Learning to Understand Information on the Internet: An Example-Based Approach

被引：20

作者：

Perkowitz M. ^{[1
]}

Doorenbos R.B. ^{[1
]}

Etzioni O. ^{[1
]}

Weld D.S. ^{[1
]}

机构：

[1] Dept. of Comp. Sci. and Engineering, Box 352350, University of Washington, Seattle

来源：

Journal of Intelligent Information Systems | 1997年 / 8卷 / 2期

基金：

美国国家科学基金会;

关键词：

Internet; Machine learning;

D O I：

10.1023/A:1008672508721

中图分类号：

学科分类号：

摘要：

The explosive growth of the Web has made intelligent software assistants increasingly necessary for ordinary computer users. Both traditional approaches - search engines, hierarchical indices - and intelligent software agents require significant amounts of human effort to keep up with the Web. As an alternative, we investigate the problem of automatically learning to interact with information sources on the Internet. We report on ShopBot and ILA, two implemented agents that learn to use such resources. ShopBot learns how to extract information from online vendors using only minimal knowledge about product domains. Given the home pages of several online stores, ShopBot autonomously learns how to shop at those vendors. After its learning is complete, ShopBot is able to speedily visit over a dozen software stores and CD vendors, extract product information, and summarize the results for the user. ILA learns to translate information from Internet sources into its own internal concepts. ILA builds a model of an information source that specifies the translation between the source's output and ILA's model of the world. ILA is capable of leveraging a small amount of knowledge about a domain to learn models of many information sources. We show that ILA's learning is fast and accurate, requiring only a small number of queries per information source.

引用

页码：133 / 153

页数：20

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