A Storage System for Scalable Knowledge Representation
SRI INTERNATIONAL MENLO PARK CA ARTIFICIAL INTELLIGENCE CENTER
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Twenty years of AI research in knowledge representation has produced frame knowledge representation systems FRSs that incorporate a number of important advances. However, FRSs lack two important capabilities that prevent them from scaling up to realistic applications they cannot provide high-speed access to large knowledge bases KBs, and they do not support shared, concurrent KB access by multiple users. Our research investigates the hypothesis that one can employ an existing database management system DBMS as a storage subsystem for an FRS, to provide high-speed access to large, shared KBs. We describe the design and implementation of a general storage system that incrementally loads referenced frames from a DBMS, and saves modified frames back to the DBMS, for two different FRSs LOOM and THEO. We also present experimental results showing that the performance of our prototype storage subsystem exceeds that of flat files for simulated applications that reference or update up to one third of the frames from a large LOOM KB.
- Information Science
- Computer Programming and Software