Search (23 results, page 1 of 2)

  • × author_ss:"Liu, X."
  1. Chen, M.; Liu, X.; Qin, J.: Semantic relation extraction from socially-generated tags : a methodology for metadata generation (2008) 0.05
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    Abstract
    The growing predominance of social semantics in the form of tagging presents the metadata community with both opportunities and challenges as for leveraging this new form of information content representation and for retrieval. One key challenge is the absence of contextual information associated with these tags. This paper presents an experiment working with Flickr tags as an example of utilizing social semantics sources for enriching subject metadata. The procedure included four steps: 1) Collecting a sample of Flickr tags, 2) Calculating cooccurrences between tags through mutual information, 3) Tracing contextual information of tag pairs via Google search results, 4) Applying natural language processing and machine learning techniques to extract semantic relations between tags. The experiment helped us to build a context sentence collection from the Google search results, which was then processed by natural language processing and machine learning algorithms. This new approach achieved a reasonably good rate of accuracy in assigning semantic relations to tag pairs. This paper also explores the implications of this approach for using social semantics to enrich subject metadata.
    Date
    20. 2.2009 10:29:07
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
  2. Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.04
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    Abstract
    We propose a new hybrid clustering framework to incorporate text mining with bibliometrics in journal set analysis. The framework integrates two different approaches: clustering ensemble and kernel-fusion clustering. To improve the flexibility and the efficiency of processing large-scale data, we propose an information-based weighting scheme to leverage the effect of multiple data sources in hybrid clustering. Three different algorithms are extended by the proposed weighting scheme and they are employed on a large journal set retrieved from the Web of Science (WoS) database. The clustering performance of the proposed algorithms is systematically evaluated using multiple evaluation methods, and they were cross-compared with alternative methods. Experimental results demonstrate that the proposed weighted hybrid clustering strategy is superior to other methods in clustering performance and efficiency. The proposed approach also provides a more refined structural mapping of journal sets, which is useful for monitoring and detecting new trends in different scientific fields.
    Date
    1. 6.2010 9:29:57
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119
  3. Liu, X.; Guo, C.; Zhang, L.: Scholar metadata and knowledge generation with human and artificial intelligence (2014) 0.03
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    Abstract
    Scholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata (structural keywords) using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars (both as authors and users) can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.6, S.1187-1201
  4. Zhang, X.; Fang, Y.; He, W.; Zhang, Y.; Liu, X.: Epistemic motivation, task reflexivity, and knowledge contribution behavior on team wikis : a cross-level moderation model (2019) 0.03
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    Abstract
    A cross-level model based on the information processing perspective and trait activation theory was developed and tested in order to investigate the effects of individual-level epistemic motivation and team-level task reflexivity on three different individual contribution behaviors (i.e., adding, deleting, and revising) in the process of knowledge creation on team wikis. Using the Hierarchical Linear Modeling software package and the 2-wave data from 166 individuals in 51 wiki-based teams, we found cross-level interaction effects between individual epistemic motivation and team task reflexivity on different knowledge contribution behaviors on wikis. Epistemic motivation exerted a positive effect on adding, which was strengthened by team task reflexivity. The effect of epistemic motivation on deleting was positive only when task reflexivity was high. In addition, epistemic motivation was strongly positively related to revising, regardless of the level of task reflexivity involved.
