International Business Machines Corporation

Juan Leon-Rodriquez

Lev Kozakov

Youssef Drissi

Moon Ju Kim

Gregory T Brown

Yurdaer Nezihi Doganata

Jun-Jang Jeng

Tong-Haing Fin

Ricardo Vilalta

Noon Ju Kim

Search time is reduced with a search engine that includes a bi-directional inverted index facility which can be accessed with a keyword search in one of a number of languages and provide a listing of documents contained in all of those languages. The keywords in all supported languages are preferably stored in an inverted index lookup table cross referenced to documents in those language containing the keywords. Keywords with the same meaning in different languages are accessible together when that keyword in one of the languages is queried. The search engine containing the table can identify pertinent documents either in a selected language, a second language or in all supported languages, as determined by the user. Information about each document can include not only the identity of the document but also information used in ranking the documents such as the number of times that a keyword appears in that document, and the keywords proximity to other keywords. The use of the inverted index table therefore reduces search time by eliminating the need for translation of keywords, their identification in documents and accumulating of ranking information at search runtime and avoids inaccuracies which may result from full text translations of documents.

Retrieving matching documents by queries in any national language

Euclidean analysis is used to define queries in terms of a multi-axis query space where each of the keywords T, T, . . . T, . . . Tis assigned an axis in that space. Sets of test queries Sfor each one from one of a plurality of server sources, are plotted in the query space. Clusters of the search terms are identified based on the proximity of the plotted query vectors to one another. Predominant servers are identified for each of the clusters. When a search query Sis received, the location of its vector is determined and the servers accessed by the search query Sare those that are predominant in the cluster which its vector may fall or is in closest proximity to.

Optimization of server selection using euclidean analysis of search terms

Query routing is based on identifying the preeminent search systems and data sources for each of a number of information domains. This involves assigning a weight to each search system or data source for each of the information domains. The greater the weight, the more preeminent a search system or data source is in a particular information domain. These weights Wi{1=0, 1,2, . . . N] are computed through a recursive learning process employing meta processing. The meta learning process involves simultaneous interrogation of multiple search systems to take advantage of the cross correlation between the search systems and data sources. In this way, assigning a weight to a search system takes into consideration results obtained about other search systems so that the assigned weights reflect the relative strengths of each of the systems or sources in a particular information domain. In the present process, a domain dataset used as an input to query generator. The query generator extracts keywords randomly from the domain dataset. Sets of the extracted keywords constitute a domain specific search query. The query is submitted to the multiple search systems or sources to be evaluated. Initially, a random average weight is assigned to each search system or source. Then, the meta learning process recursively evaluates the search results and feeds back a weight correction dWi to be applied to each system or source server by using weight difference calculator. After a certain number of iterations, the weights Wi reach stable values. These stable values are the values assigned to the search system under evaluation. When searches are performed, the weights are used to determine search systems or sources that are interrogated.

Query routing based on feature learning of data sources

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Danbury, CT, United States of America

Juan Leon-Rodriquez