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Article
Publication date: 1 February 2016

Chongchong Zhao, Chao Dong and Xiaoming Zhang

The integration and retrieval of the vast data have attracted sufficient attention, thus the W3C workgroup releases R2RML to standardize the transformation from relational…

Abstract

Purpose

The integration and retrieval of the vast data have attracted sufficient attention, thus the W3C workgroup releases R2RML to standardize the transformation from relational data to semantic-aware data. However, it only provides a data transform mechanism to resource description framework (RDF). The generation of mapping alignments still needs manual work or other algorithms. Therefore, the purpose of this paper is to propose a domain-oriented automatic mapping method and an application of the R2RML standard.

Design/methodology/approach

In this paper, materials science is focussed to show an example of domain-oriented mapping. source field concept and M3B2 (Metal Materials Mapping Background Base) knowledge bases are established to support the auto-recommending algorithm. As for the generation of RDF files, the idea is to generate the triples and the links, respectively. The links of the triples follow the object-subject relationship, and the links of the object properties can be achieved by the range individuals and the trail path.

Findings

Consequently based on the previous work, the authors proposed Engine for Metal Materials Mapping Background Base (EM3B2), a semantic integration engine for materials science. EM3B2 not only offers friendly graphical interfaces, but also provides auto-recommending mapping based on materials knowledge to enable users to avoid vast manually work. The experimental result indicates that EM3B2 supplies accurate mapping. Moreover, the running time of E3MB2 is also competitive as classical methods.

Originality/value

This paper proposed EM3B2 semantic integration engine, which contributes to the relational database-to-RDF mapping by the application of W3C R2RML standard and the domain-oriented mapping.

Details

Program, vol. 50 no. 1
Type: Research Article
ISSN: 0033-0337

Keywords

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Article
Publication date: 6 November 2017

Ademar Crotti Junior, Christophe Debruyne, Rob Brennan and Declan O’Sullivan

This paper aims to evaluate the state-of-the-art in CSV uplift tools. Based on this evaluation, a method that incorporates data transformations into uplift mapping…

Abstract

Purpose

This paper aims to evaluate the state-of-the-art in CSV uplift tools. Based on this evaluation, a method that incorporates data transformations into uplift mapping languages by means of functions is proposed and evaluated. Typically, tools that map non-resource description framework (RDF) data into RDF format rely on the technology native to the source of the data when data transformation is required. Depending on the data format, data manipulation can be performed using underlying technology, such as relational database management system (RDBMS) for relational databases or XPath for XML. For CSV/Tabular data, there is no such underlying technology, and instead, it requires either a transformation of source data into another format or pre/post-processing techniques.

Design/methodology/approach

To evaluate the state-of-the-art in CSV uplift tools, the authors present a comparison framework and have applied it to such tools. A key feature evaluated in the comparison framework is data transformation functions. They argue that existing approaches for transformation functions are complex – in that a number of steps and tools are required. The proposed method, FunUL, in contrast, defines functions independent of the source data being mapped into RDF, as resources within the mapping itself.

Findings

The approach was evaluated using two typical real-world use cases. The authors have compared how well our approach and others (that include transformation functions as part of the uplift mapping) could implement an uplift mapping from CSV/Tabular into RDF. This comparison indicates that the authors’ approach performs well for these use cases.

Originality/value

This paper presents a comparison framework and applies it to the state-of-the-art in CSV uplift tools. Furthermore, the authors describe FunUL, which, unlike other related work, defines functions as resources within the uplift mapping itself, integrating data transformation functions and mapping definitions. This makes the generation of RDF from source data transparent and traceable. Moreover, as functions are defined as resources, these can be reused multiple times within mappings.

Details

International Journal of Web Information Systems, vol. 13 no. 4
Type: Research Article
ISSN: 1744-0084

Keywords

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Article
Publication date: 3 November 2014

Nikolaos Konstantinou, Dimitrios-Emmanuel Spanos, Nikos Houssos and Nikolaos Mitrou

– This paper aims to introduce a transformation engine which can be used to convert an existing institutional repository installation into a Linked Open Data repository.

Abstract

Purpose

This paper aims to introduce a transformation engine which can be used to convert an existing institutional repository installation into a Linked Open Data repository.

Design/methodology/approach

The authors describe how the data that exist in a DSpace repository can be semantically annotated to serve as a Semantic Web (meta)data repository.

Findings

The authors present a non-intrusive, standards-compliant approach that can run alongside with current practices, while incorporating state-of-the art methodologies.

Originality/value

Also, they propose a set of mappings between domain vocabularies that can be (re)used towards this goal, thus offering an approach that covers both the technical and semantic aspects of the procedure.

Details

The Electronic Library, vol. 32 no. 6
Type: Research Article
ISSN: 0264-0473

Keywords

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Article
Publication date: 21 November 2016

Young Man Ko, Min Sun Song and Seung Jun Lee

The purpose of this paper is to construct a structural definition-based terminology ontology system that defines the meanings of academic terms on the basis of properties…

Abstract

Purpose

The purpose of this paper is to construct a structural definition-based terminology ontology system that defines the meanings of academic terms on the basis of properties and links terms with properties that are structured by conceptual categories (classes). This study also aims to test the possibility of semantic searches by generating inference rules and setting very complicated search scenarios.

Design/methodology/approach

For the study, 55,236 keywords from the articles of the “Korea Citation Index” were structurally defined and relationships among terms and properties were built. Then, the authors converted the RDB data into RDF and designed ontologies using the ontology developing tool Protégé. The authors also tested the designed ontology with the inference engine of the Protégé editor. The generated reference rules were tested by TBox and SPARQL queries.

Findings

The authors generated inference control rules targeting high-input-ratio data in the properties of classes by calculating the input ratio of real input data in the system, and then the authors executed a semantic search by SPARQL query by setting very complicated search scenarios, for which it would be difficult to deduce results via a simple keyword search. As a result, it was confirmed that the search results show the logical combination of semantically related term data.

Practical implications

The proposed terminology ontology system was constructed with the author keywords from research papers, it will be useful in searching the research papers which include the keywords as search results by the complex combination of semantic relation. And the Structural Terminology Net database could be utilized as an index database in retrieval services and the mining of informal big data through the application of well-defined semantic concepts to each term.

Originality/value

This paper presented a methodology for supporting IR using expanded queries based on a novel model of structural terminology-based ontology. The user who wants to access the specific topic can create query that brings the semantically relevant information. The search results show the logical combination of semantically related term data, which would be difficult to deduce results via traditional IR systems.

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