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Source

Journal of the Association for Information Science and Technology. 68(2017) no.12, S.2846

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Abstract

This article provides an alternative perspective for measuring author impact by applying PageRank algorithm to a coauthorship network. A weighted PageRank algorithm considering citation and coauthorship network topology is proposed. We test this algorithm under different damping factors by evaluating author impact in the informetrics research community. In addition, we also compare this weighted PageRank with the h-index, citation, and program committee (PC) membership of the International Society for Scientometrics and Informetrics (ISSI) conferences. Findings show that this weighted PageRank algorithm provides reliable results in measuring author impact.

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Abstract

This article analyzes the context of citations within the full text of research articles. It studies articles published in a single journal: the Journal of Informetrics (JOI), in the first year the journal was published, 2007. The analysis classified the citations into in- and out-disciplinary content and looked at their use within the articles' sections such as introduction, literature review, methodology, findings, discussion, and conclusions. In addition, it took into account the age of cited articles. A thematic analysis of these citations was performed in order to identify the evolution of topics within the articles sections and the journal's content. A matrix describing the relationships between the citations' use, and their in- and out-disciplinary focus within the articles' sections is presented. The findings show that an analysis of citations based on their in- and out-disciplinary orientation within the context of the articles' sections can be an indication of the manner by which cross-disciplinary science works, and reveals the connections between the issues, methods, analysis, and conclusions coming from different research disciplines.

Object

Journal of informetrics

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.9, S.1903-1913

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Abstract

Purpose - The aim of this paper is to propose a conceptual model of the field of informetrics. Specifically, the paper argues that informetrics comprises the study of entities in three dimensions: the social, documentary and epistemic dimensions containing respectively agents, documents, and concepts or cognitions. Design/methodology/approach - The paper outlines a conceptual model, drawing on earlier work by Kochen, Leydesdorff, Borgman and others. Subsequently, each dimension and interdimensional relation is analyzed and discussed. Findings - It is shown that not every study necessarily involves each of the three dimensions, but that the field as a whole cannot be reduced to one or two of them. Moreover, the dimensions should be kept separate but they are not completely independent. The paper discusses what kinds of relations exist between the dimensions. Special attention is given to the nature of the citation relation within this framework. The paper also considers the place of concepts like mapping, proximity and influence in the model. Research limitations/implications - This conceptual paper is a first step. Multi-relational networks may be a key instrument to further the study of the interplay between the three dimensions. Originality/value - The paper provides a framework to characterise informetric studies and makes the characteristics of the field explicit.

Source

Journal of documentation. 69(2013) no.2, S.295-308

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Abstract

In one of the first attempts at providing a mathematical framework for the Hirsch index, Egghe and Rousseau (2006) assumed the standard Lotka model for an author's citation distribution to derive a delightfully simple closed formula for his/her h-index. More recently, the same authors (Egghe & Rousseau, 2012b) have presented a new (implicit) formula based on the so-called shifted Lotka function to allow for the objection that the original model makes no allowance for papers receiving zero citations. Here it is shown, through a small empirical study, that the formulae actually give very similar results whether or not the uncited papers are included. However, and more important, it is found that they both seriously underestimate the true h-index, and we suggest that the reason for this is that this is a context-the citation distribution of an author-in which straightforward Lotkaian informetrics is inappropriate. Indeed, the analysis suggests that even if we restrict attention to the upper tail of the citation distribution, a simple Lotka/Pareto-like model can give misleading results.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1504-1514

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Abstract

Scientometrics have become an essential element in the practice and evaluation of science and research, including both the evaluation of individuals and national assessment exercises. This book brings together the theories that guide informetrics and scholarly communication research. It is a much needed compilation by leading scholars in the field that gathers together the theories that guide our understanding of authorship, citing, and impact

