Burst topic detection aims to extract rapidly emerging topics from large volumes of text streams, including scientific literature. Currently there are several burst models and detection algorithms based on different burst definitions, which share the common deficiency that semantic information of topics is not taken into consideration, which results in noisy bursts in identified burst topics. In this paper, a K-state automaton burst detection model based on a KOS (knowledge organization system) is proposed and applied in detecting emerging trends and burst topics in the cancer field. Experiments showed that the K-state automaton burst detection model can better represent the variety of bursts and detect burst concepts with maximal confidence. Furthermore, the application of KOS in the process of concept extraction could effectively remove noisy concepts and enhance the accuracy of identifying burst concepts.
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