Information Ecology References
[Full text PDF versions of the following papers and book chapters are freely available on the "Information Ecology References" link cited above.]
Published Papers 2003
McArthur, R. and Bruza, P.D. (2003) Discovery of implicit and explicit connections between people using email utterance: To be published in the Proceedings of the Eighth European Conference of Computer-supported Cooperative Work, Helsinki, September 2003
Abstract. This paper is about finding explicit and implicit connections between people by mining semantic associations from their email communications. Following from a sociocognitive stance, we propose a model called HALe which automatically derives dimensional representations of words in a high dimensional context space from an email corpus. These dimensional representations are used to discover a network of people based on a seed contextual description. Such a network represents useful connections between people not easily achievable by 'normal' retrieval means. Implicit connections are 'lifted' by applying latent semantic analysis to the high dimensional context space. The discovery techniques are applied to a substantial corpus of real-life email utterance drawn from a small-to-medium size information technology organization. The techniques are computationally tractable, and evidence is presented that suggests appropriate explicit connections are being brought to light, as well as interesting, and perhaps serendipitous implicit connections. The ultimate goal of such techniques is to bring to light contextsensitive, ephemeral, and often hidden relationships between people, and between people and information, which pervade the enterprise.
McArthur, R. and Bruza, P.D. (2003)
Chapters in Chance Discovery, Ohsawa, Y. and McBurney, P. (Eds), Springer-Verlag
Discovery of tacit knowledge and topical ebbs and flows within the utterances of online community
5.1 Introduction
This chapter will show how to derive post-semantic context ([2][3]) based on vector representations of words (described in Chapter 5). The core problem is to discover relevant word associations in relation to seed words in the utterance. This may involve uncovering implicit associations or re-weighting explicit associations more highly. In other words, the goal of the mining process is to provide highly weighted associations, firstly between a seed word such as "John", and words inherent to the background information surrounding "John" such as "Smith", "Microsoft" etc., and secondly with terms implicit in the original utterance (Note: we continue using the example framed in 5.2) It is our view that the set of such associations form a part, if not the basis, of Grice's conversational implicature [1]. The chapter describes techniques for computing associations in a dimensional space that have shown promise in the literature. The goal is to provide some initial insights as to their usefulness for mining conversational implicature by applying them to a small set of email utterances. The second illustration illuminates how the representation and techniques inform of association's changes over time capturing the ebb and flow of conversations in the community of a mailing list.
Dimensional representations of knowledge in online community
6.1 Introduction
Chance discovery in online communities has many facets. It is the serendipitous meeting of two people with a background or interest in common (the interest being subsidiary to the community's raison d'etre). It is a solution to some problem that the community has, but that solution must come from without.
In this chapter, we separate the area into three facets:
1. chance discovery of online communities;
2. between communities;
3. and within a community.