-
Song, M.; Kang, K.; An, J.Y.: Investigating drug-disease interactions in drug-symptom-disease triples via citation relations (2018)
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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
-
Tu, Y.-N.; Hsu, S.-L.: Constructing conceptual trajectory maps to trace the development of research fields (2016)
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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
-
Jonkers, K.; Moya Anegon, F. de; Aguillo, I.F.: Measuring the usage of e-research infrastructure as an indicator of research activity (2012)
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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.
-
He, B.; Ding, Y.; Ni, C.: Mining enriched contextual information of scientific collaboration : a meso perspective (2011)
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-
Rokach, L.; Kalech, M.; Blank, I.; Stern, R.: Who is going to win the next Association for the Advancement of Artificial Intelligence Fellowship Award? : evaluating researchers by mining bibliographic data (2011)
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-
Lievers, W.B.; Pilkey, A.K.: Characterizing the frequency of repeated citations : the effects of journal, subject area, and self-citation (2012)
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- Abstract
- Previous studies have repeatedly demonstrated that the relevance of a citing document is related to the number of times with which the source document is cited. Despite the ease with which electronic documents would permit the incorporation of this information into citation-based document search and retrieval systems, the possibilities of repeated citations remain untapped. Part of this under-utilization may be due to the fact that very little is known regarding the pattern of repeated citations in scholarly literature or how this pattern may vary as a function of journal, academic discipline or self-citation. The current research addresses these unanswered questions in order to facilitate the future incorporation of repeated citation information into document search and retrieval systems. Using data mining of electronic texts, the citation characteristics of nine different journals, covering the three different academic fields (economics, computing, and medicine & biology), were characterized. It was found that the frequency (f) with which a reference is cited N or more times within a document is consistent across the sampled journals and academic fields. Self-citation causes an increase in frequency, and this effect becomes more pronounced for large N. The objectivity, automatability, and insensitivity of repeated citations to journal and discipline, present powerful opportunities for improving citation-based document search.
-
Song, M.; Kim, S.Y.; Lee, K.: Ensemble analysis of topical journal ranking in bioinformatics (2017)
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- Abstract
- Journal rankings, frequently determined by the journal impact factor or similar indices, are quantitative measures for evaluating a journal's performance in its discipline, which is presently a major research thrust in the bibliometrics field. Recently, text mining was adopted to augment journal ranking-based evaluation with the content analysis of a discipline taking a time-variant factor into consideration. However, previous studies focused mainly on a silo analysis of a discipline using either citation-or content-oriented approaches, and no attempt was made to analyze topical journal ranking and its change over time in a seamless and integrated manner. To address this issue, we propose a journal-time-topic model, an extension of Dirichlet multinomial regression, which we applied to the field of bioinformatics to understand journal contribution to topics in a field and the shift of topic trends. The journal-time-topic model allows us to identify which journals are the major leaders in what topics and the manner in which their topical focus. It also helps reveal an interesting distinct pattern in the journal impact factor of high- and low-ranked journals. The study results shed a new light for understanding topic specific journal rankings and shifts in journals' concentration on a subject.
-
Stuart, D.: Web metrics for library and information professionals (2014)
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- LCSH
- Data mining
- Subject
- Data mining
-
Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014)
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- Date
- 18. 3.2014 19:13:22
-
Scholarly metrics under the microscope : from citation analysis to academic auditing (2015)
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- Date
- 22. 1.2017 17:12:50
-
Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
-
Ohly, P.: Dimensions of globality : a bibliometric analysis (2016)
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- Date
- 20. 1.2019 11:22:31
-
Crespo, J.A.; Herranz, N.; Li, Y.; Ruiz-Castillo, J.: ¬The effect on citation inequality of differences in citation practices at the web of science subject category level (2014)
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- Abstract
- This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.
-
Yan, E.: Finding knowledge paths among scientific disciplines (2014)
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- Date
- 26.10.2014 20:22:22
-
Zhu, Q.; Kong, X.; Hong, S.; Li, J.; He, Z.: Global ontology research progress : a bibliometric analysis (2015)
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- Date
- 20. 1.2015 18:30:22
17. 9.2018 18:22:23
-
Campanario, J.M.: Large increases and decreases in journal impact factors in only one year : the effect of journal self-citations (2011)
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- Date
- 22. 1.2011 12:53:00
-
Ding, Y.: Applying weighted PageRank to author citation networks (2011)
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- Date
- 22. 1.2011 13:02:21
-
Schlögl, C.: Internationale Sichtbarkeit der europäischen und insbesondere der deutschsprachigen Informationswissenschaft (2013)
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- Date
- 22. 3.2013 14:04:09
-
Vieira, E.S.; Cabral, J.A.S.; Gomes, J.A.N.F.: Definition of a model based on bibliometric indicators for assessing applicants to academic positions (2014)
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- Date
- 18. 3.2014 18:22:21
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Ajiferuke, I.; Lu, K.; Wolfram, D.: ¬A comparison of citer and citation-based measure outcomes for multiple disciplines (2010)
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- Date
- 28. 9.2010 12:54:22
