A method to produce a list of documents retrieved from a search engine, ranked according to their relevance to search terms, includes interleaving the lists resulting from two searches. The first search is one that occurs over documents where the list is ranked using a first probabilistic ranking algorithm. The second list is a list of documents obtained by searching over a query log of prior searches using the same search terms. The second list is ranked according to a second probabilistic algorithm. Interleaving of the two lists includes eliminating duplicate occurrences of documents and appending the remaining documents to the end of the interleaved list if the two lists of individual search results differ in length. The interleaved list may then be presented to a user for review.

System and method for blending the results of a classifier and a search engine

Most previous corpus-based algorithms disambiguate a word with a classifier trained from previous usages of the same word. Separate classifiers have to be trained for different words. We present an algorithm that uses the same knowledge sources to disambiguate different words. The algorithm does not require a sense-tagged corpus and exploits the fact that two different words are likely to have similar meanings if they occur in identical local contexts.

Using syntactic dependency as local context to resolve word sense ambiguity

The present invention relates to an advanced search engine. The advanced search engine may include a client component for monitoring an end-user's browsing activity, a remote server (may comprise one or more computers) for storing and processing data received from the client component, and a module that process web pages and serves search results to end-users. The advanced search engine may collect web pages for keywords of proven interest, fetch web pages requested by end-users, generate snippets or abstracts of the web pages, eliminate duplicate web pages, rank the importance of the web pages, and provide relevant web pages or links to web pages in response to an end-user search request for information regarding one or more keywords, for example. Particular aspects of the present invention are described in the claims, specification and drawings.

Method and apparatus for responding to end-user request for information-personalization

We propose to mine structured query templates from search logs, for enabling rich query interpretation that recognizes both query intents and associated attributes. We formalize the notion of template as a sequence of keywords and domain attributes, and our objective is to discover templates with high precision and recall for matching queries in a domain of interest. Our solution bootstraps from small seed input knowledge to discover relevant query templates, by harnessing the wealth of information available in search logs. We model this information in a tri-partite QueST network of queries, sites, and templates. We propose a probabilistic inferencing framework based on the dual metrics of precision and recall- and we show that the dual inferencing correspond respectively to the random walks in backward and forward directions. We deployed and tested our algorithm over a real-world search log of 15 million queries. The algorithm achieved accuracy of as high as 90% (on F-measure), with little seed knowledge and even with incomplete domain schema.

/pdf/towards-rich-query-interpretation-walking-back-and-forth-for-5f6q7hhzom.pdf

Towards rich query interpretation: walking back and forth for mining query templates

We present a novel approach to detect misuse within an information retrieval system by gathering and maintaining knowledge of the behavior of the user rather than anticipating attacks by unknown assailants. Our approach is based on building and maintaining a profile of the behavior of the system user through tracking, or monitoring of user activity within the information retrieval system. Any new activity of the user is compared to the user profile to detect a potential misuse for the authorized user. We propose four different methods to detect misuse in information retrieval systems. Our experimental results on $2$ GB collection favorably demonstrate the validity of our approach.

/pdf/misuse-detection-for-information-retrieval-systems-5dpx4bak69.pdf

Misuse detection for information retrieval systems

User logs of a popular search engine keep track of user activities including user queries, user click-through from the returned list, and user browsing behaviors. Knowledge about user queries discovered from user logs can improve the performance of the search engine. We propose a data-mining approach that produces generalized query patterns or templates from the raw user logs of a popular commercial knowledge-based search engine that is currently in use. Our simulation shows that such templates can improve search engine's speed and precision, and can cover queries not asked previously. The templates are also comprehensible so web editors can easily discover topics in which most users are interested.

http://cling.csd.uwo.ca/papers/hicss2001.pdf

Mining generalized query patterns from web logs

We propose a data-mining approach that produces generalized query patterns (with generalized keywords) from the raw user logs of the Microsoft Encarta search engine (http://encarta.msn.com). Those query patterns can act as cache of the search engine, improving its performance. The cache of the generalized query patterns is more advantageous than the cache of the most frequent user queries since our patterns are generalized, covering more queries and future queries even those not previously asked. Our method is unique since query patterns discovered reflect the actual dynamic usage and user feedbacks of the search engine, rather than the syntactic linkage structure of web pages (as Google does). Simulation shows that such generalized query patterns improve search engine’s overall speed considerably. The generalized query patterns, when viewed with a graphical user interface, are also helpful to web editors, who can easily discover topics in which users are mostly interested.

/pdf/improving-encarta-search-engine-performance-by-mining-user-1rvq3t74aj.pdf

Weining Qian

Papers

Mining generalized query patterns from web logs

Improving encarta search engine performance by mining user logs