Musag an Agent That Learns What You Mean
نویسندگان
چکیده
This paper presents a system that carries out highly eeective searches over collections of textual information, such as those found on the Internet. The system is comprised of two major parts. The rst part consists of an agent, MUSAG, that learns to relate concepts that are semantically \similar" to one another. In other words, this agent dynamically builds a dictionary of expressions for a given concept that captures the words people have in mind when mentioning the speciic concept. The second part consists of another agent, SAg, who is responsible for retrieving documents, given a set of keywords with relative weights. This retrieval makes use of the dictionary learned by MUSAG, in the sense that the documents to be retrieved for a query are semantically related to the concept given. In this way, we overcome two main problems with current text search engines, which are largely based on syntactic methods. One problem is that the keyword given in the 1 query might have ambiguous meaning, leading to the retrieval of documents not related to the topic requested. The second problem concerns relevant documents that will not be recommended to the user, since they did not include the speciic keyword mentioned in the query. Using semantic methods we will be able to retrieve such documents if they include other words, learned by MUSAG, that are related to the main concept. The system also includes two other agents, USAg and Ag. USAg is concerned with the interface to the user, and directs the user's requests to other relevant agents. Ag is a low level agent that is responsible for the output of the search process. We describe the agents' system architecture, along with the nature of their interactions. We also describe our learning and search algorithms, and present results from experiments performed on speciic concepts.
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عنوان ژورنال:
- Applied Artificial Intelligence
دوره 11 شماره
صفحات -
تاریخ انتشار 1997