W. Meng, C. T. Yu, and K.-L. Liu. Detection of heterogeneities in a multiple text database environment. In CoopIS '99.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....normal queries and query probes. Reference [1] probes text databases with queries to determine an approximation of their vocabulary and associated statistics. This technique requires retrieving the documents in the query results for further analysis. Finally, guided query probing has been used in [13] to determine sources of heterogeneity in the algorithms used to index and search locally at each text database. 2. TEXT DATABASE CLASSIFICATION In this section we will describe two basic approaches for classifying text databases. One approach classifies a database into one category when the ....

Weiyi Meng, Clement T. Yu, and King-Lup Liu. Detection of heterogeneities in a multiple text database environment. In Proceedings of the Fourth IFCIS International Conference on Cooperative Information Systems, Edinburgh, Scotland, pages 22--33. IEEE Computer Society Press, 1999.

....probes differently and that the cost to handle query probes is smaller than that for normal queries. Recently, Etzioni and Sugiura [27] used query probing for query expansion to route web queries to the appropriate search engines. Query probing has also been used for other tasks. Meng et al. [20] used guided query probing to determine sources of heterogeneity in the algorithms used to index and search locally at each text database. Query probing has been used by Etzioni et al. 22] to automatically understand query forms and extract information from web databases to build a ....

W. Meng, C. T. Yu, and K.-L. Liu. Detection of heterogeneities in a multiple text database environment. In Proceedings of the Fourth IFCIS International Conference on Cooperative Information Systems, pages 22--33, 1999.

....3 http: www.copernic.com 1 databases with queries to determine an approximation of their vocabulary and associated statistics. This technique requires retrieving the documents in the query results for further analysis. Finally, guided query probing has been used in [MYL99] to determine sources of heterogeneity in the algorithms used to index and search locally at each text database. 2 Classification of Text Databases In this section we will describe two basic approaches for classifying text databases. One approach classifies a database into one category when the ....

Weiyi Meng, Clement T. Yu, and King-Lup Liu. Detection of heterogeneities in a multiple text database environment. In Proceedings of the Fourth IFCIS International Conference on Cooperative Information Systems, Edinburgh, Scotland, September 2-4, 1999, pages 22--33. IEEE Computer Society Press, 1999.

....normal queries and query probes. Reference [1] probes text databases with queries to determine an approximation of their vocabulary and associated statistics. This technique requires retrieving the documents in the query results for further analysis. Finally, guided query probing has been used in [13] to determine sources of heterogeneity in the algorithms used to index and search locally at each text database. 2. TEXT DATABASE CLASSIFICATION In this section we will describe two basic approaches for classifying text databases. One approach classi es a database into one category when the ....

Weiyi Meng, Clement T. Yu, and King-Lup Liu. Detection of heterogeneities in a multiple text database environment. In Proceedings of the Fourth IFCIS International Conference on Cooperative Information Systems, Edinburgh, Scotland, pages 22-33. IEEE Computer Society Press, 1999.

No context found.

W. Meng, C. T. Yu, and K.-L. Liu. Detection of heterogeneities in a multiple text database environment. In CoopIS '99.

....The collection fusion problem is to retrieve documents from selected databases and then merge these documents with the objective of listing more useful documents ahead of less useful ones. Various heterogeneities among multiple search engines often make it very difficult to achieve a good fusion [20]. A good metasearch engine should have the retrieval effectiveness close to that as if all documents were in a single database while minimizing the access cost. In this paper, we propose a new approach to perform database selection and collection fusion. This method uses the framework that was ....

W. Meng, C. Yu, and K. Liu. Detection of Heterogeneities in a Multiple Text Database Environment. CoopIS, 1999.

....The collection fusion problem is to retrieve documents from selected databases and then merge these documents with the objective of listing more useful documents ahead of less useful ones. Various heterogeneities among multiple search engines often make it very dicult to achieve a good fusion [30]. A good metasearch engine should have the retrieval e ectiveness close to that as if all documents were in a single database while minimizing the access cost. In this paper, we propose a new approach to perform database selection and collection fusion. 3 This method uses the framework that was ....

