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.     

知识获取方法DECO

知识获取方法学是目前人工智能领域中最重要的分支之一,现在已出现了几种不同应用范围的获取方法,集成式知识获取方法的研究已成为当前的一种趋势,本文所介绍的DECO方法是一种集成式方法,它将知识获取分为若干个基本上相互独立的步骤,并且溶进了知识库快速生成技术以及基于专家分析实例的学习方法。     

农业智能系统的知识获取技术研究

作者及其团队长期针对农业领域的知识获取技术进行了系列性研究.阐述了运用智能引导、机器学习、数据挖掘、智能计算等技术的人工和自动/半自动的知识获取方法.这些方法能够有效地获取领域知识,发现隐含模式,进行知识精化.研发了知识获取工具.这些方法和工具反映了知识获取技术对农业信息工程所起的重要作用.     

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.     

Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition

This empirical study provides evidence that machine learning models can provide better classification accuracy than explicit knowledge acquisition techniques. The findings suggest that the main contribution of machine learning to expert systems is not just cost reduction, but rather the provision of tools for the development of better expert systems.     

An historical overview of natural language processing systems that learn

A fundamental issue in natural language processing is the prerequisite of an enormous quantity of preprogrammed knowledge concerning both the language and the domain under examination. Manual acquisition of this knowledge is tedious and error prone. Development of an automated acquisition process would prove invaluable.This paper references and overviews a range of the systems that have been developed in the domain of machine learning and natural language processing. Each system is categorised into either a symbolic or connectionist paradigm, and has its own characteristics and limitations described.     

一种基于符号神经网络的知识获取方法

本文介绍一种基于符号神经网络的知识获取方法，该方法首先用传统的机器学习方法获取关于某领域的粗略知识，然后把这些知识映射到神经网络结构，通过神经网络的自学习获取关于该领域的精细知识，这样，既解决了传统机器学习中知识精度，知识表示等问题，又解决了神经网络获取知识时间长，能释能力弱等问题。     

THE USEFULNESS OF A MACHINE LEARNING APPROACH TO KNOWLEDGE ACQUISITION

This paper presents results of experiments showing how machine learning methods arc useful for rule induction in the process of knowledge acquisition for expert systems. Four machine learning methods were used: ID3, ID3 with dropping conditions, and two options of the system LERS (Learning from Examples based on Rough Sets): LEM1 and LEM2. Two knowledge acquisition options of LERS were used as well. All six methods were used for rule induction from six real-life data sets. The main objective was to lest how an expert system, supplied with these rule sets, performs without information on a few attributes. Thus an expert system attempts to classify examples with all missing values of some attributes. As a result of experiments, it is clear that all machine learning methods performed much worse than knowledge acquisition options of LERS. Thus, machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge bases.     

应用遗传算法思想进行知识库自学习的研究

对于知识获取问题,常用的手工方法效率较低,已经不能满足人们的需求,因此提出使用机器学习这种自动知识获取方法来解决该问题.以虚拟旅行代理平台为背景,采用遗传算法和机器学习相结合的理论和方法,将目标知识库的目标荻取问题转化为组合优化问题,并提出了一个目标知识库自学习算法.通过该算法优化出新的旅行目标,实现目标库的更新.实验结果表明,该方法是有效的.     

Task-Structures,Knowledge Acquisition and Learning

One of the old saws about learning in AI is that an agent can only learn what it can be told, i.e., the agent has to have a vocabulary for the target structure which is to be acquired by learning. What this vocabulary is, for various tasks, is an issue that is common to whether one is building a knowledge system by learning or by other more direct forms of knowledge acquisition. I long have argued that both the forms of declarative knowledge required for problem solving as well as problem-solving strategies are functions of the problem-solving task and have identified a family of generic tasks that can be used as building blocks for the construction of knowledge systems. In this editorial, I discuss the implication of this line of research for knowledge acquisition and learning.     

基于遗传算法的知识获取及其在故障诊断中的应用研究

从优化角度出发,本文提出一种基于遗传算法的机器学习方法,该方法能够从学习 实例中总结出有用的知识．针对该优化模型,本文用一种新型的遗传算法对其进行优化,并 将该方法应用于旋转机械故障诊断知识获取过程,仿真实验结果说明该方法是比较有效的．     

装备故障诊断专家系统知识获取方法

针对导弹武器装备故障诊断专家系统的知识获取与机器学习进行研究，系统介绍了知识获取与机器学习系统的构成。提出了一种基于仿真技术的故障知识获取方式，仿真数据经过分析、变换，转化为知识，从而实现知识获取。     

Learning adaptivity in support of flipped learning: An ontological problem‐solving approach

In recent years, flipped learning has received tremendous attention from educational practitioners and researchers. However, this study argues that existing e‐learning systems mainly serve for learning management and content delivery purposes, although they lack support for flipped learning. As an innovative educational approach, flipped learning requires more pedagogical elements, such as integrated instructional design and adaptive content delivery, to achieve effective direct instruction. This study aims to create a learning adaptivity design to support effective learning in the flipped individual learning space in which the teacher is absent. Because teaching involves various pedagogical and content knowledge sources, we propose a conceptual model of teaching as a function of the knowledge triad of Guideline (G), Teaching Activity (T), and Material (M). To realize this conceptualization, an ontological problem‐solving approach is used for knowledge‐based systems development to integrate the relevant knowledge sources. The knowledge model is created using the Protégé platform to develop OWL‐based domain ontology, task ontology, and SWRL‐based semantic rules to enable inference in the GTM triad for learning adaptivity. The case illustration shows that the knowledge‐based system prototype can adaptively guide student learning in the flipped individual learning space with the knowledge sources integrated.     

