66,872 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Author Co-Citation Analysis (ACA): a powerful tool for representing implicit knowledge of scholar knowledge workers
No full textIn the last decade, knowledge has emerged as one of the most important and valuable organizational assets. Gradually this importance caused to emergence of new discipline entitled ―knowledge management‖. However one of the major challenges of knowledge management is conversion implicit or tacit knowledge to explicit knowledge. Thus Making knowledge visible so that it can be better accessed, discussed, valued or generally managed is a long-standing objective in knowledge management. Accordingly in this paper author co- citation analysis (ACA) will be proposed as an efficient technique of knowledge visualization in academia (Scholar knowledge workers)
Co-citation Analysis: An Overview
No full textThis article gives an overview of co-citation analysis and its applications in tracking the linkages among the intellectual works and mapping the evolutionary structure of scientific disciplines. It also focuses on the features, interface, terminology used, merits and demerits of co-citation based online database applications
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Co-occurrence analysis of author keywords.
No full text(A) The network visualization of total author keywords (2,656 items) was conducted. (B) The overlay visualization of co-occurrence analysis of author keywords was presented in VOSviewer, the Weight was an occurrence, and Scores were the average published year. (C) The crosstalk of author keywords was presented.</p
Mapping the Discipline of the Olympic Games An Author-Cocitation Analysis
No full textThe authors conducted an author cocitation analysis on prominent authors writing about the Olympics during the 1990s. Author cocitation is an established bibliometric technique that can be used to measure the relative similarities of topics written about by the cited authors. This enables a visual representation of the “intellectual space” of the discipline, in this case the Olympics, to be created for the period under review. So core and peripheral research areas are identified, along with their major contributors. The representation appears as a two-dimensional cluster-enhanced map. Subject expertise was then applied to the results to place labels on the generated clusters of authors and their topics
The methodological status of co-authorship networks
No full textA powerful strategy within the study of collaboration
in science is to posit that co-authorship patterns
represent social networks.
It is prerequisite to an application of Social
Network Analysis (SNA) to define the network
entities. A network analysis of the inter-institutional
collaboration in COLLNET on the basis
of co-authorships was conducted. The study reveals
that it is crucial whether the co-authorship
itself is seen as an author's relational property or
as a social event that brings the authors together.
The former possibility is represented by a onemode
network in which each author can be related
to each other author. Quite distinct from
that are two-mode networks, the latter approach.
They consist of two single data sets in which relations
are only possible between different sets.
Different modes of representations require
different network approaches. One is that co-authorship
networks are seen as one-mode networks,
which has the advantage of the application
of a variety of measures. In contrast, twomode
networks, the other option, cannot be analysed
by standard techniques but its distinctive
features demand a new conceptualisation of
measures. In conclusion, the two-mode perspective
is more promising because it allows a dual
perspective on collaboration in science which includes
researchers as well as their scientific output
Mapping the structure and development of Science using co-citation analysis
No full textCo-citation analysis is a unique method used for studying the cognitive structure of science andassessing the research productivity. It is a research tool for examining the intellectual development and structure of the scientific discipline. This paper illustrates principles, techniques and applications of co-citation analysis. It also introduces the newly emerging co-citation analysis softwares,especially SciVal Spotlight and CiteSpace. Co-citation analysis is based on grouping together the papers that are frequently cited in pairs. Combined with single-link clustering and multidimensional scaling techniques, co-citation analysis can literally map the structure of specialized research areas as well as science as a whole
Distributed human computation framework for linked data co-reference resolution
No full textDistributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud
Some Comments on the Question Whether Co-occurrence Data Should Be Normalized
No full textIn a recent paper in the Journal of the American Society for Information Science and Technology, Leydesdorff and Vaughan assert that raw cocitation data should be analyzed directly, without first applying a normalization like the Pearson correlation. In this report, it is argued that there is nothing wrong with the widely adopted practice of normalizing cocitation data. One of the arguments put forward by Leydesdorff and Vaughan turns out to depend crucially on incorrect multidimensional scaling maps that are due to an error in the PROXSCAL program in SPSS.Multidimensional scaling;Author cocitation analysis;Co-occurrence data;Normalization;PROXSCAL;Pearson correlation
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