• DocumentCode
    3564645
  • Title

    Influence Discovery in Semantic Networks: An Initial Approach

  • Author

    Trovati, Marcello ; Bagdasar, Ovidiu

  • Author_Institution
    Sch. of Comput. & Math., Univ. of Derby, Derby, UK
  • fYear
    2014
  • Firstpage
    154
  • Lastpage
    158
  • Abstract
    Assessing the influence between concepts, which include people, physical objects, as well as theoretical ideas, plays a crucial role in understanding and discovering knowledge. Despite the huge amount of literature on knowledge discovery in semantic networks, there has been little attempt to fully classify and investigate the influence, which also includes causality, of a semantic entity on another one as dynamical entities. In this paper we will introduce an approach to discover and assess influence among nodes in a semantic network, with the aim to provide a tool to identify its type and direction. Even though this is still being developed, the preliminary evaluation shows promising and interesting results.
  • Keywords
    causality; data mining; semantic networks; causality; influence discovery; knowledge discovery; knowledge understanding; semantic entity; semantic networks; Computational modeling; Educational institutions; Equations; Mathematical model; Semantics; Social network services; Algorithms; Knowledge Discovery; Knowledge Representations; Knowledge acquisition; Semantics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Modelling and Simulation (UKSim), 2014 UKSim-AMSS 16th International Conference on
  • Print_ISBN
    978-1-4799-4923-6
  • Type

    conf

  • DOI
    10.1109/UKSim.2014.48
  • Filename
    7046055