Content

Frontmatter -- -- Foreword -- -- Prologue -- -- Contents -- -- Introduction -- -- Part I: Critical informetrics -- -- The Incessant Chattering of Texts -- -- Informetrics Needs a Foundation in the Theory of Science -- -- Part II: Citation theories -- -- Referencing as Cooperation or Competition -- -- Semiotics and Citations -- -- Data Citation as a Bibliometric Oxymoron -- -- Part III: Statistical theories -- -- TypeToken Theory and Bibliometrics -- -- From a Success Index to a Success Multiplier -- -- From Matthew to Hirsch: A Success-Breeds-Success Story -- -- Informations Magic Numbers: The Numerology of Information Science -- -- Part IV: Authorship theories -- -- Authors as Persons and Authors as Bundles of Words -- -- The Angle Sum Theory: Exploring the Literature on Acknowledgments in Scholarly Communication -- -- The Flesh of Science: Somatics and Semiotics -- -- Part V: Knowledge organization theories -- -- Informetric Analyses of Knowledge Organization Systems (KOSs) -- -- Information, Meaning, and Intellectual Organization in Networks of Inter-Human Communication -- -- Modeling the Structure and Dynamics of Science Using Books -- -- Part VI: Altmetric theories -- -- Webometrics and Altmetrics: Home Birth vs. Hospital Birth -- -- Scientific Revolution in Scientometrics: The Broadening of Impact from Citation to Societal -- -- Altmetrics as Traces of the Computerization of the Research Process -- -- Interpreting Altmetrics: Viewing Acts on Social Media through the Lens of Citation and Social Theories -- -- Biographical information for the editor and contributors -- -- Index

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Abstract

The h-index, as originally proposed (Hirsch, 2005), is a purely heuristic construction. Burrell (2013) showed that efforts to derive formulae from the mathematical framework of Lotkaian informetrics could lead to misleading results. On this note, we argue that a simple heuristic "thermodynamical" model can enable a better three-dimensional (3D) evaluation of the information production process leading to what we call the zynergy-index.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.2, S.426-427

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Abstract

The research question studied in this contribution is how to find an adequate representation to describe the diffusion of scientific ideas over time. We claim that citation data, at least of articles that act as concept symbols, can be considered to contain this information. As a case study we show how the founding article by Nobel Prize winner Kao illustrates the evolution of the field of fiber optics communication. We use a continuous description of discrete citation data in order to accentuate turning points and breakthroughs in the history of this field. Applying the principles explained in this contribution informetrics may reveal the trajectories along which science is developing.

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Abstract

The publication performance of 30 scientometricians is studied. The individuals are classified into 3 cohorts according to their manifested professional recognition, as Price medalists (Pm), members of the editorial board of Scientometrics and the Journal of Informetrics (Rw), and session chairs (Sc) at an International Society of Scientometrics and Informetrics (ISSI) conference. Several core impact indicators are calculated: h, g, p, citation distribution score (CDS), percentage rank position (PRP), and weight of influence of papers (WIP10). The indices significantly correlate with each other. The mean value of the indices of the cohorts decreases parallel with the decrease in professional recognition: Pm?>?Rw?>?Sc. The 30 scientometricians studied were clustered according to the core impact indices. The members in the clusters so obtained overlap only partly with the members in the cohorts made by professional recognition. The Total Overlap is calculated by dividing the sum of the diagonal elements in the cohorts-clusters matrix with the total number of elements, times 100. The highest overlap (76.6%) was obtained with the g-index. Accordingly, the g-index seems to have the greatest discriminative power in the system studied. The cohorts-clusters method may be used for validating scientometric indicators.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.1, S.234-242

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Abstract

This paper examines the influence of the works of Hope A. Olson by conducting an ego-centric informetric analysis of her published works. Publication and citation data were collected from Google Scholar and the Thomson Reuters Web of Science. Classic informetrics techniques were applied to the datasets including co-authorship analysis, citer analysis, citation and co-citation analysis and text-based analysis. Co-citation and text-based data were analyzed and visualized using VOSviewer and CiteSpace, respectively. The analysis of her citation identity reveals how Dr. Olson situates her own research within the knowledge landscape while the analysis of her citation image reveals how others have situated her work in relation to the authors with whom she has been co-cited. This reflection of Dr. Olson's research contributions reveals the influence of her scholarship not only on knowledge organization but other areas of library and information science and allied disciplines.

