Search (136 results, page 1 of 7)

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Abstract

With the growth in biomedical literature, the necessity of extracting useful information from the literature has increased. One approach to extracting biomedical knowledge involves using citation relations to discover entity relations. The assumption is that citation relations between any two articles connect knowledge entities across the articles, enabling the detection of implicit relationships among biomedical entities. The goal of this article is to examine the characteristics of biomedical entities connected via intermediate entities using citation relations aided by text mining. Based on the importance of symptoms as biomedical entities, we created triples connected via citation relations to identify drug-disease pairs with shared symptoms as intermediate entities. Drug-disease interactions built via citation relations were compared with co-occurrence-based interactions. Several types of analyses were adopted to examine the properties of the extracted entity pairs by comparing them with drug-disease interaction databases. We attempted to identify the characteristics of drug-disease pairs through citation relations in association with biomedical entities. The results showed that the citation relation-based approach resulted in diverse types of biomedical entities and preserved topical consistency. In addition, drug-disease pairs identified only via citation relations are interesting for clinical trials when they are examined using BITOLA.

Date

1.11.2018 18:19:22

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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.

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Abstract

This study proposes a new method to construct and trace the trajectory of conceptual development of a research field by combining main path analysis, citation analysis, and text-mining techniques. Main path analysis, a method used commonly to trace the most critical path in a citation network, helps describe the developmental trajectory of a research field. This study extends the main path analysis method and applies text-mining techniques in the new method, which reflects the trajectory of conceptual development in an academic research field more accurately than citation frequency, which represents only the articles examined. Articles can be merged based on similarity of concepts, and by merging concepts the history of a research field can be described more precisely. The new method was applied to the "h-index" and "text mining" fields. The precision, recall, and F-measures of the h-index were 0.738, 0.652, and 0.658 and those of text-mining were 0.501, 0.653, and 0.551, respectively. Last, this study not only establishes the conceptual trajectory map of a research field, but also recommends keywords that are more precise than those used currently by researchers. These precise keywords could enable researchers to gather related works more quickly than before.

Theme

Data Mining

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Abstract

Recent Web-searching and -mining tools are combining text and link analysis to improve ranking and crawling algorithms. The central assumption behind such approaches is that there is a correiation between the graph structure of the Web and the text and meaning of pages. Here I formalize and empirically evaluate two general conjectures drawing connections from link information to lexical and semantic Web content. The link-content conjecture states that a page is similar to the pages that link to it, and the link-cluster conjecture that pages about the same topic are clustered together. These conjectures are offen simply assumed to hold, and Web search tools are built an such assumptions. The present quantitative confirmation sheds light an the connection between the success of the latest Web-mining techniques and the small world topology of the Web, with encouraging implications for the design of better crawling algorithms.

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Abstract

This paper addresses a bibliometric methodology to discover the structure of the scientific 'landscape' in order to gain detailed insight into the development of MD fields, their interaction, and the transfer of knowledge between them. This methodology is appropriate to visualize the position of MD activities in relation to interdisciplinary MD developments, and particularly in relation to socio-economic problems. Furthermore, it allows the identification of the major actors. It even provides the possibility of foresight. We describe a first approach to apply bibliometric mapping as an instrument to investigate characteristics of knowledge transfer. In this paper we discuss the creation of 'maps of science' with help of advanced bibliometric methods. This 'bibliometric cartography' can be seen as a specific type of data-mining, applied to large amounts of scientific publications. As an example we describe the mapping of the field neuroscience, one of the largest and fast growing fields in the life sciences. The number of publications covered by this database is about 80,000 per year, the period covered is 1995-1998. Current research is going an to update the mapping for the years 1999-2002. This paper addresses the main lines of the methodology and its application in the study of knowledge transfer.

Theme

Data Mining

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Abstract

Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM). Design/methodology/approach Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution. Findings It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration. Research limitations/implications The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation. Originality/value The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies.

