Design of an Ontological Interface for Chemical and Biotechnological Knowledge Acquisition by Means of an Intelligent Agent

摘要

This work advocates the use of an ontology-supported intelligent agent for knowledge acquisition in the chemical/biotechnological domain. A methodological framework is presented, under the form of an algorithmic procedure with 14 activity stages and 5 decision nodes, for introducing a taxonomy/partonomy function within an ad hoc established Knowledge Base (KB), set in the core of the IA, between a cognitive interface and the activity decision maker. This approach has been implemented in optimizing the reliability of a biosensor system. The interface built, based on ontology-supported biosensor modeling that enables the correct annotation of domain semantics for transdisciplinary searching, facilitates (i) model-supported ontology expansion based on both, user interests and domain specificity, and (ii) ontology-supported model retrieval, by which the power of ontology features can be leveraged as a fast index structure to locate most-needed information for the user. Thereby, the functionality of the scheme proposed has been proven suitable to support decision making in knowledge-intensive, multifaceted and dynamic environments.