Knowledge Yielding Ontologies
for Transition-based Organization
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Wikyoto Kybot Editor (Fact extractor)

The modelling of the facts and relations takes place through the same Wikyoto interface.

Patterns are complex structures but could be phrased to the user as examples in natural language, e.g. “decrease of populations in specific regions and specific periods”. Again, the process uses text fragments from the sources so that it knows the underlying conceptual patterns of each example. The system should hide complex structures as much as possible. 

In a possible scenario, the user selects a text fragment as an example and the system will look for similar facts in the document repository. 
For example, the user may highlight the text "dramatic decrease of snakes in rural areas in Spain".
The system then derives the corresponding semantic pattern and looks for other facts that match these patterns, e.g. a DecreasingProcess having an effect on quantities of Snakes that have their Habitat in Spain.
These patterns can be generalized by dropping constraints. The conceptual pattern is matched against text in any language, so also Spanish documents.

The user can create series of patterns and store them in a repository as profiles.
When deployed, the patterns are read by the system and applied to the uploaded text sources.

The analysis of the text is done by so-called Kybots: Knowledge Yielding Robots. The Kybots read the pattern, have access to domain wordnets in various languages with the domain ontology and scan the text to detect relations and facts. The output is a richly structured semantically annotated text repository and a repository of facts and relations that fit the patterns. 


For KYOTO-1 system, we agreed to concentrate our efforts on designing and developing the Expression Rules of the Kybots only for the TERMS section of KAF.



Collocation in the global KYOTO Architecture:


Kybot Editor

Detailed documentation and video-tutorial

ICT-211423 - 2008 © Kyoto Consortium