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Teo, H.-H.; Wang, X.; Wei, K.-K.; Sia, C.-L.; Lee, M.K.O.: Organizational learning capacity and attitude toward complex technological innovations : an empirical study (2006)
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Jiang, Y.; Zheng, H.-T.; Wang, X.; Lu, B.; Wu, K.: Affiliation disambiguation for constructing semantic digital libraries (2011)
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- Abstract
- With increasing digital information availability, semantic web technologies have been employed to construct semantic digital libraries in order to ease information comprehension. The use of semantic web enables users to search or visualize resources in a semantic fashion. Semantic web generation is a key process in semantic digital library construction, which converts metadata of digital resources into semantic web data. Many text mining technologies, such as keyword extraction and clustering, have been proposed to generate semantic web data. However, one important type of metadata in publications, called affiliation, is hard to convert into semantic web data precisely because different authors, who have the same affiliation, often express the affiliation in different ways. To address this issue, this paper proposes a clustering method based on normalized compression distance for the purpose of affiliation disambiguation. The experimental results show that our method is able to identify different affiliations that denote the same institutes. The clustering results outperform the well-known k-means clustering method in terms of average precision, F-measure, entropy, and purity.
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Walsh, J.A.; Cobb, P.J.; Fremery, W. de; Golub, K.; Keah, H.; Kim, J.; Kiplang'at, J.; Liu, Y.-H.; Mahony, S.; Oh, S.G.; Sula, C.A.; Underwood, T.; Wang, X.: Digital humanities in the iSchool (2022)
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Wang, X.; High, A.; Wang, X.; Zhao, K.: Predicting users' continued engagement in online health communities from the quantity and quality of received support (2021)
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Wang, X.; Zhang, M.; Fan, W.; Zhao, K.: Understanding the spread of COVID-19 misinformation on social media : the effects of topics and a political leader's nudge (2022)
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Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014)
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- Date
- 22. 8.2014 16:52:04
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Wang, X.; Song, N.; Zhou, H.; Cheng, H.: Argument ontology for describing scientific articles : a statistical study based on articles from two research areas (2019)
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Song, N.; Cheng, H.; Zhou, H.; Wang, X.: Linking scholarly contents : the design and construction of an argumentation graph (2022)
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Wang, X.; Song, N.; Zhou, H.; Cheng, H.: ¬The representation of argumentation in scientific papers : a comparative analysis of two research areas (2022)
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Wang, X.; Hong, Z.; Xu, Y.(C.); Zhang, C.; Ling, H.: Relevance judgments of mobile commercial information (2014)
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Tan, X.; Luo, X.; Wang, X.; Wang, H.; Hou, X.: Representation and display of digital images of cultural heritage : a semantic enrichment approach (2021)
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Wang, X.; Erdelez, S.; Allen, C.; Anderson, B.; Cao, H.; Shyu, C.-R.: Role of domain knowledge in developing user-centered medical-image indexing (2012)
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Cui, Y.; Wang, Y.; Liu, X.; Wang, X.; Zhang, X.: Multidimensional scholarly citations : characterizing and understanding scholars' citation behaviors (2023)
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- Abstract
- This study investigates scholars' citation behaviors from a fine-grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision-making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest-based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision-making, followed by citation authority and citation inertia. However, big-name scholars whose h-indexes rank among the top 1% exhibit a unique pattern of citation behaviors-their citation decision-making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.
