PRIMEROSE: PROBABILISTIC RULE INDUCTION METHOD BASED ON ROUGH SETS AND RESAMPLING METHODS

Automated knowledge acquisition is an important research issue in machine learning. Several methods of inductive learning, such as ID3 family and AQ family, have been applied to discover meaningful knowledge from large databases and their usefulness is assured in several aspects. However, since their methods are of a deterministic nature and the reliability of acquired knowledge is not evaluated statistically, these methods are ineffective when applied to domains essentially probabilistic in nature, such as medical domains. Extending concepts of rough set theory to a probabilistic domain, we introduce a new approach to knowledge acquisition, which induces probabilistic rules based on rough set theory (PRIMEROSE) and develop a program that extracts rules for an expert system from a clinical database, using this method. The results show that the derived rules almost correspond to those of the medical experts.     

On the learning of rule uncertainties and their integration into probabilistic knowledge bases

We present a natural and realistic knowledge acquisition and processing scenario. In the first phase a domain expert identifies deduction rules that he thinks are good indicators of whether a specific target concept is likely to occur. In a second knowledge acquisition phase, a learning algorithm automatically adjusts, corrects and optimizes the deterministic rule hypothesis given by the domain expert by selecting an appropriate subset of the rule hypothesis and by attaching uncertainties to them. Then, in the running phase of the knowledge base we can arbitrarily combine the learned uncertainties of the rules with uncertain factual information.Formally, we introduce the natural class of disjunctive probabilistic concepts and prove that this class is efficiently distribution-free learnable. The distribution-free learning model of probabilistic concepts was introduced by Kearns and Schapire and generalizes Valiant's probably approximately correct learning model. We show how to simulate the learned concepts in probabilistic knowledge bases which satisfy the laws of axiomatic probability theory. Finally, we combine the rule uncertainties with uncertain facts and prove the correctness of the combination under an independence assumption.     

Knowledge acquisition of conjunctive rules using multilayered neural networks

A major bottleneck in developing knowledge-based systems is the acquisition of knowledge. Machine learning is an area concerned with the automation of this process of knowledge acquisition. Neural networks generally represent their knowledge at the lower level, while knowledge-based systems use higher-level knowledge representations. the method we propose here provides a technique that automatically allows us to extract conjunctive rules from the lower-level representation used by neural networks, the strength of neural networks in dealing with noise has enabled us to produce correct rules in a noisy domain. Thus we propose a method that uses neural networks as the basis for the automation of knowledge acquisition and can be applied to noisy, realworld domains. © 1993 John Wiley & Sons, Inc.     

基础信息层经验规则的一种表达方法

该文讨论在复杂的大型辅助决策系统中,构造智能决策规则模型的一种方法。这是一种基于决策表的知识表示方法。它在传统决策表的基础上,吸收了产生式规则、框架表示法、模糊理论、关系模型等多种方法的思想和技术,把传统决策表加以扩展,得到了一种结构性好、表达能力强、可操作性较好的智能决策表达工具,用来表示大型辅助决策系统中的复杂领域知识,将其中松散的经验规则形式化成智能决策规则模型,从而增强其结构性和可操作性,有效支持对其它信息的操作。     

Ontologies for agents

An ontology is a computational model of some portion of the world. It is often captured in some form of a semantic network-a graph whose nodes are concepts or individual objects and whose arcs represent relationships or associations among the concepts. This network is augmented by properties and attributes, constraints, functions, and rules that govern the behavior of the concepts. Formally, an ontology is an agreement about a shared conceptualization, which includes frameworks for modeling domain knowledge and agreements about the representation of particular domain theories. Definitions associate the names of entities in a universe of discourse (for example, classes, relations, functions, or other objects) with human readable text describing what the names mean, and formal axioms that constrain the interpretation and well formed use of these names. For information systems, or for the Internet, ontologies can be used to organize keywords and database concepts by capturing the semantic relationships among the keywords or among the tables and fields in a database. The semantic relationships give users an abstract view of an information space for their domain of interest     

Formal properties of net-based knowledge representation schemes

In the spirit of integrating database and artificial intelligence techniques, a number of concepts widely used in relational database theory are introduced in a knowledge representation scheme. A simple network model, which allows the representation of types, is-a-relationships and disjointness constraints is considered. The concepts of consistency and redundancy are introduced and characterized by means of implication of constraints and systems of inference rules, and by means of graph theoretic concepts.     

