基于SELD描述语言的英文科技文本知识自动获取

知识获取一直是智能系统的瓶颈问题.本文将介绍我们设计的一种英文科技文献描述语言--SELD以及基于SELD的文本知识自动获取技术.SELD是一种与科技英语十分相近的类自然语言,英文科技资料经SELD语言稍加改写后,基于SELD的知识获取系统SELKAS便可以自动对其进行编译、知识提取和整理,形成领域知识库.SELKAS可作为智能系统建造过程中的知识自动获取工具.     

知识库系统技术研究

由于知识在人类生活中的重要性,知识库系统的研究具有十分重要的意义。本文首先定义了什么是知识库和知识库系统,对近年来人们在知识库领域及相关领域所做的工作进行了概括和总结,最后分析了该领域的研究动向。     

面向开放文本的逻辑推理知识抽取与事件影响推理探索

开放文本中蕴含着大量的逻辑性知识,以刻画事物之间逻辑传导关系的逻辑类知识库是推动知识推理发展的重要基础,研发大规模逻辑推理知识库有助于支持由实体或事件等传导驱动的决策任务。该文围绕逻辑推理知识库,论述了知识库的概念、类别和基本构成,提出了一种面向大规模开放文本的实体描述、事件因果逻辑知识快速抽取方法;面向金融领域,探索了一套基于逻辑推理知识库的可解释性路径推理方法和金融实体影响生成系统。算法模型和系统均取得了不错的效果。     

一种基于知识的图象信息表示与检索系统

给出了一种使用一阶谓词逻辑（Prolog语言）为图象内容建模,并结合知识库,实现高效图象检索的方案。基于知识的图象信息表示与检索系统（KIRS）将图象的人工注释、机器自动提取的图象低层信息以及知识库中的知识统一于一致的概念,以知识推理的方式进行检索。     

基于概念空间的文本分类研究

1.引言随着文本信息的快速增长,特别是Internet上在线信息的增加,文本(网页)自动分类已成为一项具有较大实用价值的关键技术,是组织和管理数据的有力手段。文本分类的方法分为两类:一是基于知识的分类方法;二是基于统计的分类方法。基于知识的文本分类系统应用于某一具体领域,需要该领域的知识库作为支撑。由于知识提取、更新、维护以及自我学习等方面存在的种种问题,使得它适用     

面向观点挖掘的汽车评价本体知识库的构建

建立了面向观点挖掘的汽车评价本体知识库,可为挖掘汽车整体信息和特性信息观点提供强大的数据资源.以汽车领域知识为背景,根据汽车知识的关系,构建了汽车评价本体知识库的概念关系.在此基础上,利用Web汽车评论真实语料库,自动获取了本体知识库中的核心概念.最后,采用OWL描述语言,构建了面向观点挖掘的本体知识库.     

一种基于知识的图象信息表示检索系统

给出了一种使用一阶谓词逻辑（Ｐｒｏｌｏｇ语言）为图象内容建模,并结合知识库,实现高效图象检索的方案。基于知识的图象信息表示与检索系统（ＫＩＲＳ）将图象的人工注释、机器自动提取的图象低层信息以及知识库中的知识统一于一致的概念,以知识推理的方式进行检索。     

基于粗糙集理论的故障诊断系统知识库设计

故障诊断是与有效决策密切相关的复杂而困难的问题。粗糙集理论可以有效地分析、处理不完备信息。知识库是整个故障诊断系统的核心,利用基于粗糙集的知识约简和决策规则提取算法,将柴油机故障信息值进行约简,求出其决策规则。知识库由事实库和规则库组成。在知识库中采用链表数据结构,以数据文件形式存储,完成知识库设计的程序。采用粗糙集方法进行故障条件属性约简十分有效,得到简化的决策规则,使得知识库的设计更加方便快捷。     

基于粗糙集理论的故障诊断系统知识库设计

故障诊断是与有效决策密切相关的复杂而困难的同题.粗糙集理论可以有效地分析、处理不完备信息.知识库是整个故障诊断系统的核心,利用基于粗糙集的知识约简和决策规则提取算法,将柴油机故障信息值进行约简,求出其决策规则.知识库由事实库和规则库组成.在知识库中采用链表数据结构,以数据文件形式存储,完成知识库设计的程序.采用粗糙集方法进行故障条件属性约简十分有效,得到简化的决策规则,使得知识库的设计更加方便快捷.     