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Abstract

This study investigates how computational overhead for topic model training may be reduced by selectively removing terms from the vocabulary of text corpora being modeled. We compare the impact of removing singly occurring terms, the top 0.5%, 1% and 5% most frequently occurring terms and both top 0.5% most frequent and singly occurring terms, along with changes in the number of topics modeled (10, 20, 30, 40, 50, 100) using three datasets. Four outcome measures are compared. The removal of singly occurring terms has little impact on outcomes for all of the measures tested. Document discriminative capacity, as measured by the document space density, is reduced by the removal of frequently occurring terms, but increases with higher numbers of topics. Vocabulary size does not greatly influence entropy, but entropy is affected by the number of topics. Finally, topic similarity, as measured by pairwise topic similarity and Jensen-Shannon divergence, decreases with the removal of frequent terms. The findings have implications for information science research in information retrieval and informetrics that makes use of topic modeling.

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Abstract

In informetrics, journals have been used as a standard unit to analyze research impact, productivity, and scholarship. The increasing practice of interdisciplinary research challenges the effectiveness of journal-based assessments. The aim of this article is to highlight topics as a valuable unit of analysis. A set of topic-based approaches is applied to a data set on library and information science publications. Results show that topic-based approaches are capable of revealing the research dynamics, impact, and dissemination of the selected data set. The article also identifies a nonsignificant relationship between topic popularity and impact and argues for the need to use both variables in describing topic characteristics. Additionally, a flow map illustrates critical topic-level knowledge dissemination channels.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.11, S.2357-2372

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Abstract

In this article we describe another problem with journal impact factors by showing that one journal's impact factor is dependent on other journals' publication delays. The proposed theoretical model predicts a monotonically decreasing function of the impact factor as a function of publication delay, on condition that the citation curve of the journal is monotone increasing during the publication window used in the calculation of the journal impact factor; otherwise, this function has a reversed U shape. Our findings based on simulations are verified by examining three journals in the information sciences: the Journal of Informetrics, Scientometrics, and the Journal of the Association for Information Science and Technology.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.3, S.780-789

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Abstract

Author-selected keywords have been widely utilized for indexing, information retrieval, bibliometrics and knowledge organization in previous studies. However, few studies exist con-cerning how author-selected keywords function semantically in scientific manuscripts. In this paper, we investigated this problem from the perspective of term function (TF) by devising indica-tors of the diversity and symmetry of keyword term functions in papers, as well as the intensity of individual term functions in papers. The data obtained from the whole Journal of Informetrics(JOI) were manually processed by an annotation scheme of key-word term functions, including "research topic," "research method," "research object," "research area," "data" and "others," based on empirical work in content analysis. The results show, quantitatively, that the diversity of keyword term function de-creases, and the irregularity increases with the number of author-selected keywords in a paper. Moreover, the distribution of the intensity of individual keyword term function indicated that no significant difference exists between the ranking of the five term functions with the increase of the number of author-selected keywords (i.e., "research topic" > "research method" > "research object" > "research area" > "data"). The findings indicate that precise keyword related research must take into account the dis-tinct types of author-selected keywords.

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Abstract

On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts. We call these attributes facets: classification has a few facets such as application (e.g., face recognition), model (e.g., svm, knn), and metric (e.g., precision). In this work, we aim at building faceted concept hierarchies from scientific literature. Hierarchy construction methods heavily rely on hypernym detection, however, the faceted relations are parent-to-child links but the hypernym relation is a multi-hop, i.e., ancestor-to-descendent link with a specific facet "type-of". We use information extraction techniques to find synonyms, sibling concepts, and ancestor-descendent relations from a data science corpus. And we propose a hierarchy growth algorithm to infer the parent-child links from the three types of relationships. It resolves conflicts by maintaining the acyclic structure of a hierarchy.

Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

Source

Graph-Based Methods for Natural Language Processing - proceedings of the Thirteenth Workshop (TextGraphs-13): November 4, 2019, Hong Kong : EMNLP-IJCNLP 2019. Ed.: Dmitry Ustalov

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Abstract

Use of research synthesis methods has contributed to changes in research practices. In disciplinary literatures, authors indicate motivations to use the methods include needs to (a) translate research-based knowledge to inform practice and policy decisions, and (b) integrate relatively large and diverse knowledge bases to increase the generality of results and yield novel insights or explanations. This review presents two histories of the diffusion of research synthesis methods: a narrative history based primarily in the health and social sciences; and a bibliometric overview across science broadly. Engagement with research synthesis was strongly correlated with evidence-based practice (EBP), and moderately with review prevalence. The social sciences were most diverse in terms of when research synthesis was adopted. Technology, physical sciences, and math appear to be relatively resistant though fields such as physics may be considered to have used similar methods long ago. Additional research is needed to assess the consequences of adoption within fields, including changes in how researchers engage with knowledge resources. This review demonstrates that particularistic histories of science and technology may be fruitfully augmented with informetrics to examine how disciplinary diffusion narratives coincide with patterns across science more broadly, thereby opening up disciplinary knowledge to inform future research.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.12, S.2693-2708

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Abstract

While classifications are heavily used to categorize web content, the evolution of the web foresees a more formal structure - ontology - which can serve this purpose. Ontologies are core artifacts of the Semantic Web which enable machines to use inference rules to conduct automated reasoning on data. Lightweight ontologies bridge the gap between classifications and ontologies. A lightweight ontology (LO) is an ontology representing a backbone taxonomy where the concept of the child node is more specific than the concept of the parent node. Formal lightweight ontologies can be generated from their informal ones. The key applications of formal lightweight ontologies are document classification, semantic search, and data integration. However, these applications suffer from the following problems: the disambiguation accuracy of the state of the art NLP tools used in generating formal lightweight ontologies from their informal ones; the lack of background knowledge needed for the formal lightweight ontologies; and the limitation of ontology reuse. In this dissertation, we propose a novel solution to these problems in formal lightweight ontologies; namely, faceted lightweight ontology (FLO). FLO is a lightweight ontology in which terms, present in each node label, and their concepts, are available in the background knowledge (BK), which is organized as a set of facets. A facet can be defined as a distinctive property of the groups of concepts that can help in differentiating one group from another. Background knowledge can be defined as a subset of a knowledge base, such as WordNet, and often represents a specific domain.

Content

PhD Dissertation at International Doctorate School in Information and Communication Technology. Vgl.: https%3A%2F%2Fcore.ac.uk%2Fdownload%2Fpdf%2F150083013.pdf&usg=AOvVaw2n-qisNagpyT0lli_6QbAQ.

Imprint

Trento : University / Department of information engineering and computer science

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Abstract

Converting UDC numbers manually to a complex format such as the one mentioned above is an unrealistic expectation; supporting building these representations, as far as possible automatically, is a well-founded requirement. An additional advantage of this approach is that the existing records could also be processed and converted. In my dissertation I would like to prove also that it is possible to design and implement an algorithm that is able to convert pre-coordinated UDC numbers into the introduced format by identifying all their elements and revealing their whole syntactic structure as well. In my dissertation I will discuss a feasible way of building a UDC-specific XML schema for describing the most detailed and complicated UDC numbers (containing not only the common auxiliary signs and numbers, but also the different types of special auxiliaries). The schema definition is available online at: http://piros.udc-interpreter.hu#xsd. The primary goal of my research is to prove that it is possible to support building, retrieving, and analyzing UDC numbers without compromises, by taking the whole syntactic richness of the scheme by storing the UDC numbers reserving the meaning of pre-coordination. The research has also included the implementation of a software that parses UDC classmarks attended to prove that such solution can be applied automatically without any additional effort or even retrospectively on existing collections.

Content

Vgl. auch: New automatic interpreter for complex UDC numbers. Unter: <https%3A%2F%2Fudcc.org%2Ffiles%2FAttilaPiros_EC_36-37_2014-2015.pdf&usg=AOvVaw3kc9CwDDCWP7aArpfjrs5b>

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Content

Vgl.: http%3A%2F%2Fcreativechoice.org%2Fdoc%2FHansJonas.pdf&usg=AOvVaw1TM3teaYKgABL5H9yoIifA&opi=89978449.