Automated knowledge acquisition for design and manufacturing: The case of micromachined atomizer

In order to remain competitive in the global market, original equipment manufacturers (OEMs) are developing a process-based, knowledge-driven product development environment with emphasis on the acquisition, storing, and utilization of manufacturing knowledge. This is usually achieved by using the symbolic artificial intelligence (AI) approach. Specifically, knowledge-based expert systems are developed to capture human expertise, mostly in terms of IF–THEN production rules. It has been recognized that the development of symbolic knowledge-based expert systems suffers from the so-called knowledge acquisition bottleneck. Knowledge acquisition is the process of collecting domain knowledge and transforming the knowledge into a computerized representation. It is a challenging and time-consuming process due to the difficulties involved in eliciting knowledge from human experts. This paper presents an automated approach for knowledge acquisition by integrating neural networks learning ability and fuzzy logics structured knowledge representation. Using this approach, knowledge is automatically acquired from data and represented using humanly intelligible fuzzy rules. The approach is applied to a case study of the design and manufacturing of micromachined atomizers for gas turbine engine. The influence of geometric features on the performance of the atomizers is investigated. The results are then compared with those obtained using traditional regression analysis approach (abstract mathematical models). It was found that the automated approach provides an efficient means for knowledge acquisition. Since the fuzzy rules extracted are easy to understand, they can be used to allow more clear specification of manufacturing processes and to shorten learning curves for novice manufacturing engineers.     

Explanation-based learning as justification of knowledge

Abstract: This paper describes the use of the explanation-based learning (EBL) machine learning technique in the practical domain of knowledge acquisition for expert systems. A knowledge acquisition tool, EBKAT (Explanation-Based Knowledge Acquisition Tool), is described, which may be used in the development of knowledge bases for diagnostic expert systems. The functioning of EBKAT attempts to combine the full potential of a domain expert's skills and the power of explanation-based machine learning techniques. The EBL component is not employed in the acquisition of the knowledge base rules but is used to justify the knowledge entered and to relate it to the knowledge already in the system. It is suggested that the EBKAT tool goes some way towards overcoming the knowledge acquisition bottleneck and results in the acquisition of knowledge which is rich in contextual information.     

融合实体描述与路径信息的知识图谱表示学习模型

知识图谱表示学习方法是将知识图谱中的实体和关系通过特定规则表示成一个多维向量的过程。现有表示学习方法多用于解决单跳知识图谱问答任务,其多跳推理能力无法满足实际需求,为提升多跳推理能力,提出一种融合实体描述与路径信息的知识图谱表示学习模型。首先通过预训练语言模型RoBERTa得到融合实体描述的实体、关系表示学习向量;其次利用OPTransE将知识图谱转化成融入有序关系路径信息的向量。最后构建总能量函数,将针对实体描述和路径信息的向量进行融合。通过实验分析与对比该模型在链路预测任务上与主流知识图谱表示学习模型的性能,验证了该模型的可行性与有效性。     

模糊智能信息系统的设计与实现

本文介绍了一个基于模糊关系数据库及模糊推理规则概念的原型模糊信息系统，该系统可对非数据２以及知识描述中的不确定性进行有效的处理。系统使用了类Ｐｒｏｌｏｇ模糊模型，具有较强的推理能力。     

Integrated graphical approach to knowledge representation and acquisition

An approach toward improving the accessbility of the knowledge and information structures of expert systems is described; it is based upon a foundation development environment called the Rule-Based Frame System (RBFS), which forms the kernel of a larger system, IDEAS. RBFS is a knowledge representation language, within which a distinction is drawn between information which represents the world or domain, and knowledge which states how to make conclusions based upon the domain. Information takes the form of frames, for system processing, but is presented to the user/developer as an associative network via a Visual Editor for the Generation of Associative Networks (VEGAN). Knowledge takes the form of production rules, which are connected at suitable points in the domain model, but again it is presented to the user via a graphical interface known as the Knowledge Encoding Tool (KET). KET is designed to assist in knowledge acquisition in expert systems. It uses a combination of decision support trees and associative networks as its representation. A combined use of VEGAN and KET will enable domain experts to interactively create and test their knowledge base with minimum involvement on behalf of a knowledge engineer. An inclusion of learning features in VEGAN/KET is desirable for this purpose. The main objective of these tools, therefore, is to encourage rapid prototyping by the domain expert. VEGAN and KET are implemented in the Poplog environment on SUN 3/50 workstations.     

