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
Link To Document :
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