关系知识表达模式及在专家系统中的应用

采用关系模式表达知识，可以利用当前流行的关系数据库管理系统（ＲＤＢＭＳ），将专家系统与ＲＤＢＭＳ上建立的管理信息系统，决策支持系统，办公自动化系统等有机结合，有利于知识的管理、存储和利用。并可在大量的管理信息，决策支持信息以及办公自动化信息中发现知识，使信息与知识共享。本文研究用关系模式表达知识的方法，并在一分类专家系统中利用ＲＤＢＭＳ强大的数据处理能力实现知识推理。     

融合知识传播和提示学习机制的推荐模型

将知识图谱中的辅助知识应用于推荐系统中，在一定程度上可以缓解数据稀疏问题。但现有基于知识图谱的推荐方法大多只利用实体间的显式关系建模用户行为，而用户和推荐物品之间可能存在无法显式表达的关系。因此，该文提出了一种融合知识图谱传播特征和提示学习范式的推荐模型。首先，以用户与物品的历史交互为起点，利用知识图谱传播用户偏好，获得用户的动态行为信息；然后，将用户静态属性特征信息作为输入，利用提示学习技术，引入预训练语言模型中的隐式知识，挖掘出用户的潜在兴趣，作为对知识图谱显式知识的补充；最后，根据模板词在预训练语言模型词汇表中的概率完成对用户的推荐。实验表明，该方法在MovieLens-1M、Book-Crossing和Last.FM三个数据集上与其他模型相比具有良好的推荐性能，在AUC评价指标上平均分别提升6.4%、4.0%和3.6%,在F₁评价指标上平均分别提升了6.0%、1.8%和3.2%。     

基于实例推理模式关键技术的研究

众所周知，知识获取的瓶颈现象妨碍了专家系统的快速开发，基于实例推理（ＣＢＲ）通过将知识表达为实例克服了这一问题。本文介绍了在设计类专家系统中建立ＣＢＲ模块的一些关键技术，如实例表达、实例的组织与管理以及最佳实例的提取技术等。探讨了在设计类专家系统中实现ＣＢＲ的途径之一。     

低环境负荷水泥配料专家系统的设计与实现

低环境负荷水泥配料专家系统是专家系统技术在水泥工程上的应用。本文重点讨论了低环境负荷水泥配料专家系统中知识获取和表示、推理机等专家系统的核心技术。在知识表示上，采用了产生式、语义网络、框架多种方式相结合的表示方法；在推理机的实现过程中，采用正向推理和逆向推理策略。     

两级多品种生产调度的专家系统

本文以一类两级多品种加工过程的生产调度为背景,提出了一种将人工智能的启发式搜 索优化技术与专家经验规则相结合的专家系统.研究了系统的知识表达、组成与结构,该系统 能方便地处理带有定性约束的问题,并利用问题的知识背景显著地提高了系统的求解效率.最 后给出了在大型炼油厂润滑油系统生产调度中应用的实例.     

雷达装备战场损伤评估专家系统研究

战场损伤评估是一个确定如何处理受损装备的决策过程,对某种装备的损伤评估都需要掌握该装备大量的相关知识和专家经验。因此,目前的研究都希望利用专家系统实现损伤评估的过程,但在理论及实践上都还不太成熟。为此,该文根据电子装备的特点,选择某雷达型号作为研究对象,分析和规划其损伤评估及修复的流程,设计其战场损伤评估专家系统。该专家系统综合运用了语义框架、扩展产生式规则及人工神经网络等多种知识表示方法来表达各种不同类型的知识,并根据不用的知识表示方法采取相应不同的推理策略进行推理,最后给出了损伤评估专家系统的有效模型。     

面向隐式类型化资源的安全防护

在物联网、边缘计算和大数据智能处理高速发展的背景下,安全保护研究的内容已经从显式内容保护扩展到了对隐式内容的保护。多来源内容背景下的隐式内容的安全保护对内容的采集、识别,保护策略定制,保护方法的建模、实现都提出了新的挑战。而实际应用中对性价比的追求更加剧了解决方案的挑战性。受DIKW方法启发提出将保护目标及背景内容分类为数据、信息和知识三种类型化资源。在DIKW上构建基于数据图谱、信息图谱和知识图谱的类型化资源安全资源保护架构。将目标安全资源根据它们在搜索空间中的存在形式分为显式的和隐式的资源,依据不同类型资源表达所对应的表达类型转换及搜索代价差异构造了对应的安全防护方案。该方案支持在不同类型转换过程中的代价以及转换后的搜索代价的差异基础上,设计并提供价值导向的安全服务。     

高炉专家系统知识的实例学习*

知识获取是专家系统建造过程中最重要的环节之一，本文根据高炉炉况知识的特点，采用背景知识学习算法、归纳学习算法，实现了高炉专家系统知识的实例学习。     

基于神经网络的专家系统

针对专家系统知识获取的“瓶颈问题”和神经网络知识表达的“黑箱结构”问题,提出将专家系统与神经网络技术集成,达到优势互补的目的。利用神经网络优良的自组织、自学习和自适应能力来解决专家系统知识获取的困难。提出了基于神经网络专家系统的结构模型,知识表示方式以及推理机制等。     

Web农业专家系统多媒体技术的应用研究

多媒体技术为专家系统多媒体知识的表达提供了良好的技术支持，多媒体知识是专家系统启发式主流知识表示的有益补充。本文结合Web油菜优质高产高效生产专家系统的开发实例，运用ASP技术和DCOM技术很好地实现了在产生式知识规则的前件和后件中多媒体知识的融合及其控制，并实现了多媒体程序与分布式专家系统平台的无缝集成。     

