Search (134 results, page 1 of 7)

  • × year_i:[2020 TO 2030}
  1. Li, G.; Siddharth, L.; Luo, J.: Embedding knowledge graph of patent metadata to measure knowledge proximity (2023) 0.08 
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    Abstract
    Knowledge proximity refers to the strength of association between any two entities in a structural form that embodies certain aspects of a knowledge base. In this work, we operationalize knowledge proximity within the context of the US Patent Database (knowledge base) using a knowledge graph (structural form) named "PatNet" built using patent metadata, including citations, inventors, assignees, and domain classifications. We train various graph embedding models using PatNet to obtain the embeddings of entities and relations. The cosine similarity between the corresponding (or transformed) embeddings of entities denotes the knowledge proximity between these. We compare the embedding models in terms of their performances in predicting target entities and explaining domain expansion profiles of inventors and assignees. We then apply the embeddings of the best-preferred model to associate homogeneous (e.g., patent-patent) and heterogeneous (e.g., inventor-assignee) pairs of entities.
    Date
    22. 3.2023 12:06:55
  2. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.06 
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    Abstract
    Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter-/cross-/multi-disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co-topic layer and a co-authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments-one with a local dataset and the other with a global dataset-demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.
    Date
    22. 6.2023 18:07:12
  3. Kuehn, E.F.: ¬The information ecosystem concept in information literacy : a theoretical approach and definition (2023) 0.06 
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    Abstract
    Despite the prominence of the concept of the information ecosystem (hereafter IE) in information literacy documents and literature, it is under-theorized. This article proposes a general definition of IE for information literacy. After reviewing the current use of the IE concept in the Association of College and Research Libraries (ACRL) Framework for Information Literacy and other information literacy sources, existing definitions of IE and similar concepts (e.g., "evidence ecosystems") will be examined from other fields. These will form the basis of the definition of IE proposed in the article for the field of information literacy: "all structures, entities, and agents related to the flow of semantic information relevant to a research domain, as well as the information itself."
    Date
    22. 3.2023 11:52:50
  4. Stephens, B.; Cummings, J.N.: Knowledge creation through collaboration : the role of shared institutional affiliations and physical proximity (2021) 0.03 
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    Abstract
    This paper examines how shared affiliations within an institution (e.g., same primary appointment, same secondary appointment, same research center, same laboratory/facility) and physical proximity (e.g., walking distance between collaborator offices) shape knowledge creation through biomedical science collaboration in general, and interdisciplinary collaboration in particular. Using archival and publication data, we examine pairwise research collaborations among 1,138 faculty members over a 12-year period at a medical school in the United States. Modeling at the dyadic level, we find that faculty members with more shared institutional affiliations are positively associated with knowledge creation and knowledge impact, and that this association is moderated by the physical proximity of collaborators. We further find that the positive influence of disciplinary diversity (e.g., collaborators from different fields) on knowledge impact is stronger among pairs that share more affiliations and is significantly reduced as the physical distance among collaborators increases. These results support the idea that shared institutional affiliations and physical proximity can increase interpersonal contact, providing more opportunities to develop trust and mutual understanding, and thus alleviating some of the coordination issues that can arise with higher disciplinary diversity. We discuss the implications for future research on scientific collaborations, managerial practice regarding office space allocation, and strategic planning of initiatives aimed at promoting interdisciplinary collaboration.
  5. Xu, H.; Bu, Y.; Liu, M.; Zhang, C.; Sun, M.; Zhang, Y.; Meyer, E.; Salas, E.; Ding, Y.: Team power dynamics and team impact : new perspectives on scientific collaboration using career age as a proxy for team power (2022) 0.03 
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    Abstract
    Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.
  6. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.03 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  7. Sjögårde, P.; Ahlgren, P.; Waltman, L.: Algorithmic labeling in hierarchical classifications of publications : evaluation of bibliographic fields and term weighting approaches (2021) 0.02 
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    Abstract
    Algorithmic classifications of research publications can be used to study many different aspects of the science system, such as the organization of science into fields, the growth of fields, interdisciplinarity, and emerging topics. How to label the classes in these classifications is a problem that has not been thoroughly addressed in the literature. In this study, we evaluate different approaches to label the classes in algorithmically constructed classifications of research publications. We focus on two important choices: the choice of (a) different bibliographic fields and (b) different approaches to weight the relevance of terms. To evaluate the different choices, we created two baselines: one based on the Medical Subject Headings in MEDLINE and another based on the Science-Metrix journal classification. We tested to what extent different approaches yield the desired labels for the classes in the two baselines. Based on our results, we recommend extracting terms from titles and keywords to label classes at high levels of granularity (e.g., topics). At low levels of granularity (e.g., disciplines) we recommend extracting terms from journal names and author addresses. We recommend the use of a new approach, term frequency to specificity ratio, to calculate the relevance of terms.
