Integrating forward-chaining rules with operations and permanent knowledge bases

Traditionally, rule-based forward-chaining systems are considered to be standalone, working on a volatile memory. This paper focuses on the integration of forward-chaining rules with command-driven programming paradigms in the context of permanent, integrated knowledge bases. A system architecture is proposed that integrates the data management functions of large computerized knowledge bases into a module called a knowledge base management system (KBMS). Experiences we had in integrating rules with operations into a prototype KBMS called DALI are surveyed. For this integration, a new form of production rule, called the activation pattern controlled rule, is introduced, which augments traditional forward-chaining rules by a second, additional left-hand side, which allows making rules sensitive to calls of particular operations. Activation pattern controlled rules play an important role in DALI's system architecture, because they facilitate the storage of knowledge that has been specified relying on mixed programming, a combination of data-driven, command-driven, and preventive programming. The general problems of implementing permanent knowledge bases that contain rules and operations are discussed, and an algorithm for implementating activation pattern controlled rules, called IPTREAT, a generalization of the TREAT algorithm, is provided. Furthermore, the paper intends to clarify the differences between traditional, volatile rule-based systems and rule-based systems that are geared toward knowledge integration by supporting a permanent knowledge base.This paper is an extended and significantly revised version of a paper entitled Integrating Rules into a Knowledge Base Management System, which was presented at the First International Conference on Systems Integration, April 1990 [1].     

An interactive tool for knowledge base refinement

We present a tool that combines two main trends of knowledge base refinement. The first is the construction of interactive knowledge acquisition tools and the second is the development of machine learning methods that automate this procedure. The tool presented here is interactive and gives experts the ability to evaluate an expert system and provide their own diagnoses on specific problems, when the expert system behaves erroneously. We also present a database scheme that supports the collection of specific instances. The second aspect of the tool is that knowledge base refinement and machine learning methods can be applied to the database, in order to automate the procedure refining the knowledge base. In this paper we examine the application of inductive learning algorithms within the proposed framework. Our main goal is to encourage the experts to evaluate expert systems and to introduce new knowledge, based on their experience.     

基于知识库分割的多知识库整合方法

知识库是集团企业云制造平台中知识服务的重要基础,知识库的质量直接决定着知识服务的质量。目前单一分散的知识库不能提供统一的知识资源全局视图,不利于知识资源的共享。多知识库整合技术已经成为该领域的研究热点之一。提出一种基于知识库分割的多知识库整合方法,采用基于蚁群聚类的分割策略,将知识库有效划分为知识块集。在知识块间利用语义概念映射生成知识块间映射图,从而实现多知识库整合。通过对算法的时间复杂度进行分析,表明该方法在时间复杂度方面要优于基于最小概念集的多知识库整合方法;实验结果也表明该方法在运行性能方面明显优于已有的方法。     

A split-combination approach to merging knowledge bases in possibilistic logic

We propose an adaptive approach to merging possibilistic knowledge bases that deploys multiple operators instead of a single operator in the merging process. The merging approach consists of two steps: the splitting step and the combination step. The splitting step splits each knowledge base into two subbases and then in the second step, different classes of subbases are combined using different operators. Our merging approach is applied to knowledge bases which are self-consistent and results in a knowledge base which is also consistent. Two operators are proposed based on two different splitting methods. Both operators result in a possibilistic knowledge base which contains more information than that obtained by the t-conorm (such as the maximum) based merging methods. In the flat case, one of the operators provides a good alternative to syntax-based merging operators in classical logic. This paper is a revised and extended version of [36].     

基于多知识源的语义搭配知识库的构建及应用

针对汉语文本语义搭配错误,在对《现代汉语语义信息词典》(以下简称《语义词典》)及《知网》相关属性的研究基础上,提出了基于多知识源的语义搭配知识库的构建方法,并设计出了面向汉语文本语义查错的三层语义搭配知识库结构.在此基础上,设计并实现了一个汉语文本自动查错算法.实验结果表明,基于上述方法所设计的语义搭配知识库,可以大大提高汉语文本语义查错算法错误查找的召回率,对于语义查错的研究具有一定的借鉴作用.     