基于知识分层提取模型的服务台知识库建设

针对传统运维知识库建设的不足,提出了一种面向服务台的生产运维知识自动分层提取模型。通过建立生产运维特征词库,对事件工单的短文本进行向量化解析,并利用改进的 KNN 算法实现事件短文本分类,最终通过领域主题规则完成知识的发现。将此方法应用到企业级服务台知识库建设中,完成了由事件工单到知识的自动转化,弥补了手工创建知识的缺陷,促进了整个运维流程的自动化。     

Acquisition of linguistic patterns for knowledge-based informationextraction

The paper presents an automatic acquisition of linguistic patterns that can be used for knowledge based information extraction from texts. In knowledge based information extraction, linguistic patterns play a central role in the recognition and classification of input texts. Although the knowledge based approach has been proved effective for information extraction on limited domains, there are difficulties in construction of a large number of domain specific linguistic patterns. Manual creation of patterns is time consuming and error prone, even for a small application domain. To solve the scalability and the portability problem, an automatic acquisition of patterns must be provided. We present the PALKA (Parallel Automatic Linguistic Knowledge Acquisition) system that acquires linguistic patterns from a set of domain specific training texts and their desired outputs. A specialized representation of patterns called FP structures has been defined. Patterns are constructed in the form of FP structures from training texts, and the acquired patterns are tuned further through the generalization of semantic constraints. Inductive learning mechanism is applied in the generalization step. The PALKA system has been used to generate patterns for our information extraction system developed for the fourth Message Understanding Conference (MUC-4)     

Modification of belief in evidential causal networks

This paper introduces a new evidential approach for the updating of causal networks which is to be added to an existing general data mining system prototype—the Mining Kernel System (MKS). We present a data mining tool which addresses both the discovery and update of causal networks hidden in database systems. It contributes to the discovery of knowledge which links rules—knowledge which would normally be considered domain knowledge (to be elicited from domain experts). We used different methods for generating networks such as our heuristic algorithm (HNG), which is briefly discussed in this paper. Evaluation of such knowledge presents difficulties but some anecdotal appraisal is presented here in the form of a simple case study.Applications of this prototype with its new causal updating supplement are under way. Our approach is based on Evidence Theory and offers important advantages over conventional Bayesian methods for the applications envisaged. These approaches allow certainty levels of rules in causal networks to be kept up to date. When a causal network has been discovered, any subsequent new evidence may be fed into the model. After updating the belief function for any node the complete network is updated through communication between neighbouring nodes.     

Automatic extraction of OWL ontologies from UML class diagrams: a semantics-preserving approach

Full implementation of the Semantic Web requires widespread availability of OWL ontologies. Manual ontology development using current OWL editors remains a tedious and cumbersome task that requires significant understanding of the new ontology language and can easily result in a knowledge acquisition bottleneck. On the other hand, abundant domain knowledge has been specified by existing database schemata such as UML class diagrams. Thus developing an automatic tool for extracting OWL ontologies from existing UML class diagrams is helpful to Web ontology development. In this paper we propose an automatic, semantics-preserving approach for extracting OWL ontologies from existing UML class diagrams. This approach establishes a precise conceptual correspondence between UML and OWL through a semantics-preserving schema translation algorithm. The experiments with our implemented prototype tool, UML2OWL, show that the proposed approach is effective and a fully automatic ontology extraction is achievable. The proposed approach and tool will facilitate the development of Web ontologies and the realization of semantic interoperations between existing Web database applications and the Semantic Web.     

