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  • × year_i:[2010 TO 2020}
  1. Cronin, B.: Thinking about data (2013) 0.12
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    Date
    22. 3.2013 16:18:36
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.435-436
  2. Grudin, J.: Human-computer interaction (2011) 0.12
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    Date
    27.12.2014 18:54:22
    Source
    Annual review of information science and technology. 45(2011) no.1, S.367-430
  3. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.12
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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
  4. Farazi, M.: Faceted lightweight ontologies : a formalization and some experiments (2010) 0.11
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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
  5. Marx, W.; Bornmann, L.: On the problems of dealing with bibliometric data (2014) 0.10
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    Date
    18. 3.2014 19:13:22
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.4, S.866-867
  6. Paul-Hus, A.; Desrochers, N.; Rijcke, S.de; Rushforth, A.D.: ¬The reward system of science (2017) 0.10
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    Date
    20. 1.2015 18:30:22
    Footnote
    Guest editorial für ein Special issue on "The reward system of science".
    Source
    Aslib journal of information management. 69(2017) no.5, S.478-485
  7. Cronin, B.: ¬The writing on the wall (2015) 0.10
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    Date
    26. 4.2015 19:27:22
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.5, S.873-875
  8. Castro, A. de: Mental models may fail when faced with self-referential descriptors (2016) 0.10
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    Date
    22. 1.2016 14:45:01
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.2, S.490
  9. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.10
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    Content
    One of a series of three publications influenced by the travelling exhibit Places & Spaces: Mapping Science, curated by the Cyberinfrastructure for Network Science Center at Indiana University. - Additional materials can be found at http://http://scimaps.org/atlas2. Erweitert durch: Börner, Katy. Atlas of Science: Visualizing What We Know.
    Date
    22. 1.2017 16:54:03
    22. 1.2017 17:10:56
    LCSH
    Science / Atlases
    Science / Study and teaching / Graphic methods
    Communication in science / Data processing
    Subject
    Science / Atlases
    Science / Study and teaching / Graphic methods
    Communication in science / Data processing
  10. Hogan, N.M.; Sweeney, K.J.: Social networking and scientific communication : a paradoxical return to Mertonian roots? (2013) 0.10
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    Abstract
    Although modes of interaction between the two continue to evolve, society and science are inextricably linked. Preserving the integrity of science, and by extension society, in the era of Twitter and Facebook represents a significant challenge. The concept of open communication in science is not a new one. Sociologist and scientific historian Robert Merton elegantly chronicled the qualities, or "norms" of science as Communism, Universalism, Disinterestedness, and Organized Scepticism, referred to by the acronym "CUDOS." Is social networking providing us with more efficient ways of upholding deep-rooted principles, or are we at risk of compromising the integrity of science by bypassing traditional gatekeepers?
    Date
    22. 3.2013 19:53:52
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.644-646
  11. Burghardt, M.; Hertlein, F.; Hinterleitner, B.; Lehenmeier, C.; Spröd, T.: ¬A crowdsourced approach for the documentation and transcription of graffiti in public restrooms (2015) 0.10
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    Date
    6. 6.2016 18:42:22
    Source
    Re:inventing information science in the networked society: Proceedings of the 14th International Symposium on Information Science, Zadar/Croatia, 19th-21st May 2015. Eds.: F. Pehar, C. Schloegl u. C. Wolff
  12. Xiong, C.: Knowledge based text representations for information retrieval (2016) 0.09
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    Abstract
    The successes of information retrieval (IR) in recent decades were built upon bag-of-words representations. Effective as it is, bag-of-words is only a shallow text understanding; there is a limited amount of information for document ranking in the word space. This dissertation goes beyond words and builds knowledge based text representations, which embed the external and carefully curated information from knowledge bases, and provide richer and structured evidence for more advanced information retrieval systems. This thesis research first builds query representations with entities associated with the query. Entities' descriptions are used by query expansion techniques that enrich the query with explanation terms. Then we present a general framework that represents a query with entities that appear in the query, are retrieved by the query, or frequently show up in the top retrieved documents. A latent space model is developed to jointly learn the connections from query to entities and the ranking of documents, modeling the external evidence from knowledge bases and internal ranking features cooperatively. To further improve the quality of relevant entities, a defining factor of our query representations, we introduce learning to rank to entity search and retrieve better entities from knowledge bases. In the document representation part, this thesis research also moves one step forward with a bag-of-entities model, in which documents are represented by their automatic entity annotations, and the ranking is performed in the entity space.
