FramerD: Representing knowledge in the large

IBM Systems Journal

Volumn 35, Issue 3-4, 1996, Pages 381-397

  Haase, K ^a  

^a MIT MEDIA LAB   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISTRIBUTED COMPUTER SYSTEMS; HUMAN COMPUTER INTERACTION; INFORMATION RETRIEVAL SYSTEMS; KNOWLEDGE REPRESENTATION; LOCAL AREA NETWORKS; OBJECT ORIENTED PROGRAMMING; OPTIMIZATION; WIDE AREA NETWORKS;

CONTENT AWARE MEDIA; FRAMER D; MEDIA CONTENT;

DATABASE SYSTEMS;

EID: 0030414966     PISSN: 00188670     EISSN: None     Source Type: Journal    
DOI: 10.1147/sj.353.0361     Document Type: Article

References (29)

1. S. Brand, The Media Lab, Viking Press (1987).
2. K. Haase, "Matching Texts for Information Extraction," Word-Net: An Online Lexical Database and Its Applications, MIT Press, Cambridge, MA (1996).
3. "Interned symbols" are structures representing strings where lexical equality of the strings implies "pointer equality" of the structures.
4. M. Minsky, "A Framework for Representing Knowledge," The Psychology of Computer Vision, Patrick Winston, Editor, McGraw Hill, New York (1975).
5. I. Goldstein, FRL: A Frame Representation Language, AI Memo 333, MIT, Cambridge, MA (1976).
6. M. Stefik, "An Examination of a Frame Structure Representation System," Proceedings of the Sixth International Joint Conference on Artifical Intelligence, Tokyo (1979).
7. R. MacNeil, "Generating Multimedia Presentations Automatically Using TYRO, the Constraint/Case-Based Designer's Apprentice," Proceedings of the IEEE '91 Workshop on Visual Languages, Kobo, Japan (1991).
8. A. Bruckman, The Electronic Scrapbook: Towards an Intelligent Home-Video Editing System, M.S. thesis, MIT, Cambridge, MA (1991).
9. B. Kernighan and D. Ritchie, The C Programming Language, Prentice-Hall Inc., Englewood Cliffs, NJ (1978).
10. G. Steele, Common Lisp: The Language, Vol. II, Digital Press, Newton, MA (1990).
11. K. Haase, "Framer: A Persistent, Portable, Representation Library," Proceedings European Conference on AI, Amsterdam (August 1994).
12. J. Rees and W. Clinger "The Revised̂3 Report on the Algorithmic Language Scheme," ACM SIGPLAN Notices 21, No. 12 (December 1988).
13. G. Davenport and T. A. Smith, "The Stratification System: A Design Environment for Random Access Video," ACM Workshop on Networking and Operating System Support for Digital Audio and Video, San Diego, CA (1992).
14. A. Portland, R. Picard, G. Davenport, and K. Haase "Video and Image Semantics: Advanced Tools for Telecommunications," IEEE Multimedia 1, No. 3, 73-75 (Summer 1994).
15. M. Davis, Media Streams: Representing Video for Repurposing and Retrieval, Ph.D. thesis, MIT, Cambridge, MA (1995).
16. A. Lippman and R. Kermode, "Media Banks: Entertainment and the Internet," IBM Systems Journal 35, Nos. 3&4, 272-291 (1996, this issue).
17. GNU (a recursive acronym for "GNUs not UNIX") is a project of the Free Software Foundation to implement quality versions of standard software that can be freely distributed and modified.
18. J. Bartlett, Scheme→C: A Portable Scheme-to-C Compiler, Research Report 89/1, Digital Western Research Laboratory, Palo Alto, CA (1989).
19. R. Zabih, D. McAllester, and D. Chapman, "Non-Deterministic LISP with Dependency-Directed Backtracking," Proceedings of the National Conference on Artificial Intelligence, Seattle WA, July 13-17, 1987, pp. 59-66.
20. J. M. Siskind and D. A. McAllester, "Nondeterministic Lisp as a Substrate for Constraint Logic Programming," Proceedings of the National Conference on Artificial Intelligence, Washington, D.C. (July 11-15, 1993), pp. 133-136.
21. For instance, names of the form "eat#1," "eat#2," "eat#3" could be generated to distinguish new objects. However, this runs into collision problems if either a user or a program wants to name an actual object "eat#2," or if two separated users or programs are both generating such names independently.
22. Our current C implementation uses a library of functions for allocating Lisp-like structures. This includes support for maintaining large numbers of simple vectors containing two address-sized values (e.g., four bytes on 32-bit machines, eight on 64-bit machines). An "object stub" consists of one such vector. The first element is either a direct 8-byte object identifier (on 64-bit machines) or a pointer to another vector containing the object identifier. The second element consists of either a pointer to a "detail structure" (aligned on even memory addresses) or an odd integer, which is used to store the bits associated with marked sets.
23. G. Salton, "A Theory of Indexing," Regional Conference Series in Applied Mathematics, Society for Industrial and Applied Mathematics (1975).
24. R. Greiner and D. Lenat, "A Representation Language Language," Proceedings of the First Annual National Conference on Artificial Intelligence, Stanford, CA, August 18-21, 1980, pp. 165-169.
25. K. Haase, "ARLO: Another Representation Language Offer," B.S. thesis, MIT, Cambridge, MA (1984), also available as MIT AI Lab Technical Report 901).
26. The syntax @Name refers to an object with the mnemonic name "Name"; in the actual implementation, the object is uniquely identified by a 64-bit object identifier though several interfaces hide this reference behind the mnemonic name. In the examples here, tokens with identical printed representations (like the several occurrences of @Width) indicate the same object.
27. D. Lenat and L. Guha, "CYC: Building Large Knowledge-Based Systems," Digital Press, Newton, MA (1990).
28. G. Miller, "WordNet: A Lexical Database for English," Communications of the ACM 38, No. 11, 39-41 (1995).
29. P. Karp and S. Paley, "Knowledge Representation in the Large," Proceedings of the 14th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Francisco, CA (1995), pp. 751-758.