Integrated knowledge acquisition architectures

An architecture for knowledge acquisition systems is proposed based upon the integration of existing methodologies, techniques and tools which have been developed within the knowledge acquisition, machine learning, expert systems, hypermedia and knowledge representation research communities. Existing tools are analyzed within a common framework to show that their integration can be achieved in a natural and principled fashion. A system design is synthesized from what already exists, putting a diversity of well-founded and widely used approaches to knowledge acquisition within an integrative framework. The design is intended to be clean and simple, easy to understand, and easy to implement. A detailed architecture for integrated knowledge acquisition systems is proposed that also derives from parallel cognitive and theoretical studies.     

基于神经网络专家系统的研究与实现

针对传统专家系统推理能力弱和智能水平低等不足,本文采用神经网络方法解决了传统专家系统在知识表示和知识获取等方面的问题。本文从描述传统专家系统几点不足出发,详细阐述了神经网络专家系统的基本原理和框架结构,最后选取三层BP神经网络模型,给出了钻井故障诊断系统的神经网络专家系统的实现。     

Using natural language sources in model-based knowledge acquisition

This paper introduces a well defined co-operation between domain expert, knowledge engineer, and knowledge acquisition and transformation tools. First, the domain expert supported by a hypertext tool generates an intermediate representation from parts of authentic texts of a domain. As a side effect, this representation serves as human readable documentation. In subsequent steps, this representation is semi-automatically transformed into a formal representation by knowledge acquisition tools. These tools are fully adapted to the expert's domain both in terminology and model structure which are developed by the knowledge engineer from a library of generic models and with preparation tools.     

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.     

基于场景及规则获取模板的知识获取方法

产生式规则作为知识库系统进行推理的常用的、可读性好的知识表示形式,在构建知识库系统时有极大的优越性.提出一种基于场景及规则获取模板的知识获取方法,并以某高分子复合材料的加工专家为知识获取对象.该方法通过分析、记录领域专家进行设计的过程、解决问题的过程和动作,将领域问题按层次细化为一系列子问题,并在子问题场景下结合场景模型及知识获取模板来获取规则性知识.采用该方法可以辅助领域专家在明晰领域知识结构的基础上,逐步挖掘领域中细粒度的规则性知识.     

An integrated system for knowledge sharing among heterogeneous knowledge derivation systems

Solving problems in a complex application domain often requires a seamles integration of some existing knowledge derivation systems which have been independently developed for solving subproblems using different inferencing schemes. This paper presents the design and implementation of an Integrated Knowledge Derivation System (IKDS) which allows the user to query against a global database containing data derivable by the rules and constraints of a number of cooperative heterogeneous systems. The global knowledge representation scheme, the global knowledge manipulation language and the global knowledge processing mechanism of IKDS are described in detail. For global knowledge representation, the dynamic aspects of knowledge such as derivational relationships and restrictive dependencies among data items are modeled by a Function Graph to uniformly represent the capabilities (or knowledge) of the rule-based systems, while the usual static aspects such as data items and their structural interrelationships are modeled by an object-oriented model. For knowledge manipulation, three types of high-level, exploratory queries are introduced to allow the user to query the global knowledge base. For deriving the best global answers for queries, the global knowledge processing mechanism allows the rules and constraints in different component systems to be indiscriminately exploited despite the incompatibilities in their inferencing mechanisms and interpretation schemes. Several key algorithms required for the knowledge processing mechanism are described in this paper. The main advantage of this integration approach is that rules and constraints can in effect be shared among heterogeneous rule-based systems so that they can freely exchange their data and operate as parts of a single system. IKDS achieves the integration at the rule level instead of at the system level. It has been implemented in C running in a network of heterogenous component systems which contain three independently developed expert systems with different rule formats and inferencing mechanisms.Database Systems Research and Development Center, Department of Computer Information Sciences, Department of Electrical Engineering, University of Florida     

“CAU”词及其知识图分析

专家系统是人工智能研究领域的一个重要研究分支。专家系统主要由两部分组成:知识库和推理机。知识库中的知识主要由“IF-THEN”这样的知识组成。知识图是一种新的知识表示方法。在知识图中,含有“IF-THEN”结构的句子是由起因操作符(causal operator)或起因关系(CAU-relation)表示的。本文挑选了一些具有一定代表性的起因意义的汉语“CAU”操作符,并且基于知识图理论分析了这些操作符,并进行了分类,目的是为专家系统中知识库的建立做准备。     

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.     

Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. to achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, it is time-consuming to develop fuzzy expert systems. In this article we propose a new approach to elicit expertise and to generate knowledge bases for fuzzy expert systems. A knowledge acquisition system based upon the approach is also presented, which can help knowledge engineers to create, adjust, debug, and execute fuzzy expert systems. Some control techniques are employed in the knowledge acquisition system so that the concepts of fuzzy logic could be directly applied to conventional expert system shells; moreover, a graphic user interface is provided to facilitate the adjustment of membership functions and the display of outputs. the knowledge acquisition system has been integrated with a popular expert system shell, CLIPS, to offer a complete development environment for knowledge engineers. With the help of this environment, the development of fuzzy expert systems becomes much more convenient and efficient. © 1995 John Wiley & Sons, Inc.     

The use of knowledge acquisition in instructional design

Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.