    This proposal includes plans to improve the quality of relevant entities with a co-learning framework that learns from both entity labels and document labels. We also plan to develop a hybrid ranking system that combines word based and entity based representations together with their uncertainties considered. At last, we plan to enrich the text representations with connections between entities. We propose several ways to infer entity graph representations for texts, and to rank documents using their structure representations. This dissertation overcomes the limitation of word based representations with external and carefully curated information from knowledge bases. We believe this thesis research is a solid start towards the new generation of intelligent, semantic, and structured information retrieval.
    Content
    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.
    Imprint
    Pittsburgh, PA : Carnegie Mellon University, School of Computer Science, Language Technologies Institute
  13. Boerner, K.: Atlas of science : visualizing what we know (2010) 0.08
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    Abstract
    Cartographic maps have guided our explorations for centuries, allowing us to navigate the world. Science maps have the potential to guide our search for knowledge in the same way, helping us navigate, understand, and communicate the dynamic and changing structure of science and technology. Allowing us to visualize scientific results, science maps help us make sense of the avalanche of data generated by scientific research today. Atlas of Science, features more than thirty full-page science maps, fifty data charts, a timeline of science-mapping milestones, and 500 color images; it serves as a sumptuous visual index to the evolution of modern science and as an introduction to "the science of science"--charting the trajectory from scientific concept to published results. Atlas of Science, based on the popular exhibit "Places & Spaces: Mapping Science," describes and displays successful mapping techniques. The heart of the book is a visual feast: Claudius Ptolemy's Cosmographia World Map from 1482; a guide to a PhD thesis that resembles a subway map; "the structure of science" as revealed in a map of citation relationships in papers published in 2002; a periodic table; a history flow visualization of the Wikipedia article on abortion; a globe showing the worldwide distribution of patents; a forecast of earthquake risk; hands-on science maps for kids; and many more. Each entry includes the story behind the map and biographies of its makers. Not even the most brilliant minds can keep up with today's deluge of scientific results. Science maps show us the landscape of what we know. Exhibition Ongoing National Science Foundation, Washington, D.C. The Institute for Research Information and Quality Assurance, Bonn, Germany Storm Hall, San Diego State College
    Content
    Vgl. ergänzend: Börner, K.: Atlas of knowledge: anyone can map. Cambridge, MA: MIT Press 2015.
    Date
    22. 1.2017 17:12:16
    LCSH
    Classification of sciences
    Communication in science
    Science
    Subject
    Classification of sciences
    Communication in science
    Science
  14. Egghe, L.; Guns, R.; Rousseau, R.; Leuven, K.U.: Erratum (2012) 0.08
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    Date
    14. 2.2012 12:53:22
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.429
  15. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.08
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    Date
    1. 2.2016 18:25:22
    Series
    Lecture notes in computer science ; 9398
  16. Kozikowski, P. et al.: Support of part-whole relations in query answering (2016) 0.08
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    Date
    1. 2.2016 18:25:22
    Series
    Lecture notes in computer science ; 9398
  17. Platis, N. et al.: Visualization of uncertainty in tag clouds (2016) 0.08
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    Date
    1. 2.2016 18:25:22
    Series
    Lecture notes in computer science ; 9398
  18. Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016) 0.08
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    Date
    1. 2.2016 18:25:22
    Series
    Lecture notes in computer science ; 9398
  19. Kopácsi, S. et al.: Development of a classification server to support metadata harmonization in a long term preservation system (2016) 0.08
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    Series
    Communications in computer and information science; 672
    Source
    Metadata and semantics research: 10th International Conference, MTSR 2016, Göttingen, Germany, November 22-25, 2016, Proceedings. Eds.: E. Garoufallou
  20. Yan, E.: Finding knowledge paths among scientific disciplines (2014) 0.08
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    Abstract
    This paper uncovers patterns of knowledge dissemination among scientific disciplines. Although the transfer of knowledge is largely unobservable, citations from one discipline to another have been proven to be an effective proxy to study disciplinary knowledge flow. This study constructs a knowledge-flow network in which a node represents a Journal Citation Reports subject category and a link denotes the citations from one subject category to another. Using the concept of shortest path, several quantitative measurements are proposed and applied to a knowledge-flow network. Based on an examination of subject categories in Journal Citation Reports, this study indicates that social science domains tend to be more self-contained, so it is more difficult for knowledge from other domains to flow into them; at the same time, knowledge from science domains, such as biomedicine-, chemistry-, and physics-related domains, can access and be accessed by other domains more easily. This study also shows that social science domains are more disunified than science domains, because three fifths of the knowledge paths from one social science domain to another require at least one science domain to serve as an intermediate. This work contributes to discussions on disciplinarity and interdisciplinarity by providing empirical analysis.
    Date
    26.10.2014 20:22:22
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.11, S.2331-2347

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