Incremental Iterative Retrieval and Browsing for Efficient Conversational CBR Systems

被引:0
|
作者
Igor Jurisica
Janice Glasgow
John Mylopoulos
机构
[1] University of Toronto,Faculty of Information Studies
[2] Queen's University,Department of Computing and Information Science
[3] University of Toronto,Department of Computer Science
来源
Applied Intelligence | 2000年 / 12卷
关键词
knowledge base technology; case-based reasoning; performance evaluation; context-based iterative browsing and retrieval;
D O I
暂无
中图分类号
学科分类号
摘要
A case base is a repository of past experiences that can be used for problem solving. Given a new problem, expressed in the form of a query, the case base is browsed in search of “similar” or “relevant” cases. Conversational case-based reasoning (CBR) systems generally support user interaction during case retrieval and adaptation. Here we focus on case retrieval where users initiate problem solving by entering a partial problem description. During an interactive CBR session, a user may submit additional queries to provide a “focus of attention”. These queries may be obtained by relaxing or restricting the constraints specified for a prior query. Thus, case retrieval involves the iterative evaluation of a series of queries against the case base, where each query in the series is obtained by restricting or relaxing the preceding query.
引用
收藏
页码:251 / 268
页数:17
相关论文
共 50 条
  • [32] Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems
    Deriul, Jan
    Tuggenerl, Don
    Von Danikenl, Pius
    Campos, Jon Ander
    Rodrigo, Alvaro
    Belkacem, Thiziri
    Soroa, Aitor
    Agirre, Eneko
    Cieliebakl, Mark
    PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP), 2020, : 3971 - 3984
  • [33] Questioning the Key Techniques Underlying the Iterative and Incremental Approach to Information Systems Development
    Yu, Angus G.
    INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY PROJECT MANAGEMENT, 2010, 1 (01) : 15 - 29
  • [34] Incremental Newton's iterative algorithm for optimal control of Ito stochastic systems
    Tian, Jiayue
    Zhao, Xueyan
    Deng, Feiqi
    APPLIED MATHEMATICS AND COMPUTATION, 2022, 421
  • [35] Phase Retrieval Using Iterative Projections: Dynamics in the Large Systems Limit
    Li, Gen
    Gu, Yuantao
    Lu, Yue M.
    2015 53RD ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON), 2015, : 1114 - 1118
  • [36] An efficient incremental method for generating equivalence groups of search results in information retrieval and queries
    Zhang, Jin
    Wei, Qiang
    Chen, Guoqing
    KNOWLEDGE-BASED SYSTEMS, 2012, 32 : 91 - 100
  • [37] Adaptively Efficient Deep Cross-Modal Hash Retrieval Based on Incremental Learning
    Zhou, Kun
    Xu, Liming
    Zheng, Bochuan
    Xie, Yicai
    Computer Engineering and Applications, 2024, 59 (02) : 85 - 93
  • [39] An Efficient Iterative Detection Scheme for Coded MIMO Systems
    Ahmed, Saleem
    Kim, Sooyoung
    2013 INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN), 2013,
  • [40] An Efficient Bayesian Iterative Method for Solving Linear Systems
    Kin Sio FONG
    数学研究及应用, 2012, 32 (03) : 288 - 296