Skip to main content
Article thumbnail
Location of Repository

Context based querying of scientific data: changing querying paradigms?

By Epaminondas Kapetanios, Moira C. Norrie and Bernhard Brabec


We are investigating and applying a semantically enhanced query answering machine for the needs of addressing semantically meaningful data and operations within a scientific information system. We illustrate a context based\ud querying paradigm on the basis of a Regional Avalanche Information and Forecasting System - RAIFoS which is concerned with the collection and analysis of snow and weather related physical parameters in the Swiss Alps. The querying paradigm relies upon the issue of interactively constructing a semantically valid query rather than formulating one in a database specific query language\ud and for a particular implementation model. In order to achieve this goal, the query answering machine has to make inferences concerning the properties and value domains, as well as data analysis operations, which are semantically valid within particular contexts. These inferences take place when the intended query is being constructed interactively on a Web-based blackboard. A graph-based display presentation formalism is used with elements including natural language terms, measurement units, statistical quantifiers and/or specific value domains.\ud A meta-data database is used to organise and provide the elements of the graph each time the graph, and consequently the intended query, is expanded or further refined. Finally, the displayed graph is transformed into elements of the implementation model from which, in turn, SQL statements and/or sequences of statistical operations are created.\u

Topics: UOW3
Publisher: IEEE Computer Society
OAI identifier:
Provided by: WestminsterResearch

Suggested articles


  1. (1996). A Database Perspective on Knowledge Discovery.
  2. (1996). A Discovery Board Application Programming Interface and Query Language for Database Mining.
  3. (1996). A Foundation for Multi-Dimensional Databases.
  4. (1994). A Medical Terminology Server.
  5. (1955). A method for synthesising sequential circuits.
  6. (1996). A Model for Classification Structures with Evolution Control.
  7. (1996). A new SQL-like operator for mining association rules.
  8. (1993). An Extended Entity-Relationship Approach to Data Management in Object-Oriented Systems.
  9. (1990). Building Large Knowledge–Based Systems: Representation and Inference in the Cyc Project.
  10. (1996). Data Cube: A relational aggregation operator generalising group-by, cross-tab, and sub-totals.
  11. (1995). Distinguishing Typing and Classification in Object Data Models.
  12. (1956). Gedanken experiments on sequential machines. Automata Studies,
  13. (1998). Generic Agent Framework for Internet Information Systems.
  14. (1979). Introduction to Automata Theory, Languages, and Computation.
  15. Keywords: Scientific Information Systems, Meta-data, Query languages, Finite State Automata, Conceptual structures,
  16. (1996). Knowledge discovery from telecommunication network alarm databases.
  17. (1995). Modeling Multidimensional Databases.
  18. (1998). OMS Internet Integration Using Standard Interfaces.
  19. (1995). Ontologies and Knowledge Bases: Towards a Terminological Clarification.
  20. (1991). Resource integration using a large knowledge base in carnot.
  21. (1993). Retrieving and Integrating Data from Multiple Information Sources.
  22. (1964). Sequential Machines: Selected Papers.
  23. (1996). Statistics: Principles and Methods.
  24. (1996). Stratified Ontologies: the Case of Physical Objects.
  25. (1997). Using a Large Linguistic Ontology for Internet-Based Retrieval of Object-Oriented Components.
  26. (1992). Using a Relational Database to Support Explanation in a Knowledge–Based System.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.