HAUSS: Incrementally building a summarizer combining multiple techniques

The idea of automatic summarization dates back to 1958, when Luhn invented the “auto abstract” (Luhn, 1958). Since then, many diverse automatic summarization approaches have been proposed, but no single technique has solved the increasingly urgent need for automatic summarization. Rather than proposing one more such technique, we suggest that the best solution is likely a system able to combine multiple summarization techniques, as required by the type of documents being summarized. Thus, this paper presents HAUSS: a framework to quickly build specialized summarizers, integrating several base techniques into a single approach. To recognize relevant text fragments, rules are created that combine frequency, centrality, citation and linguistic information in a context-dependent way. An incremental knowledge acquisition framework strongly supports the creation of these rules, using a training corpus to guide rule acquisition, and produce a powerful knowledge base specific to the domain. Using HAUSS, we created a knowledge base for catchphrase extraction in legal text. The system outperforms existing state-of-the-art general-purpose summarizers and machine learning approaches. Legal experts rated the extracted summaries similar to the original catchphrases given by the court. Our investigation of knowledge acquisition methods for summarization therefore demonstrates that it is possible to quickly create effective special-purpose summarizers, which combine multiple techniques, into a single context-aware approach.     

规则向量投影算法──一种归纳机器学习方法

为了支持在事实不完全或不充分环境中的有效推理，作者提出了一种归纳机器学习方法，并设计了一个规则向量投影算法，使用木文介绍的算法可对原始知识实行归纳，生成含一系列全新分类概念和推理路经的网络知识库，基于该知识库的机器推理系统，在作出诊断决策时所需事实量可大为减少，因此在信息量不足的情况下仍能具有很高的推理性能．     

一种交互式事件常识知识的获取方法

赋予机器常识知识是使机器具有真正智能的必备条件之一,而获得这些常识一直是人工智能研究的一个重要课题。该文提出了一种通过交互的方式来引导知识贡献者给出关于事件的常识知识的方法。方法获取过程是一个机器与贡献者的交互过程: 机器动态地生成问题,对知识贡献者进行提问;知识贡献者通过回答问题给出常识知识。交互过程通过包含提示信息的提问问题对知识贡献者进行提示,运用七种类型问题层层递进地引导知识贡献者思考,以此唤醒他们大脑中的常识知识;通过动态变化的问题改善知识贡献者贡献常识知识过程的趣味性。同时,该文还引入可接受性和有效性两个定量标准评价提问问题,用于进一步改善交互过程。实验结果表明,知识贡献者运用此方法给出的知识量增加了451.61%,同时知识的正确率也达到了92.5%。
     

基于粗集理论知识表达系统的一种归纳学习方法

基于粗集（ＲＳ）理论,针对知识表达系统提出一种新的归纳学习方法,对该方法中条件属性的简化,核值表的求取,决策规则的约简进行了详细讨论,并给出相应的求解算法,本方法为机器学习以及从数据库中进行机器发现提供了新的思路。     

Combining domain knowledge and machine learning for robust fall detection

This paper presents a method for combining domain knowledge and machine learning (CDKML) for classifier generation and online adaptation. The method exploits advantages in domain knowledge and machine learning as complementary information sources. Whereas machine learning may discover patterns in interest domains that are too subtle for humans to detect, domain knowledge may contain information on a domain not present in the available domain dataset. CDKML has three steps. First, prior domain knowledge is enriched with relevant patterns obtained by machine learning to create an initial classifier. Second, genetic algorithms refine the classifier. Third, the classifier is adapted online on the basis of user feedback using the Markov decision process. CDKML was applied in fall detection. Tests showed that the classifiers developed by CDKML have better performance than machine‐learning classifiers generated on a training dataset that does not adequately represent all real‐life cases of the learned concept. The accuracy of the initial classifier was 10 percentage points higher than the best machine‐learning classifier and the refinement added 3 percentage points. The online adaptation improved the accuracy of the refined classifier by an additional 15 percentage points.     

面向文本的本体学习研究概述

对本体（ontology）的研究在计算机领域变得越来越广泛,但手工构造本体是一项繁琐而辛苦的任务,还会导致知识获取瓶颈。本体学习技术是利用本体工程技术和机器学习技术等众多学科技术来实现本体的（半）自动构建。本体的学习可以面向文本、知识库、结构化数据、半结构化数据和无结构数据。本文主要介绍了面向文本的本体学习,并对其中的学习内容、学习方法、学习工具、学习过程和系统评价等关键技术进行了说明,特别介绍了学习方法中的基于统计的方法、词汇句法模式法和形式概念分析法并对其优缺点做了简单的分析。