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Visualizing latent domain knowledge
Knowledge discovery and data mining commonly rely on finding salient patterns of association from a vast amount of data. Traditional citation analysis of scientific literature draws insights from strong citation patterns. Latent domain knowledge, in contrast to the mainstream domain knowledge, often consists of highly relevant but relatively infrequently cited scientific works. Visualizing latent domain knowledge presents a significant challenge to knowledge discovery and quantitative studies of science. We build upon a citation-based knowledge visualization procedure and develop an approach that not only captures knowledge structures from prominent and highly cited works, but also traces latent domain knowledge through low-frequency citation chains. We apply this approach to two cases: (1) identifying cross-domain applications of Pathfinder networks (PFNETs) and (2) clarifying the current status of scientific inquiry of a possible link between Bovine spongiform encephalopathy (BSE), also known as mad cow disease, and a new variant Creutzfeldt-Jakob disease (vCJD), a type of brain disease in human
A Relational Hyperlink Analysis of an Online Social Movement
In this paper we propose relational hyperlink analysis (RHA) as a distinct approach for empirical social science research into hyperlink networks on the World Wide Web. We demonstrate this approach, which employs the ideas and techniques of social network analysis (in particular, exponential random graph modeling), in a study of the hyperlinking behaviors of Australian asylum advocacy groups. We show that compared with the commonly-used hyperlink counts regression approach, relational hyperlink analysis can lead to fundamentally different conclusions about the social processes underpinning hyperlinking behavior. In particular, in trying to understand why social ties are formed, counts regressions may over-estimate the role of actor attributes in the formation of hyperlinks when endogenous, purely structural network effects are not taken into account. Our analysis involves an innovative joint use of two software programs: VOSON, for the automated retrieval and processing of considerable quantities of hyperlink data, and LPNet, for the statistical modeling of social network data. Together, VOSON and LPNet enable new and unique research into social networks in the online world, and our paper highlights the importance of complementary research tools for social science research into the web
Science Models as Value-Added Services for Scholarly Information Systems
The paper introduces scholarly Information Retrieval (IR) as a further
dimension that should be considered in the science modeling debate. The IR use
case is seen as a validation model of the adequacy of science models in
representing and predicting structure and dynamics in science. Particular
conceptualizations of scholarly activity and structures in science are used as
value-added search services to improve retrieval quality: a co-word model
depicting the cognitive structure of a field (used for query expansion), the
Bradford law of information concentration, and a model of co-authorship
networks (both used for re-ranking search results). An evaluation of the
retrieval quality when science model driven services are used turned out that
the models proposed actually provide beneficial effects to retrieval quality.
From an IR perspective, the models studied are therefore verified as expressive
conceptualizations of central phenomena in science. Thus, it could be shown
that the IR perspective can significantly contribute to a better understanding
of scholarly structures and activities.Comment: 26 pages, to appear in Scientometric
Communities, Knowledge Creation, and Information Diffusion
In this paper, we examine how patterns of scientific collaboration contribute
to knowledge creation. Recent studies have shown that scientists can benefit
from their position within collaborative networks by being able to receive more
information of better quality in a timely fashion, and by presiding over
communication between collaborators. Here we focus on the tendency of
scientists to cluster into tightly-knit communities, and discuss the
implications of this tendency for scientific performance. We begin by reviewing
a new method for finding communities, and we then assess its benefits in terms
of computation time and accuracy. While communities often serve as a taxonomic
scheme to map knowledge domains, they also affect how successfully scientists
engage in the creation of new knowledge. By drawing on the longstanding debate
on the relative benefits of social cohesion and brokerage, we discuss the
conditions that facilitate collaborations among scientists within or across
communities. We show that successful scientific production occurs within
communities when scientists have cohesive collaborations with others from the
same knowledge domain, and across communities when scientists intermediate
among otherwise disconnected collaborators from different knowledge domains. We
also discuss the implications of communities for information diffusion, and
show how traditional epidemiological approaches need to be refined to take
knowledge heterogeneity into account and preserve the system's ability to
promote creative processes of novel recombinations of idea
Knowledge management, innovation and big data: Implications for sustainability, policy making and competitiveness
This Special Issue of Sustainability devoted to the topic of “Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness” attracted exponential attention of scholars, practitioners, and policy-makers from all over the world. Locating themselves at the expanding cross-section of the uses of sophisticated information and communication technology (ICT) and insights from social science and engineering, all papers included in this Special Issue contribute to the opening of new avenues of research in the field of innovation, knowledge management, and big data. By triggering a lively debate on diverse challenges that companies are exposed to today, this Special Issue offers an in-depth, informative, well-structured, comparative insight into the most salient developments shaping the corresponding fields of research and policymaking
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