Maintaining Retrieval Knowledge in a Case-Based Reasoning System

The knowledge stored in a case base is central to the problem solving of a case-based reasoning (CBR) system. Therefore, case-base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and similarity knowledge for improved case retrieval, also play an important role in CBR problem solving. For many CBR applications, the refinement of this retrieval knowledge is a necessary component of CBR maintenance. This article focuses on optimization of the parameters and feature selections/weights for the indexing and nearest-neighbor algorithms used by CBR retrieval. Optimization is applied after case-base maintenance and refines the CBR retrieval to reflect changes that have occurred to cases in the case base. The optimization process is generic and automatic, using knowledge contained in the cases. In this article we demonstrate its effectiveness on a real tablet formulation application in two maintenance scenarios. One scenario, a growing case base, is provided by two snapshots of a formulation database. A change in the company's formulation policy results in a second, more fundamental requirement for CBR maintenance. We show that after case-base maintenance, the CBR system did indeed benefit from also refining the retrieval knowledge. We believe that existing CBR shells would benefit from including an option to automatically optimize the retrieval process.     

PROTOTYPICAL CASES FOR RETRIEVAL, REUSE, AND KNOWLEDGE MAINTENANCE IN BIOMEDICAL CASE-BASED REASONING

Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Case-based reasoning (CBR) holds the promise to represent contextual knowledge in a way that was not possible before with traditional knowledge-based methods. One main issue in biomedical CBR is dealing with the rate of generation of new knowledge in biomedical fields, which often makes the content of a case base partially obsolete. This article proposes to make use of the concept of prototypical case to ensure that a CBR system would keep update with current research advances in the biomedical field. Prototypical cases have served various purposes in biomedical CBR systems, among which to organize and structure the memory, to guide the retrieval as well as the reuse of cases, and to serve as bootstrapping a CBR system memory when real cases are not available in sufficient quantity and/or quality. This paper emphasizes the different roles prototypical cases can play in CBR systems, and presents knowledge maintenance as a very important novel role for these prototypical cases.     

Maintaining Case-Based Reasoners: Dimensions and Directions

Experience with the growing number of large-scale and long-term case-based reasoning (CBR) applications has led to increasing recognition of the importance of maintaining existing CBR systems. Recent research has focused on case-base maintenance (CBM), addressing such issues as maintaining consistency, preserving competence, and controlling case-base growth. A set of dimensions for case-base maintenance, proposed by Leake and Wilson, provides a framework for understanding and expanding CBM research. However, it also has been recognized that other knowledge containers can be equally important maintenance targets. Multiple researchers have addressed pieces of this more general maintenance problem, considering such issues as how to refine similarity criteria and adaptation knowledge. As with case-base maintenance, a framework of dimensions for characterizing more general maintenance activity, within and across knowledge containers, is desirable to unify and understand the state of the art, as well as to suggest new avenues of exploration by identifying points along the dimensions that have not yet been studied. This article presents such a framework by (1) refining and updating the earlier framework of dimensions for case-base maintenance, (2) applying the refined dimensions to the entire range of knowledge containers, and (3) extending the theory to include coordinated cross-container maintenance. The result is a framework for understanding the general problem of case-based reasoner maintenance (CBRM). Taking the new framework as a starting point, the article explores key issues for future CBRM research.     

常识知识在事例修改中的应用

事例改写一直是基于事例推理(Case—Based Reasoning，CBR)方法中的难点之一。用知识的观点来诠释基于事例推理，细分了在CBR中所用到的知识，以银行贷款业务为例探讨了常识知识在事例修改中应用的应用方法——综合得分法，并探讨了相应的常识知识的存储。     

A generic, object-oriented case-knowledge representation scheme, and its integration into a wider information management scenario

Abstract: A knowledge base management system (KBMS) realises a combination of techniques found in database management systems and knowledge-based systems. At the data model and knowledge representation level, many systems of this kind constitute a marriage of the relational data model and the rule-based reasoning. Experience has shown that either approach is restricted in the way it can express the demanding information and knowledge structures required for applications like decision support systems. Two new technologies offer an exciting new integrated approach to knowledge management. Object-oriented database management systems (OODBMS) provide an object model that supports powerful abstraction mechanisms to facilitate the modelling of highly structured information. Whereas case-based reasoning (CBR) systems are knowledge bases which organise their capabilities around a memory of past cases and the notion of similarity. Both types of system are built upon two fundamental concepts: 1) the retrieval of entities with potentially complex structure, called objects in the former, and cases in the latter type of system; 2) the organisation of those entities in collections with common characteristics. In an OODBMS such collections are termed extents, and in CBR they are usually called categories. In either system, the conceptual meta notion to represent both, objects as well as extents, and cases as well as categories, is the class.
Revolving around a Conceptual Case Class and extending a standard object model, this paper proposes a novel and general approach to represent case-knowledge and to build KBMSs. The work presented here is a spin-off of the design of an object query language within the ESPRIT project Lynx.     