  8. Ikae, C.; Savoy, J.: Gender identification on Twitter (2022) 0.02 
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    Abstract
    To determine the author of a text's gender, various feature types have been suggested (e.g., function words, n-gram of letters, etc.) leading to a huge number of stylistic markers. To determine the target category, different machine learning models have been suggested (e.g., logistic regression, decision tree, k nearest-neighbors, support vector machine, naïve Bayes, neural networks, and random forest). In this study, our first objective is to know whether or not the same model always proposes the best effectiveness when considering similar corpora under the same conditions. Thus, based on 7 CLEF-PAN collections, this study analyzes the effectiveness of 10 different classifiers. Our second aim is to propose a 2-stage feature selection to reduce the feature size to a few hundred terms without any significant change in the performance level compared to approaches using all the attributes (increase of around 5% after applying the proposed feature selection). Based on our experiments, neural network or random forest tend, on average, to produce the highest effectiveness. Moreover, empirical evidence indicates that reducing the feature set size to around 300 without penalizing the effectiveness is possible. Finally, based on such reduced feature sizes, an analysis reveals some of the specific terms that clearly discriminate between the 2 genders.
  9. Qin, C.; Liu, Y.; Ma, X.; Chen, J.; Liang, H.: Designing for serendipity in online knowledge communities : an investigation of tag presentation formats and openness to experience (2022) 0.02 
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    Abstract
    Users increasingly acquire, share, and create knowledge in online knowledge communities, making them massive dynamic knowledge repositories that spark inspiration. While online knowledge communities provide powerful searching tools, they ignore the potential of serendipity in fostering knowledge acquisition. Against this backdrop, this research investigates whether serendipity can be stimulated by design features of communities. Specifically, we examine whether different tag presentation formats may promote serendipity. Two hundred seven participants were randomly assigned to our experimental website that displays one of three tag formats. Results show that users experienced serendipity more frequently while using tag trees than tag clouds, followed by tag lists. Moreover, tag formats moderate how openness to experience affects serendipity. Although openness did not influence serendipity across tag formats, further analysis shows that it significantly decreases serendipity for tag lists, but significantly increases serendipity for tag clouds and trees. Theoretically, these results provide an in-depth understanding of serendipity that is contingent on the interaction between community design features and personality (e.g., openness to experience). Practically, these findings demonstrate how interface features (e.g., tag presentation formats) facilitate serendipity, thus informing better design of online knowledge communities to improve the efficiency of knowledge acquisition.
  10. Yu, M.; Sun, A.: Dataset versus reality : understanding model performance from the perspective of information need (2023) 0.02 
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    Abstract
    Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real-world problems with similar settings (e.g., identical input/output) to the benchmark datasets? We argue that a model is trained to answer the same information need in a similar context (e.g., the information available), for which the training dataset is created. The trained model may be used to solve real-world problems for a similar information need in a similar context. However, information need is independent of the format of dataset input/output. Although some datasets may share high structural similarities, they may represent different research tasks aiming for answering different information needs. Examples are question-answer pairs for the question answering (QA) task, and image-caption pairs for the image captioning (IC) task. In this paper, we use the QA task and IC task as two case studies and compare their widely used benchmark datasets. From the perspective of information need in the context of information retrieval, we show the differences in the dataset creation processes and the differences in morphosyntactic properties between datasets. The differences in these datasets can be attributed to the different information needs and contexts of the specific research tasks. We encourage all researchers to consider the information need perspective of a research task when selecting the appropriate datasets to train a model. Likewise, while creating a dataset, researchers may also incorporate the information need perspective as a factor to determine the degree to which the dataset accurately reflects the real-world problem or the research task they intend to tackle.
