Advanced models and Techniques for Expert Interactive Retrieval Assistance

本文将对该系统进行改进、实验分析和评价。 在一个名为CONIT的专家检索助手上完成， 试图捕捉和形式化的一些情报体现， 在人类专家眼中，特别是，CONIT协助用户 开发一个形式化的问题描述， 系统在以下一个或多个中导出并执行搜索策略 三个不同检索系统的400个数据库。 CONIT 进一步帮助用户显示文档记录， 根据搜索结果重新制定搜索策略， 用户的愿望。 有待研究的新援助技术包括 (1)基于检索的文档相关度排序 匹配标准;（2）基于搜索评估的几种方法 估计召回加上时间和成本参数;以及（3） 选择最有利的搜索修改策略， 当前的问题背景。 使用这些工具搜索策略 规划可以建立在更理性、更量化的决策基础上， 使程序相对于更直观，定性 当前检索模式的典型程序。 分析 进行的实验将考虑如何以及特定的 技术和一般检索模型（包含在术语 “智能布尔”），实现这些目标。 一个改良的人类- 计算机接口也将通过转换进行测试 CONIT系统从大型机到工作站环境。

Enhancement, experimental analysis and evalution will be accomplished on an expert retrieval assistant named CONIT which seeks to capture and formalize some of the intelligence embodied in a human expert searcher. In particular CONIT assists users in developing a formalized problem description from which the system derives and executes a search strategy in one or more of 400 databases in three different retrieval systems. CONIT further assists the user in displaying document records and reformulating the search strategy based on search results and user desires. New assistance techniques to be studied include (1) ranking documents by estimated relevance based on retrieval match criteria; (2) several means for search evaluation based on estimates of recall plus time and cost parameters; and (3) selection of the most propitious search modification tactics for the current problem context. With these tools search strategy planning can be based on more rational, quantitative decision- making procedures as opposed to the more intuitive, qualitative procedures typical of current retrieval modalities. Analysis of experiments conducted will consider how well the particular techniques, and general retrieval models (subsumed under the term "smart Boolean"), achieve these goals. An improved human- computer interface will also be tested through conversion of the CONIT system from a mainframe to a workstation environment.