Logistics domain ontology is the foundation of logistics information sharing. According to the knowledge of logistics, an eABC ontology model was be extended from ABC ontology, and a logistics domain ontology model (LDOM) based eABC was constructed. Then relations among logistics domain knowledge were represented in this model, and five key ontology elements of LDOM were described, such as concept, relation, function, axiom and instance. Finally, LDOM was validated by the analysis of a case.
Topic crawler is a tool for collecting electronic public opinion from the internet. The identification method of topics relevance identification directly affects the acquisition rate of topic crawler. To improve the low information acquisition rate of existing topic crawlers strategy, a modified SVM classifier algorithm which is based on online incremental learning is proposed. The idea of algorithm is to remove samples that affect the training set greatly in the historical training set, and then to re-train the historical set and the incremental set to obtain a complete training set. A framework of topic crawler is constructed on the basis of this algorithm. The results of experiments show that, this method can effectively improve the acquisition rate of the crawler.
An emergency plan process modeling method has proposed in order to improve the emergency response information systems semantics of the model. First, emergency plan process ontology is designed by using ABC model as upper ontology. And the levels of the relationship of emergency plan process ontology, meta-model and hierarchically Petri nets are researched, especially the mapping rules between ontology and Petri nets. Finally, the hierarchically Petri nets model of an emergency plan is established by using the modeling method.
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