CSIR Central

Representation of Legacy Bibliographic Data in Food Science for Semantic Web Applications.

IR@CFTRI: CSIR-Central Food Technological Research Institute, Mysore


Field	Value

Relation	http://ir.cftri.com/13956/

Title	Representation of Legacy Bibliographic Data in Food Science for Semantic Web Applications.

Creator	Padmavathi, T. Krishnamurthy, M.

Subject	03 Library and Information Science

Description	Food Science database of FOSTIS consists of bibliographic records of publications involving journal articles from various Indian and international journals created by the staff of the library. We would like to discuss the experiences gained in the process of this data representation from its current data format to RDF/OWL-based data. During the course of representation we will specify the entity types in our data and choose a way to model them. For data description we are going to use the most popular and widely implemented vocabularies and domain ontologies. In this way, aligned with the concept of the semantic web, we maintain that the potential of the data would be maximized, as the information in bibliographic records would become easy to share, more visible due to incoming links, and more ready to be processed by web applications. The adoption of semantic web/linked data publishing model for bibliographic data involves moving to a more flexible data format for bibliographic data. While linked data is seen as a pragmatic implementation of the semantic web, we differ that it is not possible to implement the library data vision with the MARC, current standard for bibliographic data. Obviously, there is a need for more web-compatible and web-friendly data model. With this in mind, we proposed linked data as a part of the pragmatic implementation of the vision for bibliographic data on the Web.

Date	2015

Type	Article PeerReviewed

Format	pdf

Language	en

Identifier	http://ir.cftri.com/13956/1/V3I6-0040.pdf Padmavathi, T. and Krishnamurthy, M. (2015) Representation of Legacy Bibliographic Data in Food Science for Semantic Web Applications. International Journal of Advance Research in Computer Science and Management Studies, 3 (6). pp. 188-198.