W. Meng, C. Yu, and K. Liu. Detection of Heterogeneities in a Multiple Text Database Environment. CoopIS, 1999.

....The collection fusion problem is to retrieve documents from selected databases and then merge these documents with the objective of listing more useful documents ahead of less useful ones. Various heterogeneities among multiple search engines often makeitvery dicult to achieve a good fusion [22]. A good metasearch engine should have the retrieval effectiveness close to that as if all documents were in a single database while minimizing the access cost. In this paper, we propose a new approach to perform database selection and collection fusion. This method uses the framework that was ....

W. Meng, C. Yu, and K. Liu. Detection of Heterogeneities in a Multiple Text Database Environment. CoopIS, 1999.

....the search results from the local search engines, it must decide how to combine them into a single ranking. This is often known as the collection fusion problem [20, 21, 22] It is a difficult problem as local document similarities from different local search engines may not be comparable. In [15], it is shown that knowing the computation of document similarities by the local search engines may help the merging of search results. Another reason why it is of interest to discover the computation of document similarities of search engines is as follows. Suppose an organization has a ....

W. Meng, C. Yu, and K. Liu. Detection of heterogeneities in a multiple text database environment. In CoopIS, 1999.

....will not be described. Then we discuss the impact of these heterogeneities as well as the autonomy of component search engines on building an effective and efficient metasearch engine. 4. 1 Heterogeneities The following heterogeneities can be identified among autonomous component search engines [50]. Indexing Method: Different search engines may have different ways to determine what terms should be used to represent a given document. For example, some may consider all terms in the document (i.e. full text indexing) while others may use only a subset of the terms (i.e. partial text ....

....in a better position to figure out (1) what local similarities are reasonably comparable; 2) how to adjust some local similarities so that they will become more comparable with others; and (3) how to derive global similarities from local similarities. This is illustrated by the following example [50]. Example 7.1 Suppose it is discovered that all the component search engines selected to answer a given user query employ the same methods to index local documents and to compute local similarities, and the idf information is not used (i.e. the idf weight is 1) then the similarities from these ....

W. Meng, C. Yu, and K. Liu. Detection of Heterogeneities in a Multiple Text Database Environment. Fourth IFCIS Conference on Cooperative Information Systems (CoopIS'99), Edinburgh, Scotland, September 1999.

....The collection fusion problem is to retrieve documents from selected databases and then merge these documents with the objective of listing more useful documents ahead of less useful ones. Various heterogeneities among multiple search engines often make it very difficult to achieve a good fusion [22]. A good metasearch engine should have the retrieval effectiveness close to that as if all documents were in a single database while minimizing the access cost. In this paper, we propose a new approach to perform database selection and collection fusion. This method uses the framework that was ....

W. Meng, C. Yu, and K. Liu. Detection of Heterogeneities in a Multiple Text Database Environment. CoopIS, 1999.

No context found.

W. Meng, C. Yu, and K.L. Liu. Detection of Heterogeneities in a Multiple Text Database Environment. 4th IFCIS International Conference on Cooperative Information systems (CoopIS'99), Edinburgh, September 1999.

....The collection fusion problem is to retrieve documents from selected databases and then merge these documents with the objective of listing useful documents ahead of useless documents. Various heterogeneities among multiple search engines often make it very difficult to achieve a good fusion [27]. A good metasearch engine should have the retrieval effectiveness close to that as if all documents were in a single database while minimizing the access cost. A substantial body of research work addressing different aspects of building an effective and efficient metasearch engine has been ....

W. Meng, C. Yu, and K. Liu. Detection of Heterogeneities in a Multiple Text Database Environment. Fourth IFCIS Conference on Cooperative Information Systems (CoopIS'99), Edinburgh, Scotland, September 1999.

....the search results from the local search engines, it must decide how to combine them into a single ranking. This is often known as the collection fusion problem [23, 24, 25] It is a difficult problem as local document similarities from different local search engines may not be comparable. In [18], it is shown that knowing the computation of document similarities by the local search engines may help the merging of search results. Another reason why it is of interest to discover the computation of document similarities of search engines is determined is as follows. Suppose an organization ....

W. Meng, C. Yu and K.L. Liu. "Detection of Heterogeneities in a Multiple Text Database Environment". CoopIS'99. 10

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