Knowledge base refinement: A bibliography

Knowledge base refinement is a learning process aimed at adjusting a knowledge base for the purpose of improving the breadth, accuracy, efficiency, and efficacy of the associated knowledge-based system(s). This annotated bibliography gives an overview of this emerging field.     

利用海量知识库实现实体标注的一种方法

互联网上聚集了大量的文本、图像等非结构化信息,RDF作为W3C提出的互联网上的资源描述框架,非常适合于描述网络上的非结构化信息,因此形成了大量的RDF知识库,如Freebase、Yago、DBPedia等。RDF知识库中包含丰富的语义信息,可以对来自网页的名字实体进行标注,实现语义扩充。将网页上的名字实体映射到知识库中对应实体上称作实体标注。实体标注包括两个主要部分:实体间的映射和标注去歧义。利用海量RDF知识库的特性,提出了一种有效的实体标注方法。该方法采用简单的图加权及计算解决实体标注的去歧义问题。该方法已在云平台上实现,并通过实验验证了其准确度和可扩展性。     

Logic knowledge bases with two default rules

Logic knowledge based systems (LKBS) containing at most one form of default negation and explicit (or “classical”) negation have been studied in the literature. In this paper we describe a class of LKBS containing multiple forms of default negation in addition to explicit negation. We define a semantics for these systems in terms of the well‐founded semantics defined by Van Gelder et al. (1988) and the stable semantics introduced by Gelfond and Lifschitz (1988) and later extended to the 3‐valued case by Przymusinski (1991). We investigate properties of the new combined semantics and calculate the computational complexity of three main reasoning tasks for this semantics, namely existence of models, skeptical and credulous reasoning. An effective procedure to construct the collection of models characterizing the semantics of such a system is given. Applications to knowledge representation and knowledge base merging are presented. This revised version was published online in June 2006 with corrections to the Cover Date.     

On the consistency of knowledge bases: the COVADIS system

It is currently thought in the knowledge-based systems (KBS) domain that sophisticated tools are necessary for helping an expert with the difficult task of knowledge acquisition. The problem of detecting inconsistencies is especially crucial. The risk of inconsistencies increases with the size of the knowledge base; for large knowledge bases, detecting inconsistencies "by hand" or even by a superficial survey of the knowledge base is impossible. Indeed, most inconsistencies are due to the interaction between several rules via often deep deductions. In this paper, we first state the problem and define our approach in the framework of classical logic. We then describe a complete method to prove the consistency (or the inconsistency) of knowledge bases that we have implemented in the COVADIS system.     

动态知识库和概念格在病症智能诊断中的应用

用形式概念分析理论描述了案例库和待诊病人症状,系统借助自动学习策略由实际的案例库生成知识库,然后通过计算概念格间的相似度获得最佳诊断方案,从而实现了病症的智能诊断。     

Polyhedral representation of fuzzy linear bases

 In [15], we introduced the concepts of fuzzy bases, fuzzy linear interpolation and fuzzy polygon of four-component fuzzy linear bases. In [16], these concepts were used in the maximal profile of the set of polygons generated from a set of break points for each variable dimension. Theconcept was operationalized in a fuzzy linear basis algorithm (FLBA) for nonlinear separable programming problems involving no more than a finite number of discontinuities. The FLBA provides a powerful platform forparallel processing of the fuzzy linear sub-problems included in the finite FLB-chain. In this paper we extend the theory of fuzzy linear bases from the set of polygons toapolyhedral representation of four-component fuzzy linear bases defined on a closed subset of the real line.     

A framework of knowledge versioning management

Abstract: Knowledge is an inherently dynamic entity continuously changing and evolving. In many cases, the coexistence of different versions of the same core knowledge is a necessity. So is the availability of the proper environment and tools to deal with knowledge versioning. In this paper, a framework of knowledge versioning management is proposed and implemented dealing with hybrid knowledge representation models using frames and rules. This framework facilitates knowledge version handling and maintenance, improving, in parallel, knowledge sharing and reuse. Knowledge components are stored in a set of tables and handled as data under the auspices of a database management system. The proper structure of tables and their relationships allows the creation of independent knowledge modules. Several knowledge modules can be assembled to construct higher level modules, which finally form versions of knowledge. Corresponding knowledge base versions consist of several knowledge modules easy to handle and process in various application areas. The proposed framework has been implemented and thoroughly examined in an application area of great importance, such as pest management.     

