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  • × author_ss:"Liu, X."
  • × language_ss:"e"
  • × year_i:[2010 TO 2020}
  1. Liu, X.; Zhang, J.; Guo, C.: Full-text citation analysis : a new method to enhance scholarly networks (2013) 0.02 
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    Abstract
    In this article, we use innovative full-text citation analysis along with supervised topic modeling and network-analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author-contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be "important publications" for the target topic (keyword), and we use these cited publications to validate our topic-ranking result and to compare different publication-ranking lists. Evaluation results show that full-text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency-inverted document frequency (tf-idf), language model, BM25, PageRank, and PageRank + language model (p < .001), for academic information retrieval (IR) systems.
    Date
    9. 9.2013 19:23:26
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.9, S.1852-1863
  2. Liu, X.; Qin, J.: ¬An interactive metadata model for structural, descriptive, and referential representation of scholarly output (2014) 0.01 
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    Abstract
    The scientific metadata model proposed in this article encompasses both classical descriptive metadata such as those defined in the Dublin Core Metadata Element Set (DC) and the innovative structural and referential metadata properties that go beyond the classical model. Structural metadata capture the structural vocabulary in research publications; referential metadata include not only citations but also data about other types of scholarly output that is based on or related to the same publication. The article describes the structural, descriptive, and referential (SDR) elements of the metadata model and explains the underlying assumptions and justifications for each major component in the model. ScholarWiki, an experimental system developed as a proof of concept, was built over the wiki platform to allow user interaction with the metadata and the editing, deleting, and adding of metadata. By allowing and encouraging scholars (both as authors and as users) to participate in the knowledge and metadata editing and enhancing process, the larger community will benefit from more accurate and effective information retrieval. The ScholarWiki system utilizes machine-learning techniques that can automatically produce self-enhanced metadata by learning from the structural metadata that scholars contribute, which will add intelligence to enhance and update automatically the publication of metadata Wiki pages.
  3. Liu, X.; Guo, C.; Zhang, L.: Scholar metadata and knowledge generation with human and artificial intelligence (2014) 0.00 
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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.
  4. Jiang, Z.; Liu, X.; Chen, Y.: Recovering uncaptured citations in a scholarly network : a two-step citation analysis to estimate publication importance (2016) 0.00 
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    Abstract
    The citation relationships between publications, which are significant for assessing the importance of scholarly components within a network, have been used for various scientific applications. Missing citation metadata in scholarly databases, however, create problems for classical citation-based ranking algorithms and challenge the performance of citation-based retrieval systems. In this research, we utilize a two-step citation analysis method to investigate the importance of publications for which citation information is partially missing. First, we calculate the importance of the author and then use his importance to estimate the publication importance for some selected articles. To evaluate this method, we designed a simulation experiment-"random citation-missing"-to test the two-step citation analysis that we carried out with the Association for Computing Machinery (ACM) Digital Library (DL). In this experiment, we simulated different scenarios in a large-scale scientific digital library, from high-quality citation data, to very poor quality data, The results show that a two-step citation analysis can effectively uncover the importance of publications in different situations. More importantly, we found that the optimized impact from the importance of an author (first step) is exponentially increased when the quality of citation decreases. The findings from this study can further enhance citation-based publication-ranking algorithms for real-world applications.
  5. 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) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119
  6. 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
  7. Liu, X.; Jia, H.: Answering academic questions for education by recommending cyberlearning resources (2013) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1707-1722
  8. Liu, X.; Turtle, H.: Real-time user interest modeling for real-time ranking (2013) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1557-1576
  9. Zhang, C.; Liu, X.; Xu, Y.(C.); Wang, Y.: Quality-structure index : a new metric to measure scientific journal influence (2011) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.643-653
  10. 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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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.6, S.1166-1176

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