Integrating a semantic-based retrieval agent into case-based reasoning systems: A case study of an online bookstore

Natural language search engines should be developed to provide a friendly environment for business-to-consumer e-commerce that reduce the fatigue customers experience and help them decide what to buy. To support product information retrieval and reuse, this paper presents a novel framework for a case-based reasoning system that includes a collaborative filtering mechanism and a semantic-based case retrieval agent. Furthermore, the case retrieval agent integrates short-text semantic similarity (STSS) and recognizing textual entailment (RTE). The proposed approach was evaluated using competitive methods in the performance of STSS and RTE, and according to the results, the proposed approach outperforms most previously described approaches. Finally, the effectiveness of the proposed approach was investigated using a case study of an online bookstore, and according to the results of case study, the proposed approach outperforms a compared system using string similarity and an existing e-commerce system, Amazon.     

Formalising the Fisherman's Folly puzzle

This paper investigates the challenging problem of encoding the common sense knowledge involved in the manipulation of spatial objects from a reasoning about actions and change perspective. In particular, we propose a formal solution to a puzzle composed of non-trivial objects (such as holes and strings) assuming a version of the Situation Calculus written over first-order Equilibrium Logic, whose models generalise the stable model semantics.     

属性强度函数未知下的煤炭资源采矿权案例估价方法

科学合理的煤炭资源采矿权估价方法有助于实现煤炭资源最优配置。随着煤炭资源采矿权交易市场的快速发展大,案例估价方法将在今后煤炭资源采矿权估价中变得越来越重要。本文基于案例推理和现代统计学等理论和方法,首先揭示了基于案例推理的煤炭资源采矿权估价基本原理,对属性指标、属性强度函数、标的资产、案例资产以及案例相似度等关键概念进行界定;然后建立了属性强度函数未知情形下的煤炭资源采矿权案例选取准则和相似度模型;最后建立了煤炭资源采矿权案例估价模型和估价流程。通过一煤炭资源采矿权实例,对该模型进行了运用研究。研究结果表明:一是运用煤炭资源采矿权案例估价方法估价出的结果非常接近该采矿权的最后交易价格;二是在存在一定数量的可比较交易对象前提下,案例估价方法较其他方法所需数据较少,也更为高效和直接。     

基于C/S结构的选矿厂设备维修管理专家系统

介绍了基于C/S结构的选矿厂设备维修管理专家系统 ,着重论述了该系统功能、结构、知识库的知识表示及其推理机制     

基于案例推理的鱼雷制导系统故障诊断方法

为了解决传统的基于规则推理（RBR）方法存在的不足,将基于案例推理（CBR）方法引入鱼雷制导故障诊断系统中。阐述了案例推理的诊断策略,重点描述了二级案例检索策略及其算法,进而从案例描述、系统结构和工作流程3个方面介绍了基于案例推理的鱼雷制导系统故障诊断方法设计,并在VB6.0平台上开发了故障诊断系统,最后通过具体的故障案例比较了RBR和CBR两种方法。案例分析结果表明,所开发的基于案例推理故障诊断系统能够提高鱼雷制导系统维修的准确性和高效性。     

基于本体和规则的扬弹机故障诊断研究

通过对OWL(Web Ontology Language,OWL)本体和SWRL规则语言进行研究,将基于本体和规则的知识表示和智能推理技术应用到舰船扬弹机的故障诊断中。构建了舰船扬弹机的OWL本体及相关的故障诊断SWRL(Semantic Web Rule Language,SWRC)规则,在此基础上开发出一个故障诊断的系统,并实现了诊断推理过程。该系统能帮助设备维护员快速定位故障发生原因,并提供可选用排除故障的基本措施。     

一种基于构件的军用软件故障诊断方法

针对基于构件的军用软件使用过程中外部原因所造成的软件故障,从典型实例中分析软件故障的特征,软件各构件的运行条件、构件间的依赖关系,结合军用软件部署后的跟踪调研工作,总结其故障机理;然后根据专家系统原理研究故障的推理方法,利用成熟的CLIPS专家系统工具构建故障诊断知识库,使用CLIPS嵌入VC＋＋的方法实现军用软件故障诊断工具;通过分析诊断结果,军用软件故障诊断工具可以实现目标问题构件的快速定位,诊断效率高。     

基于事例推理的胶印质量管理专家系统

基于事例推理具有获取知识方便、易于理解、启发思维等特点,介绍了CBR的概念和关键技术,并将其应用于胶印质量控制领域.分析研究了基于事例的推理在胶印质量控制系统中应用的可行性,结果表明优于传统的基于规则的推理.     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.