Inventory control as an identification problem based on fuzzy logic

An approach is proposed to solving inventory control problems using information available on current demand and stock. The approach is based on identification of nonlinear dependences using fuzzy knowledge bases. By tuning a fuzzy model against a learning sample, model control actions can be made very close to an expert’s decision. This approach can further be developed by creating adaptive (neuro-fuzzy) inventory control models for enterprises and trading companies. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 123–133, May–June 2006.     

基于RIPPER的旋转机械故障诊断知识获取

数据挖掘技术能够从大量、不完全、有噪声、模糊、随机的实际应用数据中,提取隐含在其中的、人们事先不知道的本质的规律。为了有效地发现旋转机械故障诊断过程中的故障征兆知识,引入数据挖掘技术和方法。针对旋转机械,构建了基于重复增量修枝算法RIPPER（Repeated Incremental Pruning to Produce Error Reduction）的故障诊断知识获取系统。通过收集故障现象并整理成由故障征兆、故障类型等组成的故障信息样本,应用RIPPER算法对故障进行分析得到故障诊断规则集文件,实现故障诊断系统知识的获取和自动更新,并能对旋转机械的常见故障进行诊断,验证了算法的合理性。     

Time-evolving rule-based knowledge bases

Human knowledge in any expertise area changes with respect to time. Two types of such knowledge can be identified, time independent and time dependent. It is shown that the maintenance effort of the latter is harder than that of the former. The present paper applies research results in the area of temporal databases, in order to maintain a rule-based knowledge base whose content changes with respect to the real world time. It is shown that the approach simplifies the maintenance of time dependent knowledge. It also enables the study of the evolution of knowledge with respect to time, which is knowledge on its own. Three distinct solutions are actually proposed and evaluated. Their common characteristic is that knowledge is stored in a database; therefore, all the advantages of databases are inherited by knowledge bases. Implementations are also reported.     

Data refinement and singleton failures refinement are not equivalent

In this paper, we give simple example abstract data types, with atomic operations, that are related by data refinement under a definition used widely in the literature, but these same abstract data types are not related by singleton failure refinement. This contradicts results found in the literature.     

Construction of fuzzy knowledge bases incorporating feature selection

Constructing concise fuzzy rule bases from databases containing many features present an important yet challenging goal in the current researches of fuzzy rule-based systems. Utilization of all available attributes is not realistic due to the “curse of dimensionality” with respect to the rule number as well as the overwhelming computational costs. This paper proposes a general framework to treat this issue, which is composed of feature selection as the first stage and fuzzy modeling as the second stage. Feature selection serves to identify significant attributes to be employed as inputs of the fuzzy system. The choice of key features for inclusion is equivalent to the problem of searching for hypotheses that can be numerically assessed by means of case-based reasoning. In fuzzy modeling, the genetic algorithm is applied to explore general premise structure and optimize fuzzy set membership functions at the same time. Finally, the merits of this work have been demonstrated by the experiment results on a real data set     

Automated fuzzy knowledge acquisition with connectionist adaptation

This paper presents an automated knowledge acquisition architecture for the truck docking problem. The architecture consists of a neural network block, a fuzzy rule generation block and a genetic optimisation block. The neural network block is used to quickly and adaptively learn from trials the driving knowledge. The fuzzy rule generation block then extracts the driving knowledge to form a knowledge rule base. The driving knowledge rule base is further optimised in the genetic optimisation block using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture.     

基于词频同现与WordNet的图像自动标注改善算法研究

图像自动标注是模式识别与计算机视觉等领域中的重要问题。针对现有图像自动标注模型普遍受到语义鸿沟问题的影响,提出了基于关键词同现的图像自动标注改善方法,该方法利用数据集中标注词间的关联性来改善图像自动标注的结果。此外,针对上述方法不能反映更广义的人的知识以及易受数据库规模影响等问题,提出了基于语义相似的图像自动标注改善方法,通过引入具有大量词汇、包含了人知识的结构化电子词典WordNet来计算词汇间的关系并改善图像自动标注结果。实验结果表明,提出的两个图像自动标注改善方法在各项评价指标上相比以往模型均有所提高。