Search (7 results, page 1 of 1)

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Abstract

Identifying the users and impact of research is important for research performers, managers, evaluators, and sponsors. It is important to know whether the audience reached is the audience desired. It is useful to understand the technical characteristics of the other research/development/applications impacted by the originating research, and to understand other characteristics (names, organizations, countries) of the users impacted by the research. Because of the many indirect pathways through which fundamental research can impact applications, identifying the user audience and the research impacts can be very complex and time consuming. The purpose of this article is to describe a novel approach for identifying the pathways through which research can impact other research, technology development, and applications, and to identify the technical and infrastructure characteristics of the user population. A novel literature-based approach was developed to identify the user community and its characteristics. The research performed is characterized by one or more articles accessed by the Science Citation Index (SCI) database, beccause the SCI's citation-based structure enables the capability to perform citation studies easily. The user community is characterized by the articles in the SCI that cite the original research articles, and that cite the succeeding generations of these articles as well. Text mining is performed on the citing articles to identify the technical areas impacted by the research, the relationships among these technical areas, and relationships among the technical areas and the infrastructure (authors, journals, organizations). A key component of text mining, concept clustering, was used to provide both a taxonomy of the citing articles' technical themes and further technical insights based on theme relationships arising from the grouping process. Bibliometrics is performed on the citing articles to profile the user characteristics. Citation Mining, this integration of citation bibliometrics and text mining, is applied to the 307 first generation citing articles of a fundamental physics article on the dynamics of vibrating sand-piles. Most of the 307 citing articles were basic research whose main themes were aligned with those of the cited article. However, about 20% of the citing articles were research or development in other disciplines, or development within the same discipline. The text mining alone identified the intradiscipline applications and extradiscipline impacts and applications; this was confirmed by detailed reading of the 307 abstracts. The combination of citation bibliometrics and text mining provides a synergy unavailable with each approach taken independently. Furthermore, text mining is a REQUIREMENT for a feasible comprehensive research impact determination. The integrated multigeneration citation analysis required for broad research impact determination of highly cited articles will produce thousands or tens or hundreds of thousands of citing article Abstracts.

Source

Journal of the American Society for Information Science and technology. 52(2001) no.13, S.1148-1156

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Abstract

Proposes a new paradigm for comparing quality of published papers across different disciplines. This method uses a figure of merit of the ratio of actual citations received to the potential maximum number of citations that could have been received. It is analogous to approaches used to compare performance in physical systems, and appears intrinsically more useful than present approaches

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Abstract

Leydesdorff, in his 1998 paper 'Theories of citation?', addresses the history of citations and citation analysis, and the transformation of a reference mechanism into a purportedly quantitative measure of research impact/quality. Examines different facets of citations and citation analysis, and discusses the validity of citation analysis as a useful measure of research impact/quality

Footnote

Contribution to a thematic issue devoted to 'Theories of citation?'

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Abstract

Database tomography is an information extraction and analysis system which operates on textual databases. Its primary use to date has been to idetify pervasive technical trends and themes, and the interrelationships among these main algorithmic components are multiword phrase frequency analysis and phrase proximity analysis. Shows how database tomography can be used to enhance information retrieval from large textual databases through the newly developed process of simulated nucleation. Discusses the principles of simulated nucleation and the advantages for information retrieval. Describes an application involving the development, from Science Citation Index and Engineering Compendex, a database of periodical articles focused on near Earth space and technology

Source

Journal of information science. 23(1997) no.4, S.301-311

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Abstract

The presence of trivial words in text databases can affect record or concept (words/phrases) clustering adversely. Additionally, the determination of whether a word/phrase is trivial is context-dependent. Our objective in the present article is to demonstrate a context-dependent trivial word filter to improve clustering quality. Factor analysis was used as a context-dependent trivial word filter for subsequent term clustering. Medline records for Raynaud's Phenomenon were used as the database, and words were extracted from the record abstracts. A factor matrix of these words was generated, and the words that had low factor loadings across all factors were identified, and eliminated. The remaining words, which had high factor loading values for at least one factor and therefore were influential in determining the theme of that factor, were input to the clustering algorithm. Both quantitative and qualitative analyses were used to show that factor matrix filtering leads to higher quality clusters and subsequent taxonomies.

Source

Journal of the American Society for Information Science and Technology. 56(2005) no.9, S.946-968

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Source

Journal of the American Society for Information Science. 50(1999) no.5, S.427-447

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Source

Journal of the American Society for Information Science and Technology. 63(2012) no.8, S.1675-1676