• DocumentCode
    463386
  • Title

    SenseNet: A Knowledge Representation Model for Computational Semantics

  • Author

    Chen, Ping ; Ding, Wei ; Ding, Chengmin

  • Author_Institution
    Dept. of Comput. & Math. Sci., Univ. of Houston-Downtown, Houston, TX
  • Volume
    1
  • fYear
    2006
  • fDate
    17-19 July 2006
  • Firstpage
    434
  • Lastpage
    439
  • Abstract
    Knowledge representation is essential for semantics modeling and intelligent information processing. For decades researchers have proposed many knowledge representation techniques. However, it is a daunting problem how to capture deep semantic information effectively and support the construction of a large-scale knowledge base efficiently. This paper describes a new knowledge representation model, SenseNet, which provides semantic support for commonsense reasoning and natural language processing. SenseNet is formalized with a Hidden Markov Model. An inference algorithm is proposed to simulate human-like text analysis procedure. A new measurement, confidence, is introduced to facilitate the text analysis. We present a detailed case study of applying SenseNet to retrieving compensation information from company proxy filings
  • Keywords
    inference mechanisms; knowledge representation; natural language processing; text analysis; SenseNet; commonsense reasoning; computational semantics; hidden Markov model; intelligent information processing; knowledge representation model; natural language processing; semantics modeling; text analysis; Analytical models; Computational intelligence; Computational modeling; Hidden Markov models; Inference algorithms; Information processing; Knowledge representation; Large-scale systems; Natural language processing; Text analysis; Computational Semantics; Hidden Markov Model; Information Retrieval; Knowledge Representation; Natural Language Processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    1-4244-0475-4
  • Type

    conf

  • DOI
    10.1109/COGINF.2006.365528
  • Filename
    4216445