基于案例推理的软件需求分析研究

针对软件需求分析过程中知识的管理与重用,将CBR技术引入到了软件需求分析的处理中,从知识管理的角度运用基于案例推理的方法提出了软件需求分析的框架模型,并对该模型的关键技术进行了详细说明.     

信息抽取的语义知识资源研究

本文讨论支持信息抽取的语义资源的建设问题, 举例说明了信息抽取至少需要三种层面的语义知识:(i)宏观的话语篇章知识, 籍此可以约束信息抽取的匹配模板的类型, 预测关键性的信息项目在文本中的分布位置；(ii)中观的论元结构知识, 籍此可以建立动词的论元成分跟事件模板的传递与继承关系, 帮助确定代词或空语类跟其先行语的回指关系, 进而确定其语义所指；(iii)微观的逻辑结构知识, 籍此可以确定否定词、量化词、模态词等逻辑算子跟其所约束的成分之间的逻辑关系（比如, 哪些成分处于否定的辖城之中, 其中哪个成分是否定的焦点, 在哪些语法条件下否定词是冗余的, 等等）。最后, 指出研究这三种语义知识所可利用的几种理论和方法。     

Transferring Case Knowledge To Adaptation Knowledge: An Approach for Case-Base Maintenance

In this article we propose a case-base maintenance methodology based on the idea of transferring knowledge between knowledge containers in a case-based reasoning (CBR) system. A machine-learning technique, fuzzy decision-tree induction, is used to transform the case knowledge to adaptation knowledge. By learning the more sophisticated fuzzy adaptation knowledge, many of the redundant cases can be removed. This approach is particularly useful when the case base consists of a large number of redundant cases and the retrieval efficiency becomes a real concern of the user. The method of maintaining a case base from scratch, as proposed in this article, consists of four steps. First, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case base. Second, clustering of cases is carried out to identify different concepts in the case base using the acquired feature-weights knowledge. Third, adaptation rules are mined for each concept using fuzzy decision trees. Fourth, a selection strategy based on the concepts of case coverage and reachability is used to select representative cases. In order to demonstrate the effectiveness of this approach as well as to examine the relationship between compactness and performance of a CBR system, experimental testing is carried out using the Traveling and the Rice Taste data sets. The results show that the testing case bases can be reduced by 36 and 39 percent, respectively, if we complement the remaining cases by the adaptation rules discovered using our approach. The overall accuracies of the two smaller case bases are 94 and 90 percent, respectively, of the originals.     

Concise case indexing of time series in health care by means of key sequence discovery

Coping with time series cases is becoming an important issue in applications of case based reasoning in medical cares. This paper develops a knowledge discovery approach to discovering significant sequences for depicting symbolic time series cases. The input is a case library containing time series cases consisting of consecutive discrete patterns. The proposed approach is able to find from the given case library all qualified sequences that are non-redundant and indicative. A sequence as such is termed as a key sequence. It is shown that the key sequences discovered are highly valuable in case characterization to capture important properties while ignoring random trivialities. The main idea is to transform an original (lengthy) time series into a more concise representation in terms of the detected occurrences of key sequences. Four alternative ways to develop case indexes based on key sequences are suggested and discussed in detail. These indexes are simply vectors of numbers that are easily usable when matching two time series cases for case retrieval. Preliminary experiment results have revealed that such case indexes utilizing key sequence information result in substantial performance improvement for the underlying case-based reasoning system.     

Computational analysis and mapping of ijCSCL content

The purpose of this empirical study is to analyze and map the content of the International Journal of Computer-Supported Collaborative Learning since its inception in 2006. Co-word analysis is the general approach that is used. In this approach, patterns of co-occurrence of pairs of items (words or phrases) identify relationships among ideas. Distances based on co-occurrence frequencies measure the strength of these relationships. Hierarchical clustering and multidimensional scaling are the two complementary exploratory methods relying on these distances that are used to analyze and map the data. Some interesting findings of the work include a map of the key topics covered in the journal and a set of complementary techniques for investigating more specific questions.     

基于规则与ANN的铣削加工参数匹配知识库研究

分析铣削加工参数匹配关系及其知识表示,针对产生式规则难以全面、高效表示加工参数定量匹配知识的问题,提出应用规则推理与人工神经网络（ANN）混合技术构建知识库的方法,给出了参数定量匹配知识表示的神经网络模型和改进的Vogl知识获取方法,运用手册上提供的最复杂样本集数据进行实验验证,结果表明提出的方法具有较好的知识表示性能。最后就如何应用该技术开发面向铣削加工的参数匹配知识库系统展开论述。     

Achieving self-healing in service delivery software systems by means of case-based reasoning

Self-healing, i.e. the capability of a system to autonomously detect failures and recover from them, is a very attractive property that may enable large-scale software systems, aimed at delivering services on a 24/7 fashion, to meet their goals with little or no human intervention. Achieving self-healing requires the elicitation and maintenance of domain knowledge in the form of 〈service failure diagnosis, repair plan〉 patterns, a task which can be overwhelming. Case-Based Reasoning (CBR) is a lazy learning paradigm that largely reduces this kind of knowledge acquisition bottleneck. Moreover, the application of CBR for failure diagnosis and remediation in software systems appears to be very suitable, as in this domain most errors are re-occurrences of known problems. In this paper, we describe a CBR approach for providing large-scale, distributed software systems with self-healing capabilities, and demonstrate the practical applicability of our methodology by means of some experimental results on a real world application.     