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.5, S.448-461
  5. Liu, X.; Zheng, W.; Fang, H.: ¬An exploration of ranking models and feedback method for related entity finding (2013) 0.01
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    Source
    Information processing and management. 49(2013) no.5, S.995-1007
  6. Liu, X.: ¬The standardization of Chinese library classification (1993) 0.01
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    Abstract
    The standardization of Chinese materials classification was first proposed in the late-1970s in China. In December 1980, the CCDST, the Chinese Library Association and the Chinese Society for Information Science proposed that the Chinese Library Classification system be adopted as national standard. This marked the beginning of the standardization of Chinese materials classification. Later on, there were many conferences and workshops held and four draft national standards were discussed, those for the Chinese Library Classification systems, the Materials Classification System, the Rules for Thesaurus and Subject Headings, and the rules for Materials Classifying Color Recognition. This article gives a brief review on the historical development of the standardization on Chinese Library Classification. It also discusses its effects on automation, networking and resources sharing and the feasibility of adopting Chinese Library Classification as a National Standard. In addition, the main content of the standardization of materials classification, use of the national standard classification system and variations under the standard system are covered in this article
    Date
    8.10.2000 14:29:26
  7. Jiang, Z.; Liu, X.; Chen, Y.: Recovering uncaptured citations in a scholarly network : a two-step citation analysis to estimate publication importance (2016) 0.01
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    Date
    12. 6.2016 20:31:29
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1722-1735
  8. Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005) 0.00
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    Source
    Journal of information science. 30(2005) no.6, S.550-
  9. Liu, X.; Bu, Y.; Li, M.; Li, J.: Monodisciplinary collaboration disrupts science more than multidisciplinary collaboration (2024) 0.00
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    Abstract
    Collaboration across disciplines is a critical form of scientific collaboration to solve complex problems and make innovative contributions. This study focuses on the association between multidisciplinary collaboration measured by coauthorship in publications and the disruption of publications measured by the Disruption (D) index. We used authors' affiliations as a proxy of the disciplines to which they belong and categorized an article into multidisciplinary collaboration or monodisciplinary collaboration. The D index quantifies the extent to which a study disrupts its predecessors. We selected 13 journals that publish articles in six disciplines from the Microsoft Academic Graph (MAG) database and then constructed regression models with fixed effects and estimated the relationship between the variables. The findings show that articles with monodisciplinary collaboration are more disruptive than those with multidisciplinary collaboration. Furthermore, we uncovered the mechanism of how monodisciplinary collaboration disrupts science more than multidisciplinary collaboration by exploring the references of the sampled publications.
    Source
    Journal of the Association for Information Science and Technology. 75(2023) no.1, S.59-78
  10. Clewley, N.; Chen, S.Y.; Liu, X.: Cognitive styles and search engine preferences : field dependence/independence vs holism/serialism (2010) 0.00
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    Date
    29. 8.2010 13:11:47
  11. Liu, X.; Chen, X.: Authors' noninstitutional emails and their correlation with retraction (2021) 0.00
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.4, S.449-4473-477
  12. Liu, X.; Kaza, S.; Zhang, P.; Chen, H.: Determining inventor status and its effect on knowledge diffusion : a study on nanotechnology literature from China, Russia, and India (2011) 0.00
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    Abstract
    In an increasingly global research landscape, it is important to identify the most prolific researchers in various institutions and their influence on the diffusion of knowledge. Knowledge diffusion within institutions is influenced by not just the status of individual researchers but also the collaborative culture that determines status. There are various methods to measure individual status, but few studies have compared them or explored the possible effects of different cultures on the status measures. In this article, we examine knowledge diffusion within science and technology-oriented research organizations. Using social network analysis metrics to measure individual status in large-scale coauthorship networks, we studied an individual's impact on the recombination of knowledge to produce innovation in nanotechnology. Data from the most productive and high-impact institutions in China (Chinese Academy of Sciences), Russia (Russian Academy of Sciences), and India (Indian Institutes of Technology) were used. We found that boundary-spanning individuals influenced knowledge diffusion in all countries. However, our results also indicate that cultural and institutional differences may influence knowledge diffusion.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.6, S.1166-1176
  13. Frias-Martinez, E.; Chen, S.Y.; Liu, X.: Automatic cognitive style identification of digital library users for personalization (2007) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.2, S.237-251
  14. Liu, X.: Generating metadata for cyberlearning resources through information retrieval and meta-search (2013) 0.00
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.771-786
  15. Liu, X.; Jia, H.: Answering academic questions for education by recommending cyberlearning resources (2013) 0.00
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