Abstract
A large amount of molecular level information on biological phenomena, such as embryogenesis, cell proliferation, cell differentiation, signal transduction, transcriptional regulation, cell response, etc. is rapidly increasing because of the progress of genome science in recent years. In order to understand high-order biological phenomena, it is necessary to bridge the gap between the amount of information in the molecular level and the amount of information in the cell functional level. However, the biological knowledge in the cell functional level has not been systematized well. We have been seeking a method to systematically describe and understand cellular behaviors in morphogenesis. Various morphogenetic processes were manually selected and described according to the following format: ""cell(s)=(subject)"" ""does something=(output matter)"" ""under a certain condition=(input matter)"". CBDB is a biological database on cell behaviors in the format with images and movies which indicate To sophisticate and make use of the CBDB, we propose an AI-based approach for more multiple searching of instances and knowledge discovery from the CBDB. As the first step, we attempted to construct the ontology for understanding morphogenetic phenomena. The ontology will also facilitate clearer definition of terms and concepts, and knowledge sharing among communities of various fields. In this study, we introduce the CBDB and the prototype of ontology-based information retrieval system.
