Knowledge Representation in the Large
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Knowledge Base Technology. Pages 751-757.
Abstract
Frame knowledge representation systems lack two important capabilities that prevent them from scaling up to large applications: they do not support fast access to large knowledge bases (KBs), nor do they provide concurrent multiuser access to shared KBs. We describe the design and implementation of a storage subsystem that submerges a database management system (DBMS) within a knowledge representation system. The storage subsystem incrementally loads referenced frames from the DBMS , and can save to the DBMS only those frames that have been updated in a given session. We present experimental results that show our approach to be an improvement over the use of flat files , and that evaluate several variations of our approach .
@inproceedings{ijcai1995p98,
title = {Knowledge Representation in the Large},
author = {Karp, Peter D. and Paley, Suzanne M.},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {751--757},
year = {1995},
note = {Knowledge Base Technology},
url = {https://ijcai.org/proceedings/1995/98},
}
TY - CPAPER TI - Knowledge Representation in the Large AU - Karp, Peter D. AU - Paley, Suzanne M. T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 751 EP - 757 UR - https://ijcai.org/proceedings/1995/98 ER -Copied
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