Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2001-03-02
2004-04-13
Le, Uyen (Department: 2171)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000
Reexamination Certificate
active
06721728
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to relational analysis and representation, database information retrieval and search engine technology and, more specifically, a system and method of analyzing data in context.
BACKGROUND OF THE INVENTION
The vast amount of text and other types of information available in electronic form have contributed substantially to an “information glut.” In response, researchers are creating a variety of methods to address the need to efficiently access electronically stored information. Current methods are typically based on finding and exploiting patterns in collections of text. Variations among the methods and the factions are primarily due to varying allegiances to linguistics, quantitative analysis, representations of domain expertise, and the practical demands of the applications. Typical applications involve finding items of interest from large collections of text, having appropriate items routed to the correct people, and condensing the contents of many documents into a summary form.
One known application includes various forms of, and attempts to improve upon, keyword search type technologies. These improvements include statistical analysis and analysis based upon grammar or parts of speech. Statistical analysis generally relies upon the concept that common or often-repeated terms are of greater importance than less common or rarely used terms. Parts of speech attach importance to different terms based upon whether the term is a noun, verb, pronoun, adverb, adjective, article, etc. Typically a noun would have more importance than an article therefore nouns would be processed where articles would be ignored.
Other known methods of processing electronic information include various methods of retrieving text documents. One example is the work of Hawking, D. A. and Thistlewaite, P. B.: Proximity Operators—So Near And Yet So Far. In D. K. Harman, (ed.) Proc. Fourth Text Retrieval Conf. (TREC), pp 131-144, NIST Special Publication 500-236, 1996. Hawking, D. A. and Thistlewaite, P. B.: Relevance Weighting Using Distance Between Term Occurrences. Technical Report TR-CS-96-08, Department of Computer Science, Australian National University, June 1996 (Hawking and Thistlewaite (1995, 1996)) on the PADRE system.
The PADRE system applies complex proximity metrics to determine the relevance of documents. PADRE measures the spans of text that contain clusters of any number of target words. Thus, PADRE is based on complex, multi-way (“N-ary”) relations. PADRE's spans and clusters have complex, non-intuitive, and somewhat arbitrary definitions. Each use of PADRE to rank documents requires a user to manually select and specify a small group of words that might be closely clustered in the text. PADRE relevance criteria are based on the assumption that the greatest relevance is achieved when all of the target words are closest to each other. PADRE relevance criteria are generated manually, by the user's own “human free association.” PADRE, therefore, is imprecise and often generates inaccurate search/comparison results.
Other prior art methods include various methodologies of data mining. See for example: Fayyad, U.; Piatetsky-Shapiro, G.; and Smyth, P: The KDD Process for Extracting Useful Knowledge from Volumes of Data. Comm. ACM, vol. 39, no. 11, 1996, pp. 27-34 (Fayyad, et al., 1996). Search engines Zorn, P.; Emanoil, M.; Marshall, L; and Panek, M.: Advanced Web Searching: Tricks of the Trade. ONLINE, vol. 20, no. 3, 1996, pp. 14-28, (Zorn, et al., 1996). Discourse analysis Kitani, T.; Eriguchi, Y.; and Hara, M.: Pattern Matching and Discourse Processing in Information Extraction from Japanese Text. JAIR, vol. 2, 1994, pp. 89-100, (Kitani, et al., 1994). Information extraction Cowie, J. and Lehnert, W.: Information Extraction. Comm. ACM, vol. 39, no. 1, 1996, pp. 81-91, (Cowie, et al., 1996). Information filtering Foltz, P. W. and Dumais, S. T.: Personalized Information Delivery—An Analysis of Information Filtering Methods. Comm. ACM, vol. 35, no. 12, 1992, pp. 51-60, (Foltz, et al., 1992). Information retrieval Salton, G.: Developments in Automatic Text Retrieval, Science, vol. 253, 1991, pp. 974-980, (Salton Developments . . . 1991) and digital libraries Fox, E. A.; Akscyn, R. M.; Furuta, R. K.; and Leggett, J. J.: Digital Libraries-Introduction. Comm. ACM., vol. 38, no. 4, pp. 22-28, 1995 (Fox, et al. 1995). Cutting across these approaches are concerns about how to subdivide words and collections of words into useful pieces, how to categorize the pieces, how to detect and utilize various relations among the pieces, and how transform the many pieces into a smaller number of representative pieces.
