Graph and Topological Structure Mining on Scientific Articles

In this paper, we investigate a new approach for literature mining. We use frequent subgraph mining, and its generalization topological structure mining, for finding interesting relationships between gene names and other key biological terms from the text of scientific articles. We show how we can find keywords of interest and represent them as nodes of the graphs. We also propose several methods for inserting edges between these nodes. Our study initially focused on comparing: 1) different methods for constructing edges, and 2) patterns found from sub-graph mining and topological structure mining. Subsequently, we analyzed several frequent topological minors reported by our experiments, and explained their scientific significance. Overall, our study shows the following. First, a simple method of constructing edges, which is based on sliding windows, seems to provide the best results. Second, we are able to find much larger number of well-known and meaningful topological patterns with ...

• Type: Conference
• Year: 2007
• Where: BIBE
• Authors: Fan Wang, Ruoming Jin, Gagan Agrawal, Helen Piontkivska
• Keywords: BIBE 2007, Bioinformatics, Frequent Topological Minors, Generalization Topological Structure, Topological Structure Mining