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Wu, Y.; Bai, R.: ¬An event relationship model for knowledge organization and visualization (2017)
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- Abstract
- An event is a specific occurrence involving participants, which is a typed, n-ary association of entities or other events, each identified as a participant in a specific semantic role in the event (Pyysalo et al. 2012; Linguistic Data Consortium 2005). Event types may vary across domains. Representing relationships between events can facilitate the understanding of knowledge in complex systems (such as economic systems, human body, social systems). In the simplest form, an event can be represented as Entity A <Relation> Entity B. This paper evaluates several knowledge organization and visualization models and tools, such as concept maps (Cmap), topic maps (Ontopia), network analysis models (Gephi), and ontology (Protégé), then proposes an event relationship model that aims to integrate the strengths of these models, and can represent complex knowledge expressed in events and their relationships.
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Wu, Y.; Liu, Y.; Tsai, Y.-H.R.; Yau, S.-T.: Investigating the role of eye movements and physiological signals in search satisfaction prediction using geometric analysis (2019)
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Liu, J.; Wu, Y.; Zhou, L.: ¬A hybrid method for abstracting newspaper articles (1999)
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Allen, R.B.; Wu, Y.: Metrics for the scope of a collection (2005)
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Wu, Y.: Indexing historical, political cartoons for retrieval (2013)
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Wu, Y.; Yang, L.: Construction and evaluation of an oil spill semantic relation taxonomy for supporting knowledge discovery (2015)
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Wu, Y.; Lehman, A.; Dunaway, D:J.: Evaluations of a large topic map as a knowledge organization tool for supporting self-regulated learning (2015)
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Li, J.; Zhang, P.; Song, D.; Wu, Y.: Understanding an enriched multidimensional user relevance model by analyzing query logs (2017)
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Xiao, C.; Zhou, F.; Wu, Y.: Predicting audience gender in online content-sharing social networks (2013)
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Du, J.; Tang, X.; Wu, Y.: ¬The effects of research level and article type on the differences between citation metrics and F1000 recommendations (2016)
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Yang, L.; Wu, Y.: Creating a taxonomy of earth-quake disaster response and recovery for online earthquake information management (2019)
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Kang, X.; Wu, Y.; Ren, W.: Toward action comprehension for searching : mining actionable intents in query entities (2020)
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Wang, F.; Yang, J.; Wu, Y.: Non-synchronism in theoretical research of information science (2021)
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Wu, Y.: Organization of complex topics in comprehensive classification schemes : case studies of disaster and security (2023)
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