Most keyword search methods use term indexing such as used by Salton, G.: A blueprint for automatic indexing. ACM SIGIR Forum, vol. 16, no. 2, 1981. Reprinted in ACM SIGIR Forum, vol. 31, no. 1, 1997, pp. 23-36. (Salton, A blueprint . . . 1981), where a word list represents each document and internal query. As a consequence, given a keyword as a user query, these methods use merely the presence of the keyword in documents as the main criterion of relevance. Some methods such as Jing, Y. and Croft, W. B.: An Association Thesaurus for Information Retrieval. Technical Report 94-17, University of Massachusetts, 1994 (Jing and Croft, 1994); Gauch, S., and Wang, J.: Corpus analysis for TREC 5 query expansion. Proc. TREC 5, NIST SP 500-238, 1996, pp. 537-547 (Gauch & Wang, 1996); Xu, J., and Croft, W.: Query expansion using local and global document analysis. Proc. ACM SIGIR, 1996, pp. 4-11. (Xu and Croft, 1996); McDonald, J., Ogden, W., and Foltz, P.: Interactive information retrieval using term relationship networks. Proc. TREC 6, NIST SP 500-240, 1997, pp. 379-383 (McDonald, Ogden, and Foltz, 1997), utilize term associations to identify or display additional query keywords that are associated with the user-supplied keywords. This results in, “query drift”. Query drift occurs when the additional query keywords retrieve documents that are poorly related or unrelated to the original keywords. Further, term index methods are ineffective in ranking documents on the basis of keywords in context.
In the proximity indexing method of Hawking and Thistlewaite (1996, 1996), a query consists of a user-identified collection of words. These query words are compared with the words in the documents of the database. The search method seeks documents containing length-limited sequences of words that contain subsets of the query words. Documents containing greater numbers of query words in shorter sequences of words are considered to have greater relevance. Further, as with other conventional term indexing schemes, the method of Hawking et al. allows a single query term to be used to identify documents containing the term, but cannot rank the identified documents containing the single query term according to the relevance of the documents to the contexts of the single query term within each document.
Most phrase search and retrieval methods that currently exist, such as Fagan, J. L.: Experiments in automatic phrase indexing for document retrieval: A comparison of syntactic and non-syntactic methods. Ph.D. thesis TR87-868, Department of Computer Science, Cornell University, 1987 (Fagan (1987)); Croft, W. B., Turtle, H. R., and Lewis, D. D.: The use of phrases and structure queries in information retrieval. Proc. ACM SIGIR, 1991, pp. 3245 (Croft, Turtle, and Lewis (1991)); Gey, F. C., and Chen, A.: Phrase discovery for English and cross-language retrieval at TREC 6. Proc. TREC 6, NIST SP 500-240, 1997, pp. 637-644 (Gey and Chen (1997); Gutwin, C., Paynter, G., Witten, I. H., Nevill-Manning, C., and Frank E.: Improving browsing in digital libraries with keyphrase indexes. TR 98-1, Computer Science Department, University of Saskatchewan, 1998 (Gutwin, Paynter, Witten, Nevill-Manning, and Frank (1998)); Jones, S., and Stavely, M.: Phrasier: A system for interactive document retrieval using keyphrases. Proc. ACM SIGIR, 1999, pp. 160-167 (Jones and Staveley (1999)),
Le Uyen
Padilla Rob
Schipper John
Thai Hanh B
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