The effect of data structures modifications on algorithms for reasoning operations using a conceptual graphs knowledge base
Roger T. Hartley, Heather D. Pfeiffer · 2007
Knowledge representation (KR) is used to store and retrieve meaningful information. Meaning cannot be directly stored in the computer; therefore, a series of levels of representation transforms knowledge to a format that a computer can process. This transformed knowledge is saved using dynamic data structures that are suitable for the style of KR being implemented, and through the KR the system manipulates the knowledge in the data using reasoning operations. The data structure, together with the contents of the transformed knowledge, is called the knowledge base (KB). An algorithm and the associated data structures make up the reasoning operation, and the performance of this operation is dependent on the KB it uses. In this work, the basic reasoning operations for knowledge management will be explored using a particular style of KR called Conceptual Graphs (CGs). These operations, projection and maximal join, are the foundation for query/answer and hypothesis generation (abduction) systems, respectively. It is believed that changing the reasoning operation's algorithm and providing adequate data structures for them can improve the implementation of the operation for use in intelligent systems; therefore, making them faster and more efficient. Different algorithms and data structures execution times are analyzed over the most general form of CGs knowledge base showing that flexible, fast and efficient operations can improve higher level systems.