A probabilistic concept annotation for IT service desk tickets

Abstract

Ticket annotation and search has become an important research subject in the IT service desk delivery. Millions of tickets are created yearly to address business users' IT related problems. In IT service desk management, it is critical to first capture the pain points for a group of tickets to determine root cause; secondly, to obtain the respective distributions in order to layout the priority of addressing these pain points. An advanced ticket analytics system utilizes a combination of topic modeling and clustering to address the above issues and the integration of these features into information architecture will allow for a wider distribution of this technology and progress to a remarkable financial impact for IT industry. Topic modeling has been used to extract topics from given documents; each topic is represented by unigram distributions. However, it is not clear how to interpret the results. Due to the inadequacy to render top concepts, in this paper, we propose a probabilistic framework, which integrates topic models, POS tags, query expansion and so on, for the practical challenge. The rigorously empirical experiments demonstrate the consistent and utility performance of the proposed method on real datasets.