范例推理在智能决策系统中的应用研究

范例推理是人工智能中重要的推理方法和机器学习技术,它也是智能系统中实用的技术之一。基于范例的决策是决策者认知心理的决策过程的一个合理描述,它提供了一种实现智能系统及决策的现实环境和技术方法。本文提出了基于范例推理的智能决策技术,给出应用模型,并进行了深入讨论。     

基于模糊层次分析法的导弹总体方案匹配设计

导弹总体方案的论证和总体设计过程是一个包含了对知识的继承、集成、重用、创新和管理的复杂过程。该文分析了导弹总体设计的实际需求,提出了基于模糊层次分析法的快速匹配算法,实现了导弹方案快速匹配设计中的关键技术。该文最后给出了该算法应用的一个实例,研究表明,结合以Oracle为基础的导弹方案CASE库使用该算法,可以快速、方便地进行集成环境下的匹配设计。     

图知识蒸馏综述：算法分类与应用分析

图数据, 如引文网络, 社交网络和交通网络, 广泛地存在现实生活中. 图神经网络凭借强大的表现力受到广泛关注, 在各种各样的图分析应用中表现卓越. 然而, 图神经网络的卓越性能得益于标签数据和复杂的网络模型, 而标签数据获取困难且计算资源代价高昂. 为了解决数据标签的稀疏性和模型计算的高复杂性问题, 知识蒸馏被引入到图神经网络中. 知识蒸馏是一种利用性能更好的大模型(教师模型)的软标签监督信息来训练构建的小模型(学生模型), 以期达到更好的性能和精度. 因此, 如何面向图数据应用知识蒸馏技术成为重大研究挑战, 但目前尚缺乏对于图知识蒸馏研究的综述. 旨在对面向图的知识蒸馏进行全面综述, 首次系统地梳理现有工作, 弥补该领域缺乏综述的空白. 具体而言, 首先介绍图和知识蒸馏背景知识; 然后, 全面梳理3类图知识蒸馏方法, 面向深度神经网络的图知识蒸馏、面向图神经网络的图知识蒸馏和基于图知识的模型自蒸馏方法, 并对每类方法进一步划分为基于输出层、基于中间层和基于构造图知识方法; 随后, 分析比较各类图知识蒸馏算法的设计思路, 结合实验结果总结各类算法的优缺点; 此外, 还列举图知识蒸馏在计算机视觉、自然语言处理、推荐系统等领域的应用; 最后对图知识蒸馏的发展进行总结和展望. 还将整理的图知识蒸馏相关文献公开在GitHub平台上, 具体参见: https://github.com/liujing1023/Graph-based-Knowledge-Distillation.     

Design and implementation of a case-based reasoning system for marketing plans

Unstructured intangible experiences and knowledge are usually difficult to represent and instantiate, which engenders the hardship of knowledge transfer and sharing. Past marketing plans are such valuable documents containing strategic planning knowledge and experiences.Case-Based Reasoning (CBR), which consists of retrieving, reusing, revising, and retaining cases, has been proved effective in retrieving information and knowledge from prior situations and being widely researched and applied in a great variety of problem territories.This paper targets at designing a CBR architecture and a method that facilitate the sharing and retrieving of cases of great concern to the marketing personnel. After an intensive survey of CBR methods and applications, a CBR system embedding multi-attribute decision making method, which provides both overall similarity level and similarity level of each selected attribute, is proposed to enhance the adaptation of a new marketing plan. In addition, a multi-attribute gap analysis diagram is developed to visualize the similarity along with the gap between candidate and target cases, so as to better support interaction and group decision making in the process of strategically formulating a new marketing plan. The CBR system was implemented and successfully demonstrated on case retrieval of a telecommunication company.     

Incorporating prior knowledge in support vector machines for classification: A review

For classification, support vector machines (SVMs) have recently been introduced and quickly became the state of the art. Now, the incorporation of prior knowledge into SVMs is the key element that allows to increase the performance in many applications. This paper gives a review of the current state of research regarding the incorporation of two general types of prior knowledge into SVMs for classification. The particular forms of prior knowledge considered here are presented in two main groups: class-invariance and knowledge on the data. The first one includes invariances to transformations, to permutations and in domains of input space, whereas the second one contains knowledge on unlabeled data, the imbalance of the training set or the quality of the data. The methods are then described and classified into the three categories that have been used in literature: sample methods based on the modification of the training data, kernel methods based on the modification of the kernel and optimization methods based on the modification of the problem formulation. A recent method, developed for support vector regression, considers prior knowledge on arbitrary regions of the input space. It is exposed here when applied to the classification case. A discussion is then conducted to regroup sample and optimization methods under a regularization framework.