Search (8 results, page 1 of 1)

  • × author_ss:"Liu, Y."
  1. Liu, Y.; Rousseau, R.: Towards a representation of diffusion and interaction of scientific ideas : the case of fiber optics communication (2012) 0.00 
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    Abstract
    The research question studied in this contribution is how to find an adequate representation to describe the diffusion of scientific ideas over time. We claim that citation data, at least of articles that act as concept symbols, can be considered to contain this information. As a case study we show how the founding article by Nobel Prize winner Kao illustrates the evolution of the field of fiber optics communication. We use a continuous description of discrete citation data in order to accentuate turning points and breakthroughs in the history of this field. Applying the principles explained in this contribution informetrics may reveal the trajectories along which science is developing.
  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) 0.00 
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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) 0.00 
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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. Lim, S.C.J.; Liu, Y.; Lee, W.B.: ¬A methodology for building a semantically annotated multi-faceted ontology for product family modelling (2011) 0.00 
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    Abstract
    Product family design is one of the prevailing approaches in realizing mass customization. With the increasing number of product offerings targeted at different market segments, the issue of information management in product family design, that is related to an efficient and effective storage, sharing and timely retrieval of design information, has become more complicated and challenging. Product family modelling schema reported in the literature generally stress the component aspects of a product family and its analysis, with a limited capability to model complex inter-relations between physical components and other required information in different semantic orientations, such as manufacturing, material and marketing wise. To tackle this problem, ontology-based representation has been identified as a promising solution to redesign product platforms especially in a semantically rich environment. However, ontology development in design engineering demands a great deal of time commitment and human effort to process complex information. When a large variety of products are available, particularly in the consumer market, a more efficient method for building a product family ontology with the incorporation of multi-faceted semantic information is therefore highly desirable. In this study, we propose a methodology for building a semantically annotated multi-faceted ontology for product family modelling that is able to automatically suggest semantically-related annotations based on the design and manufacturing repository. The six steps of building such ontology: formation of product family taxonomy; extraction of entities; faceted unit generation and concept identification; facet modelling and semantic annotation; formation of a semantically annotated multi-faceted product family ontology (MFPFO); and ontology validation and evaluation are discussed in detail. Using a family of laptop computers as an illustrative example, we demonstrate how our methodology can be deployed step by step to create a semantically annotated MFPFO. Finally, we briefly discuss future research issues as well as interesting applications that can be further pursued based on the MFPFO developed.
  5. Liu, Y.; Rousseau, R.: Citation analysis and the development of science : a case study using articles by some Nobel prize winners (2014) 0.00 
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    Date
    29. 1.2014 16:31:35
  6. Liu, Y.: Precision One MediaSource : film/video locator on CD-ROM (1995) 0.00 
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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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