Topic Detection for Discussion Threads with Domain Knowledge
The online communities are becoming so popular along with the development of the web but indexing and searching for the discussion data are big challenges to current applications. Topic detection was proposed to solve the problem but the accuracy is still not satisfactory, mainly because key elements are usually implicit or ambiguous which literal content comparison cannot handle. In this paper, we propose to improve the basic topic detection model by combining domain knowledge. The domain knowledge can be automatically extracted from a collection of external knowledge sources and applied to the content analysis of the threads. Two approaches, i.e. the LDA and the Concept Mapping, are proposed to implement the knowledge extraction and integration. Experimental results show that both approaches make the detection accuracy outperform the previous model. The LDA approach achieves better overall performance while the Concept Mapping is more suitable for dynamic knowledge sources.