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  • × author_ss:"Chen, Q."
  1. Min, C.; Chen, Q.; Yan, E.; Bu, Y.; Sun, J.: Citation cascade and the evolution of topic relevance (2021) 0.00 
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    Abstract
    Citation analysis, as a tool for quantitative studies of science, has long emphasized direct citation relations, leaving indirect or high-order citations overlooked. However, a series of early and recent studies demonstrate the existence of indirect and continuous citation impact across generations. Adding to the literature on high-order citations, we introduce the concept of a citation cascade: the constitution of a series of subsequent citing events initiated by a certain publication. We investigate this citation structure by analyzing more than 450,000 articles and over 6 million citation relations. We show that citation impact exists not only within the three generations documented in prior research but also in much further generations. Still, our experimental results indicate that two to four generations are generally adequate to trace a work's scientific impact. We also explore specific structural properties-such as depth, width, structural virality, and size-which account for differences among individual citation cascades. Finally, we find evidence that it is more important for a scientific work to inspire trans-domain (or indirectly related domain) works than to receive only intradomain recognition in order to achieve high impact. Our methods and findings can serve as a new tool for scientific evaluation and the modeling of scientific history.
    Type
    a
  2. Crouch, C.J.; Crouch, D.B.; Chen, Q.; Holtz, S.J.: Improving the retrieval effectiveness of very short queries (2002) 0.00 
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    Abstract
    This paper describes an automatic approach designed to improve the retrieval effectiveness of very short queries such as those used in web searching. The method is based on the observation that stemming, which is designed to maximize recall, often results in depressed precision. Our approach is based on pseudo-feedback and attempts to increase the number of relevant documents in the pseudo-relevant set by reranking those documents based on the presence of unstemmed query terms in the document text. The original experiments underlying this work were carried out using Smart 11.0 and the lnc.ltc weighting scheme on three sets of documents from the TREC collection with corresponding TREC (title only) topics as queries. (The average length of these queries after stoplisting ranges from 2.4 to 4.5 terms.) Results, evaluated in terms of P@20 and non-interpolated average precision, showed clearly that pseudo-feedback (PF) based on this approach was effective in increasing the number of relevant documents in the top ranks. Subsequent experiments, performed on the same data sets using Smart 13.0 and the improved Lnu.ltu weighting scheme, indicate that these results hold up even over the much higher baseline provided by the new weights. Query drift analysis presents a more detailed picture of the improvements produced by this process.
    Type
    a
  3. Ma, X.; Xue, P.; Matta, N.; Chen, Q.: Fine-grained ontology reconstruction for crisis knowledge based on integrated analysis of temporal-spatial factors (2021) 0.00 
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    Abstract
    Previous studies on crisis knowledge organization mostly focused on the categorization of crisis knowledge without regarding its dynamic trend and temporal-spatial features. In order to emphasize the dynamic factors of crisis collaboration, a fine-grained crisis knowledge model is proposed by integrating temporal-spatial analysis based on ontology, which is one of the commonly used methods for knowledge organization. The reconstruction of ontologybased crisis knowledge will be implemented through three steps: analyzing temporal-spatial features of crisis knowledge, reconstructing crisis knowledge ontology, and verifying the temporal-spatial ontology. In the process of ontology reconstruction, the main classes and properties of the domain will be identified by investigating the crisis information resources. Meanwhile the fine-grained crisis ontology will be achieved at the level of characteristic representation of crisis knowledge including temporal relationship, spatial relationship, and semantic relationship. Finally, we conducted case addition and system implementation to verify our crisis knowledge model. This ontology-based knowledge organization method theoretically optimizes the static organizational structure of crisis knowledge, improving the flexibility of knowledge organization and efficiency of emergency response. In practice, the proposed fine-grained ontology is supposed to be more in line with the real situation of emergency collaboration and management. Moreover, it will also provide the knowledge base for decision-making during rescue process.
    Type
    a

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