Search (8 results, page 1 of 1)

  • × author_ss:"Liu, Y."
  1. Liu, Y.; Xu, S.; Blanchard, E.: ¬A local context-aware LDA model for topic modeling in a document network (2017) 0.01 
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    Abstract
    With the rapid development of the Internet and its applications, growing volumes of documents increasingly become interconnected to form large-scale document networks. Accordingly, topic modeling in a network of documents has been attracting continuous research attention. Most of the existing network-based topic models assume that topics in a document are influenced by its directly linked neighbouring documents in a document network and overlook the potential influence from indirectly linked ones. The existing work also has not carefully modeled variations of such influence among neighboring documents. Recognizing these modeling limitations, this paper introduces a novel Local Context-Aware LDA Model (LC-LDA), which is capable of observing a local context comprising a rich collection of documents that may directly or indirectly influence the topic distributions of a target document. The proposed model can also differentiate the respective influence of each document in the local context on the target document according to both structural and temporal relationships between the two documents. The proposed model is extensively evaluated through multiple document clustering and classification tasks conducted over several large-scale document sets. Evaluation results clearly and consistently demonstrate the effectiveness and superiority of the new model with respect to several state-of-the-art peer models.
  2. Sun, J.; Zhu, M.; Jiang, Y.; Liu, Y.; Wu, L.L.: Hierarchical attention model for personalized tag recommendation : peer effects on information value perception (2021) 0.01 
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    Abstract
    With the development of Web-based social networks, many personalized tag recommendation approaches based on multi-information have been proposed. Due to the differences in users' preferences, different users care about different kinds of information. In the meantime, different elements within each kind of information are differentially informative for user tagging behaviors. In this context, how to effectively integrate different elements and different information separately becomes a key part of tag recommendation. However, the existing methods ignore this key part. In order to address this problem, we propose a deep neural network for tag recommendation. Specifically, we model two important attentive aspects with a hierarchical attention model. For different user-item pairs, the bottom layered attention network models the influence of different elements on the features representation of the information while the top layered attention network models the attentive scores of different information. To verify the effectiveness of the proposed method, we conduct extensive experiments on two real-world data sets. The results show that using attention network and different kinds of information can significantly improve the performance of the recommendation model, and verify the effectiveness and superiority of our proposed model.
  3. Tang, X.-B.; Fu, W.-G.; Liu, Y.: Knowledge big graph fusing ontology with property graph : a case study of financial ownership network (2021) 0.01 
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    Abstract
    The scale of knowledge is growing rapidly in the big data environment, and traditional knowledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a knowledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the knowledge representation model is called knowledge big graph. In addition, this paper applies the knowledge big graph model to the ownership network in the China's financial field and builds a financial ownership knowledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership knowledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the knowledge big graph could provide technical reference for big data knowledge organization and services.
  4. Liu, Y.; Rafols, I.; Rousseau, R.: ¬A framework for knowledge integration and diffusion (2012) 0.01 
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    Abstract
    Purpose - This paper aims to introduce a general framework for the analysis of knowledge integration and diffusion using bibliometric data. Design/methodology/approach - The authors propose that in order to characterise knowledge integration and diffusion of a given issue (the source, for example articles on a topic or by an organisation, etc.), one has to choose a set of elements from the source (the intermediary set, for example references, keywords, etc.). This set can then be classified into categories (cats), thus making it possible to investigate its diversity. The set can also be characterised according to the coherence of a network associated to it. Findings - This framework allows a methodology to be developed to assess knowledge integration and diffusion. Such methodologies can be useful for a number of science policy issues, including the assessment of interdisciplinarity in research and dynamics of research networks. Originality/value - The main contribution of this article is to provide a simple and easy to use generalisation of an existing approach to study interdisciplinarity, bringing knowledge integration and knowledge diffusion together in one framework.
  5. Liu, Y.; Rousseau, R.: Citation analysis and the development of science : a case study using articles by some Nobel prize winners (2014) 0.01 
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    Date
    29. 1.2014 16:31:35
  6. Liu, Y.: Precision One MediaSource : film/video locator on CD-ROM (1995) 0.01 
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    Date
    22. 6.1997 16:34:51
  7. Liu, Y.; Qin, C.; Ma, X.; Liang, H.: Serendipity in human information behavior : a systematic review (2022) 0.00 
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    Date
    5. 6.2022 17:03:29
  8. Qin, C.; Liu, Y.; Mou, J.; Chen, J.: User adoption of a hybrid social tagging approach in an online knowledge community (2019) 0.00 
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    Date
    20. 1.2015 18:30:22

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