Purpose Driven DIKW Modeling and Analysis of Meteorology and Depression.

Many factors can induce depression. Scholars have carried out research on the correlation between meteorological elements and occurence of depression. However, research conclusions are often inconsistent and even contradictory to each other. Very few researches have investigated the automatic identification and resolution of the inconsistency of the conclusions in different papers. We propose a purpose driven DIKWP modeling and synthesis of Meteorology and Depression. Firstly, based on purpose driven strategy, we map meteorological documents and depression document in the forms of data, information, knowledge and wisdom types as DIKWP Content Graphs. Secondly, through the interactive ontological semantic calculation and reasoning in DIKWP Content Graphs among stakeholders, we retrieve the cognitive DIKWP Cognition Graphs from the stakeholders. Finally, through purpose driven processing, we combined objective DIKWP Content Graphs and subjective DIKWP Cognition Graphs to form integrated DIKWP Semantic Graphs. In the DIKWP semantic space which combines the originally discrete meteorological DIKW elements and occurence DIKW of depression with a DIKWP models represented expertise participants, we maximized the searching space for identification of the semantic level difference of the inconsistent conclusions and finding the resolution of the inconsistencies.