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Ye, F.Y.: ¬A theoretical approach to the unification of informetric models by wave-heat equations (2011)
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- Abstract
- A simple distribution function f(x, t)=p(x+q)**-ße**alpha*t obeys wave and heat equations, that constructs a theoretical approach to the unification of informetric models, with which we can unify all informetric laws. While its space-type distributions deduce naturally Lotka-type laws in size approaches and Zipf-type laws in rank approaches, its time-type distributions introduce the mechanism of Price-type and Brookes-type laws.
-
Sieglerschmidt, J.: Wissensordnungen im analogen und im digitalen Zeitalter (2017)
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- Content
- Vgl. unter: https://books.google.de/books?hl=de&lr=&id=0rtGDwAAQBAJ&oi=fnd&pg=PA35&dq=inhaltserschlie%C3%9Fung+OR+sacherschlie%C3%9Fung&ots=5u0TwCbFqE&sig=GGw3Coc21CINkone-6Lx8LaSAjY#v=onepage&q=inhaltserschlie%C3%9Fung%20OR%20sacherschlie%C3%9Fung&f=false.
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Tran, Q.-T.: Knowledge organization systems and cultural interoperability in open humanitarian settings (2018)
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-
Yoon, J.W.; Chung, E.K.: Understanding image needs in daily life by analyzing questions in a social Q&A site (2011)
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- Abstract
- Compared with search queries, which are usually composed of a few keywords, natural language questions can demonstrate detailed information needs through searchers' richer expressions. This study aims to provide understandings of ordinary people's image needs in their daily life, by analyzing 474 questions obtained from a social question and answer (social Q&A) site. The study found that image needs reflected through the natural language questions contain several components: context of image needs (motive and intervening variables), image attributes (descriptive metadata, syntactic, and semantic attributes), and associated information (information on known/similar/comparative images and related stories). Characteristics of each component of image needs were analyzed, and accordingly image-indexing guidelines were suggested. Because image needs comprise diverse attributes, a single indexing approach might not support all complex needs for images. Therefore, this study proposes that different indexing approaches should be integrated for enhancing keyword search and browsing effectiveness. Such approaches include descriptive metadata assigned by a creator and/or automatic algorithms, user-assigned tags (or users' reactions), indexing through associated text, and content-based image retrieval.
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McTavish, J.: Everyday life classification practices and technologies : applying domain-analysis to lay understandings of food, health, and eating (2015)
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- Abstract
- Purpose - Through the application of domain-analytic principles, the purpose of this paper is to explore how participants' understandings of healthy eating are related to their grouping and classification of foods. Design/methodology/approach - In total, 30 food-interested people were asked to (1) sort a series of 56 statements about food, health, and eating on a scale from "most disagree" to "most agree"; (2)complete an open card sort of 50 foods; and (3) classify these 50 foods on a scale from "most unhealthy" to "most healthy". Exercises (1) and (3) involved Q-methodology, which groups people who share similar understandings of a phenomenon. Findings - Participants' understandings of healthy eating - revealed by the first Q-methodology exercise - were related to shared food priorities, values, and beliefs; these understandings were indirectly connected with food identities, which was not expected. This suggests that lay domain knowledge is difficult to capture and must involve other methodologies than those currently employed in domain-analytic research. Research limitations/implications - Although a small sample of food-interested people were recruited, the purpose of this study was not to make generalized claims about perspectives on healthy eating, but to explore how domain knowledge is related to everyday organizational processes. Originality/value - To "classify" in Library and Information Science (LIS) usually involves an engagement with formally established classification systems. In this paper the author suggests an alternative path for LIS scholars: the investigation of everyday life classification practices. Such an approach has value beyond the idiosyncratic, as the author discusses how these practices can inform LIS researchers' strategies for augmenting the messages provided by static classification technologies.
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Jeng, W.; DesAutels, S.; He, D.; Li, L.: Information exchange on an academic social networking site : a multidiscipline comparison on researchgate Q&A (2017)
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- Abstract
- The increasing popularity of academic social networking sites (ASNSs) requires studies on the usage of ASNSs among scholars and evaluations of the effectiveness of these ASNSs. However, it is unclear whether current ASNSs have fulfilled their design goal, as scholars' actual online interactions on these platforms remain unexplored. To fill the gap, this article presents a study based on data collected from ResearchGate. Adopting a mixed-method design by conducting qualitative content analysis and statistical analysis on 1,128 posts collected from ResearchGate Q&A, we examine how scholars exchange information and resources, and how their practices vary across three distinct disciplines: library and information services, history of art, and astrophysics. Our results show that the effect of a questioner's intention (i.e., seeking information or discussion) is greater than disciplinary factors in some circumstances. Across the three disciplines, responses to questions provide various resources, including experts' contact details, citations, links to Wikipedia, images, and so on. We further discuss several implications of the understanding of scholarly information exchange and the design of better academic social networking interfaces, which should stimulate scholarly interactions by minimizing confusion, improving the clarity of questions, and promoting scholarly content management.
