Connecting Text Mining and Pathways using the PathText Resource

Many systems have been developed in the past few years to assist researchers in the discovery of knowledge published as English text, for example in the PubMed database. At the same time, higher level collective knowledge is often published using a graphical notation representing all the entities in a pathway and their interactions. We believe that these pathway visualizations could serve as an effective user interface for knowledge discovery if they can be linked to the text in publications. Since the graphical elements in a Pathway are of a very different nature than their corresponding descriptions in English text, we developed a prototype system called PathText. The goal of PathText is to serve as a bridge between these two different representations. In this paper, we first describe the overall architecture and the interfaces of the PathText system, and then provide some details about the core Text Mining components.

• Type: Conference
• Year: 2008
• Where: LREC
• Authors: Rune Sætre, Brian Kemper, Kanae Oda, Naoaki Okazaki, Yukiko Matsuoka, Norihiro Kikuchi, Hiroaki Kitano, Yoshimasa Tsuruoka, Sophia Ananiadou, Jun-ichi Tsujii
• Keywords: Education, English Text, Level Collective Knowledge, LREC 2008, Pathway Visualizations