• DocumentCode
    1247281
  • Title

    Compiling conceptual graphs

  • Author

    Ellis, Gerard

  • Author_Institution
    Dept. of Comput. Sci., R. Melbourne Inst. of Technol., Vic., Australia
  • Volume
    7
  • Issue
    1
  • fYear
    1995
  • fDate
    2/1/1995 12:00:00 AM
  • Firstpage
    68
  • Lastpage
    81
  • Abstract
    The paper addresses problems in conceptual graph implementation: subsumption and classification in a taxonomy. Conceptual graphs are typically stored using a directed acyclic graph data structure based on the partial order over conceptual graphs. We give an improved algorithm for classifying conceptual graphs into this hierarchy. It prunes the search space in the database using the information gathered while searching. We show how conceptual graphs in this hierarchy can be compiled into instructions which represent specialized cases of the canonical formation rules. This compiles subsumption of conceptual graphs and compresses knowledge in a knowledge base. Conceptual graphs are compiled as differences between adjacent graphs in the hierarchy. The differences represent the rules used in deriving the graph from the adjacent graphs. We illustrate how the method compresses knowledge bases in some experiments. Compilation is effected in three ways: removal of redundant data, use of simple instructions which ignore redundant checks when performing matching, and by sharing common processing between graphs
  • Keywords
    directed graphs; knowledge based systems; program compilers; search problems; spatial data structures; adjacent graphs; associative retrieval; canonical formation rules; classification; common processing; conceptual graph compilation; conceptual graph implementation; directed acyclic graph data structure; hierarchical knowledge bases; knowledge base; partial order; redundant data; search space; simple instructions; subsumption; Computer science; Data mining; Data structures; Design methodology; Information retrieval; Knowledge based systems; Knowledge representation; Natural language processing; Natural languages; Taxonomy;
  • fLanguage
    English
  • Journal_Title
    Knowledge and Data Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1041-4347
  • Type

    jour

  • DOI
    10.1109/69.368517
  • Filename
    368517