Authors:
The-Minh Nguyen
;
Takahiro Kawamura
;
Yasuyuki Tahara
and
Akihiko Ohsuga
Affiliation:
The University of Electro-Communications’ Graduate School of Information Systems, Japan
Keyword(s):
Evacuation-rescue, Twitter, Action network, Action-based collaborative filtering.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
e-Business
;
Enterprise Engineering
;
Enterprise Information Systems
;
Enterprise Ontologies
;
Evolutionary Computing
;
Formal Methods
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Machine Learning
;
Natural Language Processing
;
Ontologies
;
Pattern Recognition
;
Sensor Networks
;
Signal Processing
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
Abstract:
Since there is 87% of chance of an approximately 8.0-magnitude earthquake occurring in the Tokai region of Japan within the next 30 years; we are trying to help computers to recommend suitable action patterns for the victims if this massive earthquake 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. It is also important to let the computers make a recommendation in time, especially in this kind of emergency situation. This means these action patterns should to be collected in real-time. Additionally, to help the computers understand the meaning of these action patterns, we should build the collective intelligence based on web ontology language (OWL). However, the manual construction of the collective intelligence will take a large cost, and it is difficult in the emergency situation. Therefor
e, in this paper, we first design a time series action network. We then introduce a novel approach, which can automatically collects the action patterns from Twitter for the action network in realtime. Finally, we propose a novel action-based collaborative filtering, which predicts missing activity data, to complement this action network.
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