Semantic data modelling and visualization using Noetica

Greenhill, Stewart and Venkatesh, Svetha 2000, Semantic data modelling and visualization using Noetica, Data and knowledge engineering, vol. 33, no. 3, pp. 241-276.

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Title Semantic data modelling and visualization using Noetica
Author(s) Greenhill, Stewart
Venkatesh, Svetha
Journal name Data and knowledge engineering
Volume number 33
Issue number 3
Start page 241
End page 276
Total pages 36
Publisher Elsevier BV
Place of publication Amsterdam, The Netherlands
Publication date 2000-06
ISSN 0169-023X
1872-6933
Keyword(s) knowledge representation
graph traversal
data visualisation
Summary Noetica is a tool for structuring knowledge about concepts and the reIationships between them. It differs from typical information systems in that the knowledge it represents is abstract, highly connected, and includes meta-knowledge (knowledge about knowledge). Noetica represents knowledge using a strongly typed graph data model. By providing a rich type system it is possible to represent conceptual information using formalized structures. A class hierarchy provides a basic classification for all objects. This allows for a consistency of representation that is not often found in `free' semantic networks, and gives the ability to easily extend a knowledge model while retaining its semantics. Visualization and query tools are provided for this data model. Visualization can be used to explore complete sets of link-classes, show paths while navigating through the database, or visualize the results of queries. Noetica supports goal-directed queries (a series of user-supplied goals that the system attempts to satisfy in sequence) and pathfinding queries (where the system finds relationships between objects in the database by following links).
Language eng
Field of Research 080104 Computer Vision
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2000 Elsevier Science B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044269

Document type: Journal Article
Collection: School of Information Technology
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