Schema-Based Mapping Approach for Data Transformation to Enrich Semantic Web

Modern web wants the data to be in Resource Description Framework (RDF) format, a machine-readable form that is easy to share and reuse data without human intervention. However, most of the information is still available in relational form. The existing conventional methods transform the data from RDB to RDF using instance-level mapping, which has not yielded the expected results because of poor mapping. Hence, in this paper, a novel schema-based RDB-RDF mapping method (relational database to Resource Description Framework) is proposed, which is an improvised version for transforming the relational database into the Resource Description Framework. It provides both data materialization and on-demand mapping. RDB-RDF reduces the data retrieval time for nonprimary key search by using schema-level mapping. The resultant mapped RDF graph presents the relational database in a conceptual schema and maintains the instance triples as data graph. This mechanism is known as data materialization, which suits well for the static dataset. To get the data in a dynamic environment, query translation (on-demand mapping) is best instead of whole data conversion. The proposed approach directly converts the SPARQL query into SQL query using the mapping descriptions available in the proposed system. The mapping description is the key component of this proposed system which is responsible for quick data retrieval and query translation. Join expression introduced in the proposed RDB-RDF mapping method efficiently handles all complex operations with primary and foreign keys. Experimental evaluation is done on the graphics designer database. It is observed from the result that the proposed schema-based RDB-RDF mapping method accomplishes more comprehensible mapping than conventional methods by dissolving structural and operational differences.

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Technique for Transformation of Data From RDB to XML Then to RDF

Web Semantics for Textual and Visual Information Retrieval - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-2483-0.ch004 ◽

2017 ◽

pp. 70-91 ◽

Cited By ~ 1

Author(s):

Kaleem Razzaq Malik ◽

Tauqir Ahmad

Keyword(s):

Semantic Web ◽

Relational Database ◽

Resource Description Framework ◽

Data Science ◽

True Nature ◽

Extensible Markup ◽

Description Framework ◽

Mapping Techniques ◽

Resource Description ◽

The Web

This chapter will clearly show the need for better mapping techniques for Relational Database (RDB) all the way to Resource Description Framework (RDF). This includes coverage of each data model limitations and benefits for getting better results. Here, each form of data being transform has its own importance in the field of data science. As RDB is well known back end storage for information used to many kinds of applications; especially the web, desktop, remote, embedded, and network-based applications. Whereas, EXtensible Markup Language (XML) in the well-known standard for data for transferring among all computer related resources regardless of their type, shape, place, capability and capacity due to its form is in application understandable form. Finally, semantically enriched and simple of available in Semantic Web is RDF. This comes handy when with the use of linked data to get intelligent inference better and efficient. Multiple Algorithms are built to support this system experiments and proving its true nature of the study.

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PRIOR ONTOLOGY SELECTION AND QUERY TRANSLATION FOR INFORMATION SEARCH

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10s1.23490 ◽

2017 ◽

Vol 10 (13) ◽

pp. 499

Author(s):

Poornima N ◽

Shivam Agrawal ◽

Saleena B ◽

Saleena B

Keyword(s):

Information Retrieval ◽

Resource Description Framework ◽

Information Search ◽

Query Language ◽

Retrieval Process ◽

Query Translation ◽

User Intention ◽

Description Framework ◽

Ontology Selection ◽

Resource Description

Objective: Most of the current search engines follow informal keyword based search. Finding the user intention and improving the relevancy of results are the major issues faced by the current traditional keyword based search. Targeting to solve the problems of traditional search and to boost the retrieval process, a framework for semantic based information retrieval is planned. Methods: Social and wine ontologies are used to find the user intention and retrieving it. User’s natural language queries are translated into SPARQL (SPARQL Protocol and Resource Description Framework query language) query for finding related items from those ontologies.Results: The proposed method makes a significant improvement over traditional search in terms of some searches required for searching a particular number of pages using performance graph.Conclusion: Semantic based search can understand the user intention and gives better results than traditional search.

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