Learning expert systems by being corrected

This paper describes a new method of knowledge acquisition for expert systems. A program, KABCO, interacts with a domain expert and learns how to make examples of a concept. This is done by displaying examples based upon KABCO's partial knowledge of the domain and accepting corrections from the expert. When the expert judges that KABCO has learnt the domain completely a large number of examples are generated and given to a standard machine learning program that learns the actual expert system rules. KABCO vastly eases the task of constructing an expert system using machine learning programs because it allows expert system rule bases to be learnt from a mixture of general (rules) and specific (examples) information. At present KABCO can only be used for classification domains but work is proceedings to extend it to be useful for other domains. KABCO learns disjunctive concepts (represented by frames) by modifying an internal knowledge base to remain consistent with all the corrections that have been entered by the expert. KABCO's incremental learning uses the deductive processes of modification, exclusion, subsumption and generalization. The present implementation is primitive, especially the user interface, but work is proceeding to make KABCO a much more advanced knowledge engineering tool.     

基于本体的网页数据抽取技术的研究

随着网络上信息的飞速增长,网络已发展成为一个巨大的数据库,人们对快速准确地获取网页数据提出了更多的需求。目前,自然语言处理领域已经将网页信息抽取技术的研究作为一个重点。首先该文介绍了关于本体的一些基础知识,在此基础上提出并实现了一种基于领域本体的网页数据抽取方法。在该文中,利用领域本体的关键词、概念及关系来生成抽取规则,采用语法分析模块对输入的文档进行预处理,最后根据语法分析的机构和生成的抽取规则来对文档实现数据抽取。实验证明,该方法具有良好的性能。     

Incremental acquisition of search knowledge

The development of highly effective heuristics for search problems is a difficult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Though, experts do not normally have complete introspective access to that knowledge, their explanations of actual search considerations seem very valuable in constructing a knowledge-level model of their search processes.Furthermore, for the basis of our knowledge acquisition approach, we substantially extend the work done on Ripple-down rules which allows knowledge acquisition and maintenance without analysis or a knowledge engineer. This extension allows the expert to enter his domain terms during the KA process; thus the expert provides a knowledge-level model of his search process. We call this framework nested ripple-down rules.Our approach targets the implicit representation of the less clearly definable quality criteria by allowing the expert to limit his input to the system to explanations of the steps in the expert search process. These explanations are expressed in our search knowledge interactive language. These explanations are used to construct a knowledge base representing search control knowledge. We are acquiring the knowledge in the context of its use, which substantially supports the knowledge acquisition process. Thus, in this paper, we will show that it is possible to build effective search heuristics efficiently at the knowledge level. We will discuss how our system SmS1.3 (SmS for Smart Searcher) operates at the knowledge level as originally described by Newell. We complement our discussion by employing SmS for the acquisition of expert chess knowledge for performing a highly pruned tree search. These experimental results in the chess domain are evidence for the practicality of our approach.     

基于Web语料的概念获取系统的研究与实现

互联网网页中存在大量的专业知识。如何从这些资源中获取知识已经成为10多年来的一个重要的研究课题。概念和概念间的关系是知识的基本组成部分,因此如何获取并验证概念,成为从文本到知识的过程中的重要步骤。本文提出并实现了一种自动从Web语料中获取概念的方法,该方法利用了规则、统计、上下文信息等多种方法和信息。实验结果表明,该方法达到了较好的效果。     

规则半自动学习的概率软逻辑推理模型

概率软逻辑（PSL）作为一种基于声明式规则的概率模型,具有极强的扩展性和多领域适应性,目前为止,它需要人为给出大量的常识和领域知识作为规则确立的先决条件,这些知识的获取往往非常昂贵并且其中包含的不正确的信息可能会影响推理的正确性。为了缓解这种困境,将C5.0算法和概率软逻辑相结合,让数据和知识共同驱动推理模型,提出了一种规则半自动学习方法。该方法利用C5.0算法提取规则,再辅以人工规则和优化调节后的规则作为改进的概率软逻辑输入。实验结果表明,在学生成绩预测问题上所提方法比C5.0算法和没有规则学习的概率软逻辑具有更高的精度;和纯手工定义规则的方法相比,所提方法能大幅降低人工成本;和贝叶斯网络（BN）、支持向量机（SVM）等算法相比,该方法也表现出不错的效果。     

基于粗糙集信息等价矩阵的关联规则挖掘

利用粗糙集理论,从矩阵分析的角度来挖掘决策表蕴含的信息,引入粗糙集信息等价关系的同构映射——等价矩阵,等价矩阵可看作是等价关系在信息表内的知识表达。给出了等价矩阵的求取算法以及等价矩阵意义下的属性重要度和核的概念。设计了基于等价矩阵的决策信息表的最小属性约简算法。从等价矩阵本身相关操作运算来挖掘客观知识之间的关联模式,提出了基于信息等价矩阵的关联规则提取的算法。实例证明提出的算法有效,为进一步研究决策信息系统的规则提取和决策算法提供了可行的计算方法。