The role of frame-based representation in reasoning
Communications of the ACM
Object-oriented concepts, databases, and applications
Object-oriented concepts, databases, and applications
Storage management for objects in EXODUS
Object-oriented concepts, databases, and applications
A Persistent Store for Large Shared Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Using a Description Classifier to Enhance Knowledge Representation
IEEE Expert: Intelligent Systems and Their Applications
A Collaborative Environment for Authoring Large Knowledge Bases
Journal of Intelligent Information Systems
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Knowledge representation in the large
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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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.