40 research outputs found
Applying Science Models for Search
The paper proposes three different kinds of science models as value-added
services that are integrated in the retrieval process to enhance retrieval
quality. The paper discusses the approaches Search Term Recommendation,
Bradfordizing and Author Centrality on a general level and addresses
implementation issues of the models within a real-life retrieval environment.Comment: 14 pages, 3 figures, ISI 201
Analysis of Hot Points on Data Mining Research of Medical in Foreign Countries
To promote the current development of medical data mining research, a quantitative statistics and qualitative analysis of the papers in the field of medical data mining technologies were made with the methodology of bibliometric and knowledge mapping, which were enlisted in the database of Web of Science analyzing the general situation of the papers about data mining from several aspects: period sequences, subject funds, countries and regions, core authors and research institutions, the hotspots and research frontiers. Our analysis exposed that the research of data mining in medical showed a multi-disciplinary integration of the development trend, but high-yield leading author group has not yet formed. It is important to note that scholars should raise awareness of clinical medical data mining as well as explore new research directions for further studying
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
Knowledge Integration and Diffusion: Measures and Mapping of Diversity and Coherence
I present a framework based on the concepts of diversity and coherence for
the analysis of knowledge integration and diffusion. Visualisations that help
understand insights gained are also introduced. The key novelty offered by this
framework compared to previous approaches is the inclusion of cognitive
distance (or proximity) between the categories that characterise the body of
knowledge under study. I briefly discuss the different methods to map the
cognitive dimension
Bibliometric-enhanced Retrieval Models for Big Scholarly Information Systems
Bibliometric techniques are not yet widely used to enhance retrieval
processes in digital libraries, although they offer value-added effects for
users. In this paper we will explore how statistical modelling of scholarship,
such as Bradfordizing or network analysis of coauthorship network, can improve
retrieval services for specific communities, as well as for large, cross-domain
large collections. This paper aims to raise awareness of the missing link
between information retrieval (IR) and bibliometrics / scientometrics and to
create a common ground for the incorporation of bibliometric-enhanced services
into retrieval at the digital library interface.Comment: 4 pages, IEEE BigData 2013, Workshop on Scholarly Big Data:
Challenges and Idea
Jigsaw percolation: What social networks can collaboratively solve a puzzle?
We introduce a new kind of percolation on finite graphs called jigsaw
percolation. This model attempts to capture networks of people who innovate by
merging ideas and who solve problems by piecing together solutions. Each person
in a social network has a unique piece of a jigsaw puzzle. Acquainted people
with compatible puzzle pieces merge their puzzle pieces. More generally, groups
of people with merged puzzle pieces merge if the groups know one another and
have a pair of compatible puzzle pieces. The social network solves the puzzle
if it eventually merges all the puzzle pieces. For an Erd\H{o}s-R\'{e}nyi
social network with vertices and edge probability , we define the
critical value for a connected puzzle graph to be the for which
the chance of solving the puzzle equals . We prove that for the -cycle
(ring) puzzle, , and for an arbitrary connected puzzle
graph with bounded maximum degree, and for
any . Surprisingly, with probability tending to 1 as the network size
increases to infinity, social networks with a power-law degree distribution
cannot solve any bounded-degree puzzle. This model suggests a mechanism for
recent empirical claims that innovation increases with social density, and it
might begin to show what social networks stifle creativity and what networks
collectively innovate.Comment: Published at http://dx.doi.org/10.1214/14-AAP1041 in the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org