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.     

模糊面向对象数据模型的描述逻辑表示与推理

通过分析描述逻辑与面向对象数据模型之间的关系,研究了基于描述逻辑的模糊面向对象数据(fuzzy object-oriented data,简称FOOD)模型的表示与推理.首先,进一步研究了FOOD模型,提出了FOOD模型的形式化定义和语义定义,以便更好地在FOOD模型与模糊描述逻辑之间建立对应关系;然后,针对FOOD模型的特点和推理需求,介绍了模糊描述逻辑f-ALCIQ.在此基础上,研究了基于f-ALCIQ的FOOD模型的表示与推理,包括:实现了从FOOD模型到f-ALCIQ知识库在结构层和实例层上的转化,即实现了从FOOD模型到f-ALCIQ TBox的转化,以及从FOOD模型相应的数据库实例到f-ALCIQ ABox的转化;进而,基于转化得到的f-ALCIQ知识库,研究了如何利用f-ALCIQ的推理机制对FOOD模型的推理问题(一致性、包含性和冗余性等)进行推理;最后,设计并实现了基于f-ALCIQ的模糊描述逻辑推理机(FRsQ推理机),实现了对FOOD模型和f-ALCIQ模糊概念知识的自动推理.     

基于本体的推理机研究

语义网技术的兴起促进了本体技术的发展,本体作为语义网的基石,在知识表示与知识推理方面发挥着重要作用。本体表示语言与描述逻辑相结合,为本体推理的合理性和有效性提供了保证。介绍了本体语言、描述逻辑和描述逻辑推理的基本原理,重点介绍了基于SHIQ描述逻辑的推理机Racer的基本功能及其在智能信息检索中的应用。     

A cloud model-based method for the analysis of accelerated life test data

Various curve fitting models, including the Arrhenius stress model, inverse power law model, and Eyring model have been used to model the load (stress) – life relationship to aid in planning accelerated life tests; that is, the relationship between the mean of the sample lifetimes and the testing stress level. The load-life relationship is a one-to-one relationship: one mean of the sample lifetimes corresponds to one testing stress level. However, due to the random uncertainties existing in the testing stress, the relationship should be a many-to-many relationship rather than one testing stress corresponding one mean lifetime of the tested product. Based on the one-to-one relationship of the mean of the sample lifetimes to the testing stress level, a many-to-many relationship can be derived using the reasoning method presented in this paper. The reasoning method is constructed as ‘If X, then Y.’ X is termed the rule antecedent, and Y is called the rule consequent. They are constructed with the stress values and the sample lifetimes, respectively, based on the cloud model, which represents random uncertainty and fuzzy uncertainty. The reasoning method presented is called the multi-rule-based cloud reasoner, which can refine the one-to-one relationship established by models such as the Arrhenius stress model to a many-to-many relationship. In the case study, the multi-rule-based cloud reasoner was applied to a thermal stress accelerated life test of ammunition fuses. The results from the multi-rule-based cloud reasoner were compared with the estimation results from a normal cloud generator under a stress level of 20 °C. The results showed that the many-to-many relationship between the uncertain stress level and the means of the sample lifetimes was derived by the multi-rule-based cloud reasoner.     

ComR: a combined OWL reasoner for ontology classification

Ontology classification, the problem of computing the subsumption hierarchies for classes (atomic concepts), is a core reasoning service provided by Web Ontology Language (OWL) reasoners. Although general-purpose OWL 2 reasoners employ sophisticated optimizations for classification, they are still not efficient owing to the high complexity of tableau algorithms for expressive ontologies. Profile-specific OWL 2 EL reasoners are efficient; however, they become incomplete even if the ontology contains only a small number of axioms that are outside the OWL 2 EL fragment. In this paper, we present a technique that combines an OWL 2 EL reasoner with an OWL 2 reasoner for ontology classification of expressive SROIQ. To optimize the workload, we propose a task decomposition strategy for identifying the minimal non-EL subontology that contains only necessary axioms to ensure completeness. During the ontology classification, the bulk of the workload is delegated to an efficient OWL 2 EL reasoner and only the minimal non- EL subontology is handled by a less efficient OWL 2 reasoner. The proposed approach is implemented in a prototype ComR and experimental results show that our approach offers a substantial speedup in ontology classification. For the wellknown ontology NCI, the classification time is reduced by 96.9% (resp. 83.7%) compared against the standard reasoner Pellet (resp. the modular reasoner MORe).     

Parallel implementation of a class reasoner

Abstract

One method to overcome the notorious efficiency problems of logical reasoning algorithms in AI has been to combine a general-purpose reasoner with several special-purpose reasoners for commonly used subtasks. In this paper we are using Schubert's (Schubert et al. 1983, 1987) method of implementing a special-purpose class reasoner. We show that it is possible to replace Schubert's preorder number class tree by a preorder number list without loss of functionality. This form of the algorithm lends itself perfectly towards a parallel implementation,¹ and we describe design, coding and testing of such an implementation. Our algorithm is practically independent of the size of the class list, and even with several thousand nodes learning times are under a second and retrieval times are under 500 ms.     

基于本体的高校毕业生就业综合服务系统研究*

讨论采用TOVE方法论,以OWL作为元数据的描述语言,利用Protégé应用程序创建就业领域共享的本体.在.NET集成开发环境下,利用基于XML文档对象模型的编程接口实现对本体实例数据的加载,为本体操作提供了友好的人机交互界面.就业系统中,企事业单位与高校毕业生之间需求的相容匹配机制借助于RacerPro推理机和JBuilder集成环境.     

An ontology-based approach to ADL recognition in smart homes

This paper presents a method for recognition of Activities of Daily Living (ADL) in smart homes. Recognition of activities of daily living and tracking them can provide unprecedented opportunities for health monitoring and assisted living applications, especially for elderly people and people with memory deficits. This paper presents Recognizing Activities of Daily Living (RADL) by discovering and monitoring patterns of ADLs in sensor equipped smart homes. The RADL is composed of two components: smart home management monitoring and ADL pattern monitoring. This paper studies the ontology base and the reasoning that are the main parts of ADL pattern monitoring. The ontology for RADL is designed and the prototype system of RADL is implemented using Protégé and Jess tools. Also, the ontology for RADL is verified by OntoCheck in automatic mode and evaluated by a metric-based approach in manual mode.     

基于本体的领域知识搜索研究

对当前互联网的搜索引擎中无法准确、高效地返回用户真正所需的数据信息等缺点进行分析,利用本体构建领域知识库和本体推理机等方法,设计实现了一个基于本体的领域知识检索系统模型。用户通过对用户本体的选择,可得到相关的目标本体,继续对其追踪,通过本体推理,搜索引擎能准确地搜索到用户所关心的本领域内的相关知识。讨论了该系统结构中各个模块的功能和该系统的特点。     

飞机航电设备综合智能故障诊断专家系统研究

目前,智能诊断方法主要有基于规则推理、模糊模型和案例等诊断方法.在分析了上述方法的特征和局限性的基础上,取长补短,提出了综合智能故障诊断方法,通过综合推理策略及统一协调机制,提高诊断系统的故障诊断能力和诊断效率.说明了专家系统综合知识库的建立、综合推理机及软件设计的实现等技术.