Abstract
Context recognition is an important component of the common sense knowledge problem, which is one of the key research areas in the field of Artificial Intelligence. The paper develops a model of context recognition using the Internet as a knowledge base. The use of the Internet as a database for context recognition gives a context recognition model immediate access to a nearly infinite amount of data in a multiplicity of fields. Context is represented here as any textual description that is most commonly selected by a set of subjects to describe a given situation. The model input is based on any aspect of the situation that can be translated into text (such as: voice recognition, image recognition, facial expression interpretation, and smell identification). The research model is based on the streaming in text format of information that represents situations—Internet chats, e-mails, Shakespeare plays, or article abstracts. The comparison of the results of the algorithm with the results of human subjects yielded a very high agreement and correlation. The results showed there was no significant difference in the determination of context between the algorithm and the human subjects.
Similar content being viewed by others
References
AAAI (1999). Workshop on reasoning in context for AI applications (Workshop Series Tech. Rep. No. WS-99-14). Menlo Park: AAAI.
Aitchison, J., Gilchrist, A., & Bawden, D. (1997). Thesaurus construction and use: A practical manual (3rd ed.). London: Aslib.
Arens, Y., Knoblock, C. A., & Shen, W. (1996). Query reformulation for dynamic information integration. In G. Wiederhold (Ed.), Intelligent integration of information (pp. 11–42). Boston: Kluwer.
Assadi, H. (1998). Construction of a regional ontology from text and its use within a documentary system. Proceedings of the International Conference on Formal Ontology and Information Systems (FOIS-98). Amsterdam: IOS.
Buvac, S. (1996). Resolving lexical ambiguity using a formal theory of context, semantic ambiguity and underspecification. CLSI lecture notes (pp. 1–24).
Carver, N., & Lesser, V. (1992). Blackboard systems for knowledge-based signal understanding. In A. Oppenheim & H. Nawab (Eds.), Symbolic and knowledge-based signal processing (pp. 205–250). Englewood Cliffs: Prentice-Hall.
Dumais, S., & Chen, H. (2000). Hierarchical classification of web content. Proceedings of SIGIR, 23rd ACM International Conference on Research and Development in Information Retrieval, Athens (pp. 256–263).
Ein-Dor, P. (1999). Artificial intelligence: A short history and the next forty years. In K. E. Kendall (Ed.), Emerging information technologies. Thousand Oaks: Sage.
Erman, L., Hayes-Roth, F., Lesser, V., & Reddy, D. R. (1980). The hearsay II speech understanding system: Integrating knowledge to resolve uncertainty. Computing Surveys, 12(2), 213–253.
Gal, A. (1999). Semantic interoperability in information services: Experiencing with CoopWARE. SIGMOD Record, 28(1), 68–75.
Guha, R. V. (1991). Contexts: A formalization and some applications. Doctoral dissertation, Stanford University, Stanford, CT, USA (STAN-CS-91-1399-Thesis).
Hayes-Roth, B. (1985). A blackboard architecture for control. Artificial Intelligence, 26, 251–321.
Kahng, J., & McLeod, D. (1996). Dynamic classification ontologies for discovery in cooperative federated databases. Proceedings of the First IFCIS International Conference on Cooperative Information Systems (CoopIS’96), Brussels, Belgium (pp. 26–35). Belgium.
Lesser, V., Fennell, R., Erman, L., & Reddy, D. R. (1975). Organization of the Hearsay II speech understanding system. IEEE Transactions on Human Factors in Electronics, ASSP-23, 11–24.
McCarthy, J. (1987). Generality in artificial intelligence. Communication of ACM, 30, 1030–1035.
McCarthy, J., & Buvac, S. (1997). Formalizing context, computing natural language (pp. 13–50). Stanford: Stanford University.
Mena, E., Kashyap, V., Illarramendi, A., & Sheth, A. P. (2000). Imprecise answers in distributed environments: Estimation of information loss for multi-ontology based query processing. International Journal of Cooperative Information Systems, 9(4), 403–425.
Modica, G., Gal, A., & Jamil, H. M. (2001). The use of machine-generated ontologies in dynamic information seeking. Proceedings of the Sixth International Conference on Cooperative Information Systems (CoopIS 2001), Trento.
Motro, A., & Rakov, I. (1998). Estimating the quality of databases. Lecture Notes in Computer Science, 1495, 298.
Moulton, A., Madnick, S. E., & Siegel, M. (1998). Context mediation on wall street. Proceedings of the 3rd IFCIS International Conference on Cooperative Information Systems (CoopIS’98) (pp. 271–279). New York: IEEE-CS.
Ouksel, A. M., & Naiman, C. F. (1994). Coordinating context building in heterogeneous information systems. Journal of Intelligent Information Systems, 3(2), 151–183.
Papatheodorou, C., Vassiliou, A., & Simon, B. (2002). Discovery of ontologies for learning resources using word-based clustering. Proceedings of the World Conference on Educational Multimedia, Hypermedia and Telecommunications (ED-MEDIA 2002), Denver, CO (pp. 1523–1528).
Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). London: Butterworths.
Salton, G. (1989). Automatic text processing: The transformation, analysis, and retrieval of information by a computer. Reading: Addison-Wesley.
Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.
Schuyler, P. L., Hole, W. T., & Tuttle, M. S. (1993). The UMLS (Unified Medical Language System) metathesaurus: Representing different views of biomedical concepts. Bulletin of the Medical Library Association, 81, 217–222.
Smith, H., & Poulter, K. (1999). Share the ontology in XML-based trading architectures. Communications of the ACM, 42(3), 110–111.
Soergel, D. (1985). Organizing information: Principles of data base and retrieval systems. Orlando: Academic.
Turney, P. (2002). Mining the web for lexical knowledge to improve keyphrase extraction: Learning from labeled and unlabeled data. (Tech. Rep. No. ERB-1096; NRC #44947). Washington, DC: National Research Council, Institute for Information Technology.
Valdes-Perez, R. E., & Pereira, F. (2000). Concise, intelligible, and approximate profiling of multiple classes. International Journal of Human Computer Studies, 53, 411–436.
Williams, T., Lowrance, J., Hanson, A., & Riseman, E. (1977). Model-building in the VISIONS system. Proceedings of IJCAI-77, Cambridge, MA (pp. 644–645).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Segev, A., Leshno, M. & Zviran, M. Context recognition using internet as a knowledge base. J Intell Inf Syst 29, 305–327 (2007). https://doi.org/10.1007/s10844-006-0015-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10844-006-0015-y