  11. Potnis, D.; Halladay, M.; Jones, S.-E.: Consequences of information exchanges of vulnerable women on Facebook : an "information grounds" study informing value co-creation and ICT4D research (2023) 0.02 
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    Abstract
    Information and communication technology for development (ICT4D) research sporadically leverages information science scholarship. Our qualitative study employs the "information grounds" (IG) lens to investigate the consequences of information exchanges by pregnant women on Facebook, who are vulnerable in the doctor-centric birth culture in rural America. The thematic analysis of in-depth interviews with members and administrators of the Vaginal Birth After Cesarean (VBAC) group shows that positive consequences outweigh negative consequences of information exchanges and lead to the following progression of outcomes: (a) VBAC group as an information ground, (b) social capital (e.g., cognitive, structural, and relational capital) built on the information ground, (c) seven emergent properties of the information ground, and (d) value co-created (e.g., local, affordable, timely, enduring, and reliable support) by VBAC group members. The IG lens reveals the following roles of Facebook, an ICT, in development: (a) a linker that lets people with similar needs and interests convene and shapes their interactions, (b) a prerequisite to building an online, "third place" for social interactions, and (c) an apparatus for ubiquitously seeking, searching, sharing, and storing information in multiple formats and controlling its flow on the VBAC group. This paper fills in six gaps in the ICT4D research.
  12. Zhou, H.; Dong, K.; Xia, Y.: Knowledge inheritance in disciplines : quantifying the successive and distant reuse of references (2023) 0.02 
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    Abstract
    How the knowledge base of disciplines grows, renews, and decays informs their distinct characteristics and epistemology. Here we track the evolution of knowledge bases of 19 disciplines for over 45 years. We introduce the notation of knowledge inheritance as the overlap in the set of references between years. We discuss two modes of knowledge inheritance of disciplines-successive and distant. To quantify the status and propensity of knowledge inheritance for disciplines, we propose two indicators: one descriptively describes knowledge base evolution, and one estimates the propensity of knowledge inheritance. When observing the continuity in knowledge bases for disciplines, we show distinct patterns for STEM and SS&H disciplines: the former inherits knowledge bases more successively, yet the latter inherits significantly from distant knowledge bases. We further discover stagnation or revival in knowledge base evolution where older knowledge base ceases to decay after 10 years (e.g., Physics and Mathematics) and are increasingly reused (e.g., Philosophy). Regarding the propensity of inheriting prior knowledge bases, we observe unanimous rises in both successive and distant knowledge inheritance. We show that knowledge inheritance could reveal disciplinary characteristics regarding the trajectory of knowledge base evolution and interesting insights into the metabolism and maturity of scholarly communication.
  13. Furner, J.: Classification of the sciences in Greco-Roman Antiquity (2021) 0.02 
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    Abstract
    A review is undertaken of the contributions of 38 classical authors, from Pythagoras in the 6th century BCE to Isidore in the 6th century CE, to the classification of the sciences. Such classifications include some that are more theoretical in function, some that are more practical (e.g., encyclopedic, bibliographic, or curricular). The emergence of the quadrivium and trivium is charted; the Greek concept of "enkýklios paideía" and the Latin term "artes liberales" are defined; and the ways in which the form, content, and function of science classifications change during this period are assessed.
