A Storage System for Scalable Knowledge Representation

Dr. Peter D. Karp
AI Center
SRI International

Frame knowledge representation systems (FRSs) can be viewed as a distant cousin of object-oriented databases. The semantic model presented by FRSs presents many interesting contrasts to typical C++-based object-oriented databases (OODBs): the object system is separate from the programming language, it permits run-time schema alteration, it computes subsumption relationships among class definitions, and it supports rule-based inference. The OODB community could learn much by studying this large family of systems (over 50 members).

Our group at SRI is addressing several limitations of FRSs by building on existing database systems. FRSs cannot support high-speed access to large knowledge bases, nor do they have multi-user access capabilities. We have designed a storage subsystem for FRSs by submerging an existing DBMS within an FRS. We report on empirical evaluations of several variants of this architecture. We compared the performance of different DBMSs (relational and object oriented). We also developed and evaluated several optimizations including transferring data at different granularities, and the use of prefetching. The resulting system is in use in our group to manage information for the human genome project.