Review implementation of linguistic approach in schema matching

Research related schema matching has been conducted since last decade. Few approach related schema matching has been conducted with various methods such as neuron network, feature selection, constrain based, instance based, linguistic, and so on. Some field used schema matching as basic model such as e-commerce, e-business and data warehousing. Implementation of linguistic approach itself has been used a long time with various problem such as to calculated entity similarity values in two or more schemas. The purpose of this paper was to provide an overview of previous studies related to the implementation of the linguistic approach in the schema matching and finding gap for the development of existing methods. Futhermore, this paper focused on measurement of similarity in linguistic approach in schema matching

Schema Normalization for Improving Schema Matching

Schema matching is the problem of finding relationships among concepts across heterogeneous data sources (heterogeneous in format and in structure). Starting from the \hidden meaning" associated to schema labels (i.e. class/attribute names) it is possible to discover relationships among the elements of different schemata. Lexical annotation (i.e. annotation w.r.t. a thesaurus/lexical resource) helps in associating a \u201cmeaning" to schema labels. However, accuracy of semi-automatic lexical annotation methods on real-world schemata suffers from the abundance of non-dictionary words such as compound nouns and word abbreviations.In this work, we address this problem by proposing a method to perform schema labels normalization which increases the number of comparable labels. Unlike other solutions, the method semi-automatically expands abbreviations and annotates compound terms, without a minimal manual effort. We empirically prove that our normalization method helps in the identification of similarities among schema elements of different data sources, thus improving schema matching accuracy

Semantic recovery of traceability links between system artifacts

This paper introduces a mechanism to recover traceability links between the requirements and logical models in the context of critical systems development. Currently, lifecycle processes are covered by a good number of tools that are used to generate different types of artifacts. One of the cornerstone capabilities in the development of critical systems lies in the possibility of automatically recovery traceability links between system artifacts generated in different lifecycle stages. To do so, it is necessary to establish to what extent two or more of these work products are similar, dependent or should be explicitly linked together. However, the different types of artifacts and their internal representation depict a major challenge to unify how system artifacts are represented and, then, linked together. That is why, in this work, a concept-based representation is introduced to provide a semantic and unified description of any system artifact. Furthermore, a traceability function is defined and implemented to exploit this new semantic representation and to support the recovery of traceability links between different types of system artifacts. In order to evaluate the traceability function, a case study in the railway domain is conducted to compare the precision and recall of recovery traceability links between text-based requirements and logical model elements. As the main outcome of this work, the use of a concept-based paradigm to represent that system artifacts are demonstrated as a building block to automatically recover traceability links within the development lifecycle of critical systems.The research leading to these results has received funding from the H2020 ECSEL Joint Undertaking (JU) under Grant Agreement No. 826452 \Arrowhead Tools for Engineering of Digitalisation Solutions" and from speci¯c national programs and/or funding authorities