Inference networks for document retrieval
Inference networks for document retrieval
A system for discovering relationships by feature extraction from text databases
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The effectiveness of GIOSS for the text database discovery problem
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Users lost (summary): reflections on the past, future, and limits of information science
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Effective retrieval with distributed collections
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Methods for information server selection
ACM Transactions on Information Systems (TOIS)
Journal of the American Society for Information Science
Overview of the sixth text REtrieval conference (TREC-6)
Information Processing and Management: an International Journal - The sixth text REtrieval conference (TREC-6)
A user-centered design approach to personalization
Communications of the ACM
Helping people find what they don't know
Communications of the ACM
Evaluating evaluation measure stability
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
The impact of database selection on distributed searching
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Collection selection and results merging with topically organized U.S. patents and TREC data
Proceedings of the ninth international conference on Information and knowledge management
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
A cognitive approach to judicial opinion structure: applying domain expertise to component analysis
Proceedings of the 8th international conference on Artificial intelligence and law
Mercator: A scalable, extensible Web crawler
World Wide Web
Pharos: A Scalable Distributed Architecture for Locating Heterogeneous Information Sources
Pharos: A Scalable Distributed Architecture for Locating Heterogeneous Information Sources
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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The continued growth of very large data environments such as Westlaw and Dialog, in addition to the World Wide Web, increases the importance of effective and efficient database selection and searching. Current research focuses largely on completely autonomous and automatic selection, searching, and results merging in distributed environments. This fully automatic approach has significant deficiencies, including reliance upon thresholds below which databases with relevant documents are not searched (compromised recall). It also merges result sets, often from disparate data sources that users may have discarded before their source selection task proceeded (diluted precision). We examine the impact that early user interaction can have on the process of database selection. After analyzing thousands of real user queries, we show that precision can be significantly increased when queries are categorized by the users themselves, then interpreted and treated accurately by the system. Such query categorization strategies may eliminate limitations of fully automated query processing approaches. Our system harnesses the WIN search engine, a sibling to INQUERY, run against one or more authority sources when search is required. We compare our approach to one that does not recognize or utilize distinct features associated with user queries. We show that by avoiding a one-size-fits-all approach that restricts the role users can play in information discovery, database selection effectiveness can be appreciably improved.