Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data

Area of Science:

• Behavioral Ecology
• Biodiversity Informatics
• Ecosystem Informatics

Background:

• Flower-visiting data offers insights into insect-plant interactions.
• Expert ecological knowledge, particularly causal relationships, is crucial for interpreting this data.

Purpose of the Study:

• To develop and evaluate a method for eliciting and representing expert causal knowledge in behavioral ecology.
• To explore the application of this method in knowledge discovery for biodiversity and ecosystem informatics.

Main Methods:

• Combining observational data of individual organisms with expert ecological knowledge.
• Eliciting and representing expert causal knowledge of behavioral ecology.

Main Results:

• Demonstrated a method to extract valuable information on behavioral interactions (e.g., nectar foraging, pollen transfer) from flower-visiting data.
• Validated the utility of expert causal knowledge in ecological modeling.

Conclusions:

• The proposed method effectively enhances the interpretation of ecological data.
• This approach has potential for developing knowledge-based systems for broader applications in biodiversity and ecosystem informatics.