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Source

Information processing and management. 22(1986), S.417-419

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Date

13. 7.2008 19:53:22

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Source

Thermochimica acta. 110(1987), S.11-22

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Abstract

The study examined Web co-links to Canadian university Web sites. Multidimensional scaling (MDS) was used to analyze and visualize co-link data as was done in co-citation analysis. Co-link data were collected in ways that would reflect three different views, the global view, the French Canada view, and the English Canada view. Mapping results of the three data sets accurately reflected the ways Canadians see the universities and clearly showed the linguistic and cultural differences within Canadian society. This shows that Web co-linking is not a random phenomenon and that co-link data contain useful information for Web data mining. It is proposed that the method developed in the study can be applied to other contexts such as analyzing relationships of different organizations or countries. This kind of research is promising because of the dynamics and the diversity of the Web.

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Abstract

According to the bibliographical data included in the Web of Science, SCOPUS, Chemical Abstracts, and other specialized information services covering the period 1900-1950, the first publications in mainstream journals by Mexican researchers appeared only in the first decades of the 20th century. Contrary to expectations, we find that the academic community was not the protagonist in the early stages of Mexican scientific practices, but that there was a strong contribution coming from researchers associated with the public-health sector and the chemical and mining industries. We were able to identify in this half century four different modes of scientific production: amateur, institutional, academic, and industrial, which in turn correspond to distinct stages in the evolution of the Mexican scientific production. We characterize these modes of production with a variety of indicators: publication and citation patterns, author output, journal and subject categories, institutional collaborations, and geographical distribution.

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Abstract

This study combines Web usage mining, Web link analysis, and bibliometric methods for analyzing research activities in research organizations. It uses visits to the Expert Protein Analysis System (ExPASy) server-a virtual research infrastructure for bioinformatics-as a proxy for measuring bioinformatic research activity. The study finds that in the United Kingdom (UK), Germany, and Spain the number of visits to the ExPASy Web server made by research organizations is significantly positively correlated with research output in the field of biochemistry, molecular biology, and genetics. Only in the UK do we find a significant positive correlation between ExPASy visits per publication and the normalized impact of an organization's publications. The type of indicator developed in this study can be used to measure research activity in fields in which e-research has become important. In addition, it can be used for the evaluation of e-research infrastructures.

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Date

22. 5.1999 19:22:35

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Date

22. 7.2006 15:22:28

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Footnote

Rez. in: Journal of library and information science 22(1996) no.2, S.116-117 (L.C. Smith)

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Date

22. 7.2006 18:55:29

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Date

22. 7.2006 18:55:55

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Abstract

Vom 5. bis 7. November 2003 findet auf Initiative und in der Verantwortung der Zentralbibliothek des Forschungszentrums Jülich die Konferenz "Bibliometric Analysis in Science and Research" statt: Bibliometrische Indikatoren, Bibliomefrisches Mapping, Webmetrie und Forschungspolitik stehen auf dem Programm. Nach einer Phase der Beruhigung auf dem Bibliometriesektor scheint dieses Forschungsfeld nun von der Bibliothekswissenschaft wieder eine Belebung zu erfahren. Vor allem in den 80erJahren wurden Gesetze von Bradford, Lotka und Zipf heiß diskutiert. Halbwertszeiten, Forschungsfronten und Kernzeitschriften sind Dank der Datenbanken des ISI problemlos aufzuspüren und werden gerne zur Selbstbespiegelung der Wissenschaft benutzt (Diodalo 1994). Die Zeitschrifen Scientometrics und die JASIST belegen, dass die mathematischen Modellierungen auf diesem Gebiet noch immer nicht an ihre Grenzen gestoßen sind. Und Vereinigungen wie die Gesellschaft für Wissenschaftsforschung oder die ISSI und deren Diskussionsliste oder Sigmetrics zeigen, dass nach wie vor eine starke Community auf diesem Gebiet aktiv ist. Andererseits hat der Begriff Bibliometrie ein wenig von seinem schillernden Glanz verloren und wird gerne durch Mapping, Cybermetrics (gleichnamig das "International Journal of Scientometrics, Informetrics and Bibliometrics"), Information Mining und anders in modernere Kontexte gesetzt (Park/Thelwall 2003). War es das relativierende Wissenschaftsverständnis, der Wegfall der konkurrierenden politischen Systeme oder die stürmische Medienentwicklung in der Wissenschaft, welche die Bibliometrie aus der Bibliotheks- und Informationsdiskussion vorübergehend verschwinden ließ?