Gary G. Hendrix
Abstract
Aspects of an intelligent interface that provides natural language access to a large body of data distributed over a computer network are described. The overall system architecture is presented, showing how a user is buffered from the actual database management systems (DBMSs) by three layers of insulating components. These layers operate in series to convert natural language queries into calls to DBMSs at remote sites. Attention is then focused on the first of the insulating components, the natural language system. A pragmatic approach to language access that has proved useful for building interfaces to databases is described and illustrated by examples. Special language features that increase system usability, such as spelling correction, processing of incomplete inputs, and run-time system personalization, are also discussed. The language system is contrasted with other work in applied natural language processing, and the system's limitations are analyzed.
- 1 BROWN, J.S., AND BURTON, R.R. Multiple representations of knowledge for tutorial reasoning. In Representation and Understanding, D.G. Bob~ow and A. Collins, Eds., Academic Press, New York, 1975, pp. 311-349.Google Scholar
- 2 BURTON, R.R. "Semantic grammar: An engineering technique for constructing natural language understanding systems. BBN Rep. 3453, Bolt, Beranek, and Newman, Boston, Mass., Dec. 1976.Google Scholar
- 3 CODD, E.F. Seven steps to rendezvous with the casual user. In Data Base Management, J.W. Klimbie and K.I. Koffeman, Eds., North-Holland, Amsterdam, 1974, pp. 179-200.Google Scholar
- 4 Computer Corporation of America. Datacomputer version 1 user manual. CCA, Cambridge, Mass., Aug. 1975.Google Scholar
- 5 ERMAN, L.D., ED. ACM SIGART Newsletter 61, Feb. 1977.Google Scholar
- 6 FARR~.LL, J. The Dataeomputer--A network data utility. Proc. Berkeley Workshop on Distributed Data Management and Computer Networks, Berkeley, Calif., May 1976 pp. 352-364.Google Scholar
- 7 Gttosz, B.J. The representation and use of focus in dialog understanding. Ph.D. dissertation, U. of California, Berkeley, May 1977.Google Scholar
- 8 HXRRIS, L.R. ROBOT :A high performance natural language processor for data base query. ACM SIGART Newsletter 61, Feb. 1977, pp. 39-40. Google ScholarDigital Library
- 9 HARRIS, L.R. User oriented data base query with the ROBOT natural language query system. Proc. 3rd Int. Conf. on Very Large Data Bases, Tokyo, Japan, Oct. 6--8, 1977.Google ScholarCross Ref
- 10 HENDRIX, G.G. The LIFER manual: A guide to building practical natural language interfaces. Tech. Note 138, SRI Artificial Intelligence Center, Menlo Park, Calif., Feb. 1977.Google Scholar
- 11 HENDRIX, G.G. Human engineering for applied natural language processing. Proc. 5th int. Joint Conf. on Artificial Intelligence, Cambridge, Mass., Aug. 1977.Google Scholar
- 12 HOPCROFT, J.E., AND ULLMAN, J.D. Formal Languages and Their Relation to Automata. Addison-Wesley, Reading, Mass., 1969. Google ScholarDigital Library
- 13 MORRIS, P., AND SAGALOWlCZ, D. Managing network access to a distributed data base. Proc. 2nd Berkeley Workshop on Distributed Data Management and Computer Networks, Berkeley, Calif., May 1977.Google Scholar
- 14 MYLOPOULOS, J., BORGIDA, A., COHEN, P., ROLISSOPOULOS, N., TSOTSOS, J., AND WOI~G, H. TORUS--A natural language understanding system for data management. Proc. 4th Int. Joint Conf. on Artificial Intelligence, Tbilisi, U.S.S.R., Aug. 1975.Google Scholar
- 15 PAXTON, W.H. A framework for speech understanding. Tech. Note 142, SRI Artificial Intelligence Center, Menlo Park, Calif., June 1977.Google Scholar
- 16 SACERDOTI, E.D. Language access to distributed data with error recovery. Proc. 5th Int. Joint Conf. on Artificial Intelligence, Cambridge, Mass, Aug. 1977.Google Scholar
- 17 SAGALOWlCZ, D. IDA: An intelligent data access program. Proc. 3rd Int. Conf. on Very Large Data Bases, Tokyo, Japan, Oct. 1977.Google Scholar
- 18 SowA, J.F. Conceptual graphs for a database interface. IBM J. Res. Develop. 20, 4 (July 1976), 336-357.Google Scholar
- 19 TEITELMAN, W. INTERLISP reference manual. Xerox PARC, Palo Alto, Calif., Dec. 1975.Google Scholar
- 20 THOMPSON, F.B., AND THOMPSON, B.H. Practical natural language processing: The REL system as prototype. In Advances in Computers 18, M. Rubinoff and M.C. Yovits, Eds., Academic Press, New York, 1975.Google Scholar
- 21 WALKER, D.E., GI~osz, B.J., HENDRIX, G.G., PAXTON, W.H., ROBINSON, A.E., AND SLO- CU~, J. An overview of speech understanding research at SRI. Proc. 5th int. Joint Conf. on Artificial Intelligence, Cambridge, Mass., Aug. 1977.Google Scholar
- 22 WALTZ, D. Natural language access to a large data base:An engineering approach. Proc. 4th Int. Joint Conf. on Artificial intelligence, Tbilisi, U.S.S.R., Sept. 1975, pp. 868-872.Google Scholar
- 23 WooDs, W.A. Transition network grammars for natural language analysis. Comm. A CM 18, 10 (Oct. 1970), 591--606. Google ScholarDigital Library
- 24 WooDs, W.A. An experimental parsing system for transition network grammars. In Natural Language Processing, R. Rustin, Ed., Algorithmics Press, New York, 1973.Google Scholar
- 25 WOODS, W.A., BXTES, M., BROWN, G., BRUCE, B., COOK, C., KLOVSTAD, J., MAKHOUL, J., NAsH-WZBBER, B., SCHWARTZ, R., WOLF, J., AND ZUE, V. Speech understanding systems, Final technical progress report. Tech. Rep. 3438, Bolt, Beranek, and Newman, Cambridge, Mass., Dec. 1976.Google Scholar
- 26 WOODS, W.A., KAPLAN, R.M., AND NAsH-WEBBER, B. The lunar sciences natural language information system. BBN Rep. 2378, Bolt Beranek and Newman, Cambridge, Mass., 1972.Google Scholar
Index Terms
- Developing a natural language interface to complex data
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