Cross-context linking concepts discovery in E-government literature

Abstract

To conduct their business, organizations are nowadays challenged to handle huge amount of information from heterogeneous sources. Novel technologies can help them dealing with this delicate assignment. In this paper we describe an approach to document clustering and outlier detection that is regularly used to organize and summarize knowledge stored in huge amounts of documents in a government organization. The motivation for our preliminary study has been three-fold: first, to obtain an overview of the topics addressed in the recently published e-government papers, with the emphasis on identifying the shift of focus through the years; second, to form a collection of papers related to a preselected terms of interest in order to explore the characteristic keywords that discriminate this collection with respect to the rest of the documents; and third, to compare the papers that address a similar topic from two document sources and to show characteristic similarities and differences between the two origins, with a particular aim to identify outlier papers in each document source that are potentially worth for further exploration. As a document source for our study we used E-Government Reference Library of articles and PubMed. The presented case study results suggest that the document exploration supported by a document clustering tool can be more focused, efficient and effective.