  14. Dietz, K.: en.wikipedia.org > 6 Mio. Artikel (2020) 0.02 
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    Content
    "Die Englischsprachige Wikipedia verfügt jetzt über mehr als 6 Millionen Artikel. An zweiter Stelle kommt die deutschsprachige Wikipedia mit 2.3 Millionen Artikeln, an dritter Stelle steht die französischsprachige Wikipedia mit 2.1 Millionen Artikeln (via Researchbuzz: Firehose <https://rbfirehose.com/2020/01/24/techcrunch-wikipedia-now-has-more-than-6-million-articles-in-english/> und Techcrunch <https://techcrunch.com/2020/01/23/wikipedia-english-six-million-articles/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+Techcrunch+%28TechCrunch%29&guccounter=1&guce_referrer=aHR0cHM6Ly9yYmZpcmVob3NlLmNvbS8yMDIwLzAxLzI0L3RlY2hjcnVuY2gtd2lraXBlZGlhLW5vdy1oYXMtbW9yZS10aGFuLTYtbWlsbGlvbi1hcnRpY2xlcy1pbi1lbmdsaXNoLw&guce_referrer_sig=AQAAAK0zHfjdDZ_spFZBF_z-zDjtL5iWvuKDumFTzm4HvQzkUfE2pLXQzGS6FGB_y-VISdMEsUSvkNsg2U_NWQ4lwWSvOo3jvXo1I3GtgHpP8exukVxYAnn5mJspqX50VHIWFADHhs5AerkRn3hMRtf_R3F1qmEbo8EROZXp328HMC-o>). 250120 via digithek ch = #fineBlog s.a.: Angesichts der Veröffentlichung des 6-millionsten Artikels vergangene Woche in der englischsprachigen Wikipedia hat die Community-Zeitungsseite "Wikipedia Signpost" ein Moratorium bei der Veröffentlichung von Unternehmensartikeln gefordert. Das sei kein Vorwurf gegen die Wikimedia Foundation, aber die derzeitigen Maßnahmen, um die Enzyklopädie gegen missbräuchliches undeklariertes Paid Editing zu schützen, funktionierten ganz klar nicht. *"Da die ehrenamtlichen Autoren derzeit von Werbung in Gestalt von Wikipedia-Artikeln überwältigt werden, und da die WMF nicht in der Lage zu sein scheint, dem irgendetwas entgegenzusetzen, wäre der einzige gangbare Weg für die Autoren, fürs erste die Neuanlage von Artikeln über Unternehmen zu untersagen"*, schreibt der Benutzer Smallbones in seinem Editorial <https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_Signpost/2020-01-27/From_the_editor> zur heutigen Ausgabe."
  15. Gabler, S.: Vergabe von DDC-Sachgruppen mittels eines Schlagwort-Thesaurus (2021) 0.02 
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    Content
    Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.
  16. ¬Der Student aus dem Computer (2023) 0.02 
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    Date
    27. 1.2023 16:22:55
  17. Thelwall, M.; Foster, D.: Male or female gender-polarized YouTube videos are less viewed (2021) 0.02 
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    Abstract
    As one of the world's most visited websites, YouTube is potentially influential for learning gendered attitudes. Nevertheless, despite evidence of gender influences within the site for some topics, the extent to which YouTube reflects or promotes male/female or other gender divides is unknown. This article analyses 10,211 YouTube videos published in 12 months from 2014 to 2015 using commenter-portrayed genders (inferred from usernames) and view counts from the end of 2019. Nonbinary genders are omitted for methodological reasons. Although there were highly male and female topics or themes (e.g., vehicles or beauty) and male or female gendering is the norm, videos with topics attracting both males and females tended to have more viewers (after approximately 5 years) than videos in male or female gendered topics. Similarly, within each topic, videos with gender balanced sets of commenters tend to attract more viewers. Thus, YouTube does not seem to be driving male-female gender differences.
  18. Jaeger, L.: Wissenschaftler versus Wissenschaft (2020) 0.02 
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    Date
    2. 3.2020 14:08:22
  19. Ibrahim, G.M.; Taylor, M.: Krebszellen manipulieren Neurone : Gliome (2023) 0.02 
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    Source
    Spektrum der Wissenschaft. 2023, H.10, S.22-24
  20. Schlagwein, D.: Consolidated, systemic conceptualization, and definition of the "sharing economy" (2020) 0.02 
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    Abstract
    The "sharing economy" has recently emerged as a major global phenomenon in practice and is consequently an important research topic. What, precisely, is meant by this term, "sharing economy"? The literature to date offers many, often incomplete and conflicting definitions. This makes it difficult for researchers to lead a coherent discourse, to compare findings and to select appropriate cases. Alternative terms (e.g., "collaborative consumption," "gig economy," and "access economy") are a further complication. To resolve this issue, our article develops a consolidated (based on all prior work) and systemic (relating to the phenomenon in its entire scope) definition of the sharing economy. The definition is based on the detailed analysis of definitions and explanations in 152 sources identified in a systematic literature review. We identify 36 original understandings of the term "sharing economy." Using semantic integration strategies, we consolidate 84 semantic facets in these definitions into 18 characteristics of the sharing economy. Resolving conflicts in the meaning and scope of these characteristics, we arrive at a consolidated, systemic definition. We evaluate the definition's appropriateness and applicability by applying it to cases claimed by the media to be examples of the sharing economy. This article's definition is useful for future research and discourse on the sharing economy.

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