score is one (1), means the words are 100% lexically identic. In contrast, zero (0) indicates that there is no common word between given strings.…”

“…The recent information problematic issue is the rapid data growth [1]. Text similarity measurement is a text mining approach that could be overcome this overwhelming problem.…”

The radiation oncology ontology (ROO): Publishing linked data in radiation oncology using semantic web and ontology techniques

“…We decided to use D2RQ because it represents one of the most common tools for database transformation from relational database to network structures. 18 In addition, the language is modular, easily allowing to link entities from the database to concepts and properties in ROO. The mapping defines a virtual RDF graph that contains instructions how to connect and map the information from the relational database.…”

The Influence of Loss Function Usage at SIAMESE Network in Measuring Text Similarity

“…The very fast growth of information nowdays causes a particular problem, such as an overwhelming of information [21]. It is very likely among those collections of huge of information found some similar ones, so that, they can be grouped into several classes based on their similarity.…”