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, doi: 10.1016/S0169-023X(00)00003-3.

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Title Semantic data modelling and visualization using Noetica
Author(s) Greenhill, Stewart
Venkatesh, SvethaORCID iD for 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
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
DOI 10.1016/S0169-023X(00)00003-3
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.
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