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  • × author_ss:"Li, X."
  • × year_i:[2020 TO 2030}
  1. Zhu, L.; Xu, A.; Deng, S.; Heng, G.; Li, X.: Entity management using Wikidata for cultural heritage information (2024) 0.01 
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    Abstract
    Entity management in a Linked Open Data (LOD) environment is a process of associating a unique, persistent, and dereferenceable Uniform Resource Identifier (URI) with a single entity. It allows data from various sources to be reused and connected to the Web. It can help improve data quality and enable more efficient workflows. This article describes a semi-automated entity management project conducted by the "Wikidata: WikiProject Chinese Culture and Heritage Group," explores the challenges and opportunities in describing Chinese women poets and historical places in Wikidata, the largest crowdsourcing LOD platform in the world, and discusses lessons learned and future opportunities.
  2. Wang, P.; Li, X.: Assessing the quality of information on Wikipedia : a deep-learning approach (2020) 0.01 
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    Abstract
    Currently, web document repositories have been collaboratively created and edited. One of these repositories, Wikipedia, is facing an important problem: assessing the quality of Wikipedia. Existing approaches exploit techniques such as statistical models or machine leaning algorithms to assess Wikipedia article quality. However, existing models do not provide satisfactory results. Furthermore, these models fail to adopt a comprehensive feature framework. In this article, we conduct an extensive survey of previous studies and summarize a comprehensive feature framework, including text statistics, writing style, readability, article structure, network, and editing history. Selected state-of-the-art deep-learning models, including the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTMs) network, CNN-LSTMs, bidirectional LSTMs, and stacked LSTMs, are applied to assess the quality of Wikipedia. A detailed comparison of deep-learning models is conducted with regard to different aspects: classification performance and training performance. We include an importance analysis of different features and feature sets to determine which features or feature sets are most effective in distinguishing Wikipedia article quality. This extensive experiment validates the effectiveness of the proposed model.

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