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  • × author_ss:"Wu, Y."
  1. Wu, Y.; Lehman, A.; Dunaway, D:J.: Evaluations of a large topic map as a knowledge organization tool for supporting self-regulated learning (2015) 0.05 
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    Abstract
    A large topic map was created to facilitate understanding of the impacts of the 2010 Gulf of Mexico Oil Spill Incident. The topic map has both a text and graphical interface, which complement each other. A formative evaluation and two summative evaluations were conducted, as qualitative studies, to assess the usefulness and usability of the large topic maps for facilitating self-regulated learning. The topic maps were found useful for knowledge fusion and discovery, and can be useful when undertaking interdisciplinary and multidisciplinary research. Users reported some usability issues about the graphical topic map, including information overload and cluttered display of topics when displaying large number of topics and their associated topics. The text topic map was found easier to use due to displaying topics, relationships and references in a linear view.
    Object
    Topic maps
  2. Wu, Y.; Bai, R.: ¬An event relationship model for knowledge organization and visualization (2017) 0.04 
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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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