Extracting Relevant Attribute Values for Improved Search

A new kind of metadata offers a synthesized view of an attribute's values for a user to exploit when creating or refining a search query in data-integration systems. The extraction technique that obtains these values is automatic and independent of an attribute domain but parameterized with various metrics for similarity measures. The authors describe a fully implemented prototype and some experimental results to show the effectiveness of "relevant values" when searching a knowledge base.     

Interactive induction of expert knowledge

The process of extracting, structuring and organizing elicited knowledge (called knowledge acquisition) is a bottleneck in developing knowledge-based systems. A manual approach that elicits domain knowledge by interviewing human experts typically has problems, because the experts are often unable to articulate their reasoning rules. An automatic approach that induces knowledge from a set of training cases also suffers from the unavailability of sufficient training cases. We present an integrated approach that combines the strengths of both methods to compensate for their weaknesses. In this approach, human experts are responsible for solving problems, whereas an inductive learning algorithm is responsible for reasoning and consistency checking.     

面向知识网格的本体学习研究

网格计算正在从单纯的面向大型计算的分布式资源共享发展为一种面向服务的架构,以实现透明而可靠的分布式系统集成。网格智能是指如何获取、预处理、表示和集成不同层次的网格服务(如HTML/XML/RDF/OWL文档、服务响应时间和服务质量等)的数据和信息,并最终转换为有用的智能(知识)。因为高层知识将在未来的网格应用起到越来越重要的作用,本体是知识网格实现的关键。文章提出了一种实现从Web文档中本体(半)自动构建的本体学习框架WebOntLearn,并讨论了本体学习中领域概念的抽取、概念之间关系的抽取和分类体系的自动构建等关键技术。     

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called shallow knowledge of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.     

Object-oriented representations, causal reasoning and expert systems

Abstract: In this article we describe how two popular AI representation techniques—frames and production systems— can be usefully combined under a general AI object-oriented approach to problems which arise in the domain of hardware fault diagnosis. One of the main advantages of such a combination is that causal reasoning— crucial to the domain under consideration — can also be naturally and effectively represented. In order to put the combination of frames and production systems on a sound methodological footing, we first provide a knowledge engineering methodology which is an object-oriented re-interpretation of ontological analysis.     

基于事件动作方向的隐式因果关系抽取方法

抽取事件之间的因果关系能够应用于自动问答、知识提取、常识推理等方面。隐式因果关系由于缺乏明显的词汇特征和中文复杂的句法结构,使得其抽取极为困难,已成为当前研究的难点。相比而言,显示因果关系的抽取比较容易、准确率高,且因果关系事件之间的逻辑关系稳定。为此,文中提出了一种原创的方法,首先通过对抽取的显示因果事件对进行事件动作的归一化处理后形成事件方向,然后对事件主体进行泛化处理,最终形成标准的匹配因果事件对集合。利用此集合根据事件相似度从语句中抽取隐式因果事件对。为了识别更多的隐式因果关系,文中同时提出了一种因果连接词发现算法。在网易财经、腾讯财经和新浪财经上爬取的实验数据验证,对事件动作进行归一化处理后形成事件方向相比传统方法抽取准确率提高了1.02%。     

Causal knowledge elicitation based on elicitation failures

The paper presents an approach to causal knowledge elicitation supported by a tool directly used by the domain expert. This knowledge elicitation approach is characterized by trying to guess an interpretation of the knowledge entered by the expert. The tool (initially general), as it is used, self customizes its guessing capability, remembers failures in guessing (in order to avoid similar failures in the future) and when they occur elicits their explanations. Even in this case, elicitation is supported by guessing on the basis of previous similar failures. The resulting overall effect is that the tool digs up tenaciously causal knowledge from the expert's mind, playing in this way a cooperative role for model building