Building earthquake semantic network by mining human activity from Twitter

Since there is 87% of chance of an approximately 8.0 Richter earthquake occurring in the Tokai region of Japan in next 30 years; we are trying to help computers to recommend the most suitable action patterns for the victims if this massive disaster happens. For example, the computer will recommend “what should do to go to a safe place”, “how to come back home”, etc. To realize this goal, it is necessary to have a collective intelligence of action patterns, which relate to the earthquake. Additionally, to help the computers understand the meaning of these action patterns, we should build the collective intelligence based on OWL (Web Ontology Language). However, the manual construction of the collective intelligence will take a large cost. Therefore, in this paper, we firstly design an earthquake semantic network. Secondly, we propose a novel approach, which can automatically collects the action patterns from Twitter for the semantic network.