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Fallaw, C.; Dunham, E.; Wickes, E.; Strong, D.; Stein, A.; Zhang, Q.; Rimkus, K.; ill Ingram, B.; Imker, H.J.: Overly honest data repository development (2016)
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-
Resnick, M.P.; Cardillo, E.; Jamoulle, M.; Araujo Novaes, M. de; Shamenek, F.S.: Towards the semantic annotation and the prevention of the loss of information of second opinion requests from rural Brazilian primary healthcare providers : the Q-codes use case - a work in progress (2018)
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Sandberg, J.; Jin, Q.: How should catalogers provide authority control for journal article authors? : Name identifiers in the linked data world (2016)
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-
Dandan, Z.; Zheng, D.; Dongfeng, Q.: ¬A survey of RDA in Asia and a vision for future development (2019)
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Nguyen, P.H.P.; Kaneiwa, K.; Nguyen, M.-Q.: Ontology inferencing rules and operations in conceptual structure theory (2010)
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Yuan, Y.C.; Zhao, X.; Liao, Q.; Chi, C.: ¬The use of different information and communication technologies to support knowledge sharing in organizations : from e-mail to micro-blogging (2013)
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Cao, Q.; Lu, Y.; Dong, D.; Tang, Z.; Li, Y.: ¬The roles of bridging and bonding in social media communities (2013)
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Jiang, S.; Gao, Q.; Chen, H.; Roco, M.C.: ¬The roles of sharing, transfer, and public funding in nanotechnology knowledge-diffusion networks (2015)
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Zhang, J.; Yu, Q.; Zheng, F.; Long, C.; Lu, Z.; Duan, Z.: Comparing keywords plus of WOS and author keywords : a case study of patient adherence research (2016)
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Yan, E.; Yu, Q.: Using path-based approaches to examine the dynamic structure of discipline-level citation networks (2016)
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Zhou, Q.; Leydesdorff, L.: ¬The normalization of occurrence and co-occurrence matrices in bibliometrics using Cosine similarities and Ochiai coefficients (2016)
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Wang, Q.: ¬A bibliometric model for identifying emerging research topics (2018)
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Alzahrani, S.; Palade, V.; Salim, N.; Abraham, A.: Using structural information and citation evidence to detect significant plagiarism cases in scientific publications (2012)
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- Abstract
- In plagiarism detection (PD) systems, two important problems should be considered: the problem of retrieving candidate documents that are globally similar to a document q under investigation, and the problem of side-by-side comparison of q and its candidates to pinpoint plagiarized fragments in detail. In this article, the authors investigate the usage of structural information of scientific publications in both problems, and the consideration of citation evidence in the second problem. Three statistical measures namely Inverse Generic Class Frequency, Spread, and Depth are introduced to assign a degree of importance (i.e., weight) to structural components in scientific articles. A term-weighting scheme is adjusted to incorporate component-weight factors, which is used to improve the retrieval of potential sources of plagiarism. A plagiarism screening process is applied based on a measure of resemblance, in which component-weight factors are exploited to ignore less or nonsignificant plagiarism cases. Using the notion of citation evidence, parts with proper citation evidence are excluded, and remaining cases are suspected and used to calculate the similarity index. The authors compare their approach to two flat-based baselines, TF-IDF weighting with a Cosine coefficient, and shingling with a Jaccard coefficient. In both baselines, they use different comparison units with overlapping measures for plagiarism screening. They conducted extensive experiments using a dataset of 15,412 documents divided into 8,657 source publications and 6,755 suspicious queries, which included 18,147 plagiarism cases inserted automatically. Component-weight factors are assessed using precision, recall, and F-measure averaged over a 10-fold cross-validation and compared using the ANOVA statistical test. Results from structural-based candidate retrieval and plagiarism detection are evaluated statistically against the flat baselines using paired-t tests on 10-fold cross-validation runs, which demonstrate the efficacy achieved by the proposed framework. An empirical study on the system's response shows that structural information, unlike existing plagiarism detectors, helps to flag significant plagiarism cases, improve the similarity index, and provide human-like plagiarism screening results.
-
Hu, K.; Luo, Q.; Qi, K.; Yang, S.; Mao, J.; Fu, X.; Zheng, J.; Wu, H.; Guo, Y.; Zhu, Q.: Understanding the topic evolution of scientific literatures like an evolving city : using Google Word2Vec model and spatial autocorrelation analysis (2019)
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