Issue No.04 - April (2014 vol.26)
Yihong Gong , Sch. of Electron. & Inf. Eng., Xi'an Jiaotong Univ., Xi'an, China
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.56
Knowledge discovery from scientific articles has received increasing attention recently since huge repositories are made available by the development of the Internet and digital databases. In a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: document itself and a citation of other documents. In the existing topic models, little effort is made to differentiate these two roles. We believe that the topic distributions of these two roles are different and related in a certain way. In this paper, we propose a Bernoulli process topic (BPT) model which considers the corpus at two levels: document level and citation level. In the BPT model, each document has two different representations in the latent topic space associated with its roles. Moreover, the multi-level hierarchical structure of citation network is captured by a generative process involving a Bernoulli process. The distribution parameters of the BPT model are estimated by a variational approximation approach. An efficient computation algorithm is proposed to overcome the difficulty of matrix inverse operation. In addition to conducting the experimental evaluations on the document modeling and document clustering tasks, we also apply the BPT model to well known corpora to discover the latent topics, recommend important citations, detect the trends of various research areas in computer science between 1991 and 1998, and to investigate the interactions among the research areas. The comparisons against state-of-the-art methods demonstrate a very promising performance. The implementations and the data sets are available online .
text mining, Unsupervised learning, latent models,
Yihong Gong, "A Two-Level Topic Model Towards Knowledge Discovery from Citation Networks", IEEE Transactions on Knowledge & Data Engineering, vol.26, no. 4, pp. 780-794, April 2014, doi:10.1109/TKDE.2013.56