Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
1996-06-27
2003-06-17
Rones, Charles L. (Department: 2175)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000, C707S793000, C707S793000, C707S793000, C704S010000
Reexamination Certificate
active
06581056
ABSTRACT:
FIELD OF THE INVENTION
The present invention is related generally to the field of information retrieval from one or more distal information sources, and in particular to techniques for enabling a user to more efficiently generate queries and browse the results of queries.
BACKGROUND OF THE INVENTION
With the advent of networking technology, the ability to access information from distal information sources has greatly increased. The explosive growth of the Internet and commercial on-line networks and databases (collectively referred to as information sources) are indicative of the high demand for easily accessible information. When interacting with a large information source, a user typically writes a query that returns on the order of 1000 items (which is referred to here as a medium-sized collection). This is about one and a half orders of magnitude more than can be dealt with directly by the user in most task situations. Thus, the user might view a few items, and then narrow the scope and resubmit the query to decrease the number of result items. This cycle is usually repeated many times until the user arrives at a manageable set of documents to view in detail, typically 30-50 documents. This process is referred to as iterative query refinement.
This process has a number of well-known problems, most notably that it is time-consuming and that it often ineffective for arriving at a good collection of 30-50 documents. Often, overly-aggressive attempts to reduce the result set to a manageable size can lead to queries that lock in on too narrow a specification of what is sought. This happens because only a few items are browsed per cycle, and thus very little available information is used to make refinement decisions in each cycle.
Various techniques are used for aiding query refinement. A simple technique is to provide the number of hits for each of the search terms, and/or the number of hits for specified logical combinations of the search terms. This helps the user to determine if there is a problem with a particular search term or logical combination (i.e. it is overly broad or too narrow), or if the search requires more terms for narrowing the number of hits.
Another technique is known as relevance feedback. The essence of this technique is that a user indicates documents in a result set that are relevant. Typically, an automatic procedure is used to reweight, add, or remove terms in a subsequent query (although some systems permit this to be done by the user). Though relevance feedback is most often used in the context of a ranking model of retrieval (i.e. one that scores and orders results), it has also been used in a Boolean context. For example, The European Nuclear Documentation System (ENDS) automatically constructs a Boolean query based on co-occurence of terms in at least 2 documents selected as relevant.
Other known techniques for supporting iterative query refinement include scatter/gather and snippet search. Each of these techniques provide more efficient means of browsing a collection in order to refine the query.
The problem of query refinement is exacerbated when the query is directed towards multiple disparate information sources (databases). This is because each of the information sources may have different functional capabilities, different search engines, operate under different protocols, etc. There are no known systems which been designed to provide aids for query refinement on collections of documents that have been obtained responsive to a query to multiple disparate information sources.
SUMMARY
An information retrieval system which provides for secondary content analysis of retrieved collection of documents is disclosed. The collection of documents is the result of a query to one or more information sources, e.g. databases. The secondary analysis may be performed for various reasons such as summarization of the collection, navigation through the collection, understanding the relationship between the documents in the collection or for query refinement. In the currently preferred embodiment, the secondary analysis is comprised of generating statistical information which may be used for query refinement and for more effective browsing of the ephemeral document collection. The analysis performed is termed secondary since some primary analysis may have been performed at the information sources. The secondary content analysis is performed on an Information Access (IA) client which can couple to the various information sources.
The general method of query, refinement and browsing enabled by the present invention is comprised of the steps of: a user generating a query, the query translated and transmitted to the respective information sources, the results returned and collected, secondary content analysis performed on the returned collection wherein document level and collection level statistics are obtained, the user selects a query refinement option, the statistics are used to provide the selected option, and the user refines their query as needed.
When the document collection is comprised of a collection of primarily textual documents, the process of generating the secondary content analysis is generally comprised of the steps of: tokenizing document text, filtering the tokens according to the type of statistics to be collected and collecting the statistics from the remaining tokens.
In addition to aiding the query refinement process, the performance of secondary content analysis on a collection of documents has other advantages. First, it permits more efficient browsing. Second it permits the use of the query refinement and browsing techniques on documents from information sources that do not support such functions.
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Domingo Richard B.
Rones Charles L.
Xerox Corporation
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