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Mahesh, Kavi
- Supporting Multiple Points of View in Knowledge Organization
Abstract Views :413 |
PDF Views:10
Authors
Affiliations
1 Centre for Knowledge Analytics and Ontological Engineering – KAnOE, Department of Computer Science and Engineering, PES Institute of Technology, Bangalore 560085, IN
2 Centre for Knowledge Analytics and Ontological Engineering – KAnOE, Department of Computer Science and Engineering, PES Institute of Technology, Bangalore-560085, IN
1 Centre for Knowledge Analytics and Ontological Engineering – KAnOE, Department of Computer Science and Engineering, PES Institute of Technology, Bangalore 560085, IN
2 Centre for Knowledge Analytics and Ontological Engineering – KAnOE, Department of Computer Science and Engineering, PES Institute of Technology, Bangalore-560085, IN
Source
Journal of Information and Knowledge (Formerly SRELS Journal of Information Management), Vol 50, No 6 (2013), Pagination: 831-842Abstract
Knowledge organization schemes typically assume a single point of view and a consequent single scheme of classification and a single set of categories with "standard" definitions and conceptual relationships. The real world, on the other hand, is full of multiple points of view that result in different conceptualizations and categories which are incompatible with each other. This article attempts to formulate the present lack of support for multiple points of view as a formal problem in ontological modeling and proposes a solution that enables multiple, incompatible views of the world to co-exist in a single KO scheme. Although a particular view in such a KO scheme is likely to be inconsistent with other views within the same scheme, the solution guarantees that each view presents a consistent conceptualization of the world or domain being organized. The proposed solution is expected to be of significant practical value in supporting multiple conceptualizations resulting from cultural, historical, philosophical, linguistic and political differences. This article also examines the gaps in current standards such as OWL and proposes an extension to OWL to enable multiple points of view.Keywords
Knowledge Organization, Ontology, Owl, Multiple Conceptualization.References
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- Managing Research Data
Abstract Views :152 |
PDF Views:0
Authors
Affiliations
1 Centre for Knowledge Analytics and Ontological Engineering, PES University, Bengaluru, IN
1 Centre for Knowledge Analytics and Ontological Engineering, PES University, Bengaluru, IN
Source
Information Studies, Vol 21, No 1 (2015), Pagination: 1-6Abstract
It is becoming a common practice to publish data generated in research projects along with the results derived from the data. As more and more datasets are published, the digital library of the future needs an effective solution for managing repositories of both documents and datasets. Academic libraries may soon be expected to manage and provide access to research data in addition to books and periodicals. The technological needs of such a research data service suggest a model based on the use of semantic linking using the constructs of ontology, on top of data represented in the form of Linked Open Datasets.References
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- Kavi Mahesh; Shruthi Chari and Shrinidhi Ramakrishnan (2013). LODScape: OntologyBased Multiple-LOD Object Browser In: Proc. 12th ISWC-2013, Semantic Web Challenge, Sydney, Australia.
- Kavi Mahesh (2013). Ontology-Based Content Management In: Proc. International Conference on Digital Libraries - ICDL-2013, TERI, 27-29 Nov 2013, New Delhi, India.
- Kavi Mahesh and Pallavi Karanth (2012). A Novel Knowledge Organization Scheme for the Web: Superlinks with Semantic Roles. In: Advances in Knowledge Organization, Vol 13: Categories, Contexts and Relations in Knowledge Organization, Proceedings of the Twelfth International ISKO Conference, 6-9 August 2012, Mysore, India, pp90-95, ISBN 9783899139020 Edited by:A.Neelameghan and K.S.Raghavan. Ergon Verlag.
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- From Data to Knowledge Analytics:Capabilities and Limitations
Abstract Views :394 |
PDF Views:0
Authors
Affiliations
1 KAnOE - Center for Knowledge Analytics and Ontological Engineering, PES University, Bangalore 560085, IN
1 KAnOE - Center for Knowledge Analytics and Ontological Engineering, PES University, Bangalore 560085, IN
Source
Information Studies, Vol 21, No 4 (2015), Pagination: 261-274Abstract
Data analytics is playing a central role in deriving useful information from large amounts of data available online in a variety of domains and applications. Analytics enploys a wide array of methods ranging from classical statistical techniques to those exploiting the visual and cognitive capabiUties of human users. In spite of all its capabilities, analytics at present seems to suffer from significant limitations in deahng with unstructured data and knowledge. This article explores the Umitations and defines key requirements to be met by fiiture developments in analytics. The article concludes with a sketch of tme knowledge analytics which is capable of delivering insights from knowledge structures, not just tabular data.Keywords
Analytics, Unstructured Data, Knowledge, Capabilities, Limitations.References
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