Search (1150 results, page 3 of 58)

  • × year_i:[2010 TO 2020}
  1. Thelwall, M.; Sud, P.; Wilkinson, D.: Link and co-inlink network diagrams with URL citations or title mentions (2012) 0.03 
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    Date
    6. 4.2012 18:16:22
  2. Baloh, P.; Desouza, K.C.; Hackney, R.: Contextualizing organizational interventions of knowledge management systems : a design science perspectiveA domain analysis (2012) 0.03 
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    Date
    11. 6.2012 14:22:34
  3. Keilty, P.: Sexual boundaries and subcultural discipline (2012) 0.03 
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    Date
    22. 2.2013 11:24:44
  4. Heneberg, P.: Supposedly uncited articles of Nobel laureates and Fields medalists can be prevalently attributed to the errors of omission and commission (2013) 0.03 
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    Date
    22. 3.2013 19:21:46
  5. Zhang, P.; Soergel, D.: Towards a comprehensive model of the cognitive process and mechanisms of individual sensemaking (2014) 0.03 
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    Date
    22. 8.2014 16:55:39
  6. Khoo, C.S.G.; Teng, T.B.-R.; Ng, H.-C.; Wong, K.-P.: Developing a taxonomy to support user browsing and learning in a digital heritage portal with crowd-sourced content (2014) 0.03 
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    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  7. Costas, R.; Zahedi, Z.; Wouters, P.: ¬The thematic orientation of publications mentioned on social media : large-scale disciplinary comparison of social media metrics with citations (2015) 0.03 
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    Date
    20. 1.2015 18:30:22
  8. Bhattacharya, S.; Yang, C.; Srinivasan, P.; Boynton, B.: Perceptions of presidential candidates' personalities in twitter (2016) 0.03 
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    Date
    22. 1.2016 11:25:47
  9. Dalip, D.H.; Gonçalves, M.A.; Cristo, M.; Calado, P.: ¬A general multiview framework for assessing the quality of collaboratively created content on web 2.0 (2017) 0.03 
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    Date
    16.11.2017 13:04:22
  10. Monireh, E.; Sarker, M.K.; Bianchi, F.; Hitzler, P.; Doran, D.; Xie, N.: Reasoning over RDF knowledge bases using deep learning (2018) 0.03 
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    Date
    16.11.2018 14:22:01
  11. Wu, I.-C.; Vakkari, P.: Effects of subject-oriented visualization tools on search by novices and intermediates (2018) 0.03 
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    Date
    9.12.2018 16:22:25
  12. Lu, W.; Ding, H.; Jiang, J.: ¬A document expansion framework for tag-based image retrieval (2018) 0.03 
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    Abstract
    Purpose The purpose of this paper is to utilize document expansion techniques for improving image representation and retrieval. This paper proposes a concise framework for tag-based image retrieval (TBIR). Design/methodology/approach The proposed approach includes three core components: a strategy of selecting expansion (similar) images from the whole corpus (e.g. cluster-based or nearest neighbor-based); a technique for assessing image similarity, which is adopted for selecting expansion images (text, image, or mixed); and a model for matching the expanded image representation with the search query (merging or separate). Findings The results show that applying the proposed method yields significant improvements in effectiveness, and the method obtains better performance on the top of the rank and makes a great improvement on some topics with zero score in baseline. Moreover, nearest neighbor-based expansion strategy outperforms the cluster-based expansion strategy, and using image features for selecting expansion images is better than using text features in most cases, and the separate method for calculating the augmented probability P(q|RD) is able to erase the negative influences of error images in RD. Research limitations/implications Despite these methods only outperform on the top of the rank instead of the entire rank list, TBIR on mobile platforms still can benefit from this approach. Originality/value Unlike former studies addressing the sparsity, vocabulary mismatch, and tag relatedness in TBIR individually, the approach proposed by this paper addresses all these issues with a single document expansion framework. It is a comprehensive investigation of document expansion techniques in TBIR.
    Date
    20. 1.2015 18:30:22
  13. Olivares-Rodríguez, C.; Guenaga, M.; Garaizar, P.: Using children's search patterns to predict the quality of their creative problem solving (2018) 0.03 
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    Date
    20. 1.2015 18:30:22
  14. Rajagopal, P.; Ravana, S.D.; Koh, Y.S.; Balakrishnan, V.: Evaluating the effectiveness of information retrieval systems using effort-based relevance judgment (2019) 0.03 
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    Date
    20. 1.2015 18:30:22
  15. Hensinger, P.: Trojanisches Pferd "Digitale Bildung" : Auf dem Weg zur Konditionierungsanstalt in einer Schule ohne Lehrer? (2017) 0.03 
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    Content
    Vgl. auch: Häring, N. u. Hensinger, P.: "Digitale Bildung": Der abschüssige Weg zur Konditionierungsanstalt. Unter: http://norberthaering.de/de/27-german/news/1100-digitale-bildung [27.01.2019].
    Date
    22. 2.2019 11:45:19
  16. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.03 
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    Source
    http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja
  17. Levin, M.; Krawczyk, S.; Bethard, S.; Jurafsky, D.: Citation-based bootstrapping for large-scale author disambiguation (2012) 0.02 
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    Abstract
    We present a new, two-stage, self-supervised algorithm for author disambiguation in large bibliographic databases. In the first "bootstrap" stage, a collection of high-precision features is used to bootstrap a training set with positive and negative examples of coreferring authors. A supervised feature-based classifier is then trained on the bootstrap clusters and used to cluster the authors in a larger unlabeled dataset. Our self-supervised approach shares the advantages of unsupervised approaches (no need for expensive hand labels) as well as supervised approaches (a rich set of features that can be discriminatively trained). The algorithm disambiguates 54,000,000 author instances in Thomson Reuters' Web of Knowledge with B3 F1 of.807. We analyze parameters and features, particularly those from citation networks, which have not been deeply investigated in author disambiguation. The most important citation feature is self-citation, which can be approximated without expensive extraction of the full network. For the supervised stage, the minor improvement due to other citation features (increasing F1 from.748 to.767) suggests they may not be worth the trouble of extracting from databases that don't already have them. A lean feature set without expensive abstract and title features performs 130 times faster with about equal F1.
  18. Euzenat, J.; Shvaiko, P.: Ontology matching (2010) 0.02 
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    Date
    20. 6.2012 19:08:22
  19. Willis, C.; Greenberg, J.; White, H.: Analysis and synthesis of metadata goals for scientific data (2012) 0.02 
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    Abstract
    The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg's () metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (>0.6), a Fisher's exact test for nonparametric data was used to determine significance (p < .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have "scheme harmonization" (compatibility and interoperability with related schemes) as an objective; schemes with the objective "abstraction" (a conceptual model exists separate from the technical implementation) also have the objective "sufficiency" (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective "data publication" do not have the objective "element refinement." The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes.
  20. Ceri, S.; Bozzon, A.; Brambilla, M.; Della Valle, E.; Fraternali, P.; Quarteroni, S.: Web Information Retrieval (2013) 0.02 
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    Date
    16.10.2013 19:22:44

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