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Modeling of Food Technology Knowledge base Information System using Semantic Web Technologies


Affiliations
1 CSIR-Central Food Technological Research Institute, Food Science & Technology Information Services (FOSTIS/Library), Mysore-20, Kamataka, India
2 Documentation Research & Training Centre (DRTC), Indian Statistical Institute, Bangalore, India
     

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The impact of knowledge explosion in various fields during the recent years has resulted in creation of vast amount of scientific literature. Food Science &Technology (FST) is also an important subject domain where rapid developments are taking place due to large and diverse research and development activities. Due to these, information storage and retrieval has become very complex and current information retrieval systems (IRs) are constrained in achieving a high precision with good response time. To overcome these constraints as well as provide natural language based effective context based retrieval mechanism, a knowledge engineering framework need to be apphed to represent, share and discover information. Semantic web technologies provides the mechanism for creating knowledge based systems, ontologies and rules for handling the data, ultimately results in inteUigent information retrieval. Ontologies are the backbone of these types of knowledge systems. Ontology is a knowledge represented on the basis of conceptualization that intends a description of object and concept sets and relations between them. The set of objects, and the description of relationship among them, reflected the representational vocabulary with which a knowledge-based program knowledge.
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  • Batista (2006). Ontology construction: cooking domain. URL: www.inesc-id.pt/pt/indicadores/Ficheiros/3615 .pdf

  • Blazquez et al. (1998). Building ontologies at the knowledge level using the ontology design environment. Proceedings of the 11th Banff Knowledge Acquisition for KnowledgeBased Systems Workshop (KAW'98), 18-23 April 1998.

  • Boulos (2002). Towards a semantic medical web: Health cyber map's tool for building an RDF metadata base of health information resources based on the qualified Dublin core metadata set. Med Sci Monit, 8(7): MT124-136.

  • Corcho (2003). Methodologies, tools and languages for building ontologies. Where is their meeting point? Data & Knowledge Engineering, 46: 41-64

  • Dublin Core (2011). Dublin Core Metadata Element Set, Version 1.1. http:// dublincore.org/documents/dces/

  • Fernandez, et al. (1997). METHONTOLOGY: From ontological art towards ontological engineering. Spring Symposium series, Stanford. p33-40

  • Gangemi (2002). A Formal Ontological Framework for Semantic Interoperability in the Fishery Domain. In Euzenat J, et al. (eds.), Workshop on Ontologies and Semantic Interoperability, CEUR-WS, 64. http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-64/

  • Graea (2005). Ontology building process: the wine domain. In EFITAAVCCA 2005, Vila Real.

  • Gennari (2003). The evolution of Protege: an environment for knowledge-based systems development. International Journal of Human-Computer Studies, 58(1): 1-32.

  • Gomez-Perez (1996). A framework to verify knowledge sharing technology. Expert Systems with Application, 11(4): 519-529.

  • Gomez-Perez (1996). Towards a method to conceptualize domain ontologies. Workshop on Ontological Engineering, ECAr96, Budapest, Hungary, p41-52.

  • Gomez-Perez (2004). Methodologies and Methods for Building Ontologies, Ontological Engineering. Springer, Germany.

  • Gruber (1992). Toward principles for the design of ontologies used for knowledge sharing. Technical Report KSL-93-04, Knowledge Systems Laboratory, Stanford University, CA.

  • Gruninger (1995). Methodology for the design and evaluation of ontologies. IJCAI Workshop on Benchmarking. Theory and Practice. Trondheim, Norway.

  • IEEE Standard for Developing Software Life Cycle Processes (1996). IEEE Computer Society, New York (USA).

  • Kim (2005). ONTOWEB: Implementing an ontology-based web retrieval system. Journal of the American Society for Information Science and Technology, 56(11): 1167-1176.

  • Lauser (2006). From AGROVOC to the agricultural ontology service/concept server: an OWL model for creating ontologies in the agricultural domain." DCMI '06 Proceedings of the 2006 international conference on Dublin Core and Metadata Applications: metadata for knowledge and learning.

  • Lopez (1999). Overview of methodologies for building ontologies. Proceedings of the IJCAl-99 workshop on Ontologies and Problem-Solving Methods (KRR5) Stockholm, Sweden, August 2,1999.

  • Lopez (1999). Building a Chemical Ontology Using Methontology and the Ontology Design Environment. IEEE Intelligent Systems, Jan/Feb 1999,37-46

  • MarcOnt Bibliographic Ontology. MarcOnt Bibliographic Ontology 2.1 Specification, http://semdl.info^ooks/2/appendices/G#SECTION077200610000000000000

  • Noy (2(X)1). Ontology Development 101: A Guide to Creating Your First Ontology. Development, 32(1): 1-25.

  • Park (2008). Developing Graduation Screen Ontology based on the METHONTOLOGY approach. Fourth International Conference on Networked Computing and Advanced Information Management, p375-380.

  • Pinto (2004). Ontologies: How can they be Built? Knowledge and Information Systems, 6: 441-464.

  • Sini (2007). Ontology-based Navigation of Bibliographic Metadata: Example from the Food. Nutrition and Agriculture Journal, ICSD-2007,64-76

  • (Suae, 2008, 168-176), "FOODS: A Food-Oriented Ontology-Driven System." Second IEEE International Conference on Digital Ecosystems and Technologies.

  • Villanas (2004). Ontology-based semantic querying of the web with respect to food recipes. Master's thesis. Technical University of Denmark.

  • Uschold (1996). ONTOLOGIES: Principles, Methods and Apphcations. Knowledge Engineering Review, 11(2).

  • YAO (2009). Protege-based ontology knowledge representation for MIS courses. International Conference on Web Information Systems and Mining, 787-791.


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  • Modeling of Food Technology Knowledge base Information System using Semantic Web Technologies

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Authors

T. Padmavathi
CSIR-Central Food Technological Research Institute, Food Science & Technology Information Services (FOSTIS/Library), Mysore-20, Kamataka, India
M. Krishnamurthy
Documentation Research & Training Centre (DRTC), Indian Statistical Institute, Bangalore, India

Abstract


The impact of knowledge explosion in various fields during the recent years has resulted in creation of vast amount of scientific literature. Food Science &Technology (FST) is also an important subject domain where rapid developments are taking place due to large and diverse research and development activities. Due to these, information storage and retrieval has become very complex and current information retrieval systems (IRs) are constrained in achieving a high precision with good response time. To overcome these constraints as well as provide natural language based effective context based retrieval mechanism, a knowledge engineering framework need to be apphed to represent, share and discover information. Semantic web technologies provides the mechanism for creating knowledge based systems, ontologies and rules for handling the data, ultimately results in inteUigent information retrieval. Ontologies are the backbone of these types of knowledge systems. Ontology is a knowledge represented on the basis of conceptualization that intends a description of object and concept sets and relations between them. The set of objects, and the description of relationship among them, reflected the representational vocabulary with which a knowledge-based program knowledge.

References