煤矿事故案例本体知识库的构建及推理研究

针对煤矿事故案例知识组织、共享和再利用不足等问题,将本体引入事故案例知识管理中,研究基于本体的事故案例知识库的构建和推理技术。通过分析事故案例知识构建了事故知识表示元组和事故案例元本体。在此基础上,按照知识库模型和构建方法分别构建了事故案例本体库和规则库,从而完成知识库的构建。以瓦斯事故为例实现了基于煤矿事故案例知识库的知识推理应用。研究结果表明,利用所构建的煤矿事故案例知识库能够较好地进行知识组织和知识的自动推理,提高了煤矿事故案例知识的共享和重用。     

话题案例知识库动态模型及优化策略

传统的话题检测方法仅通过最初几篇话题相关报道的特征来表示话题,不能适应话题动态变化的特点。为此,提出一种话题案例知识库的动态模型。采用资源描述框架,实现话题案例知识表示,并在层次语义树基础上,利用案例融合策略实现话题案例知识库的动态更新,使用最大容忍优化策略解决话题质心漂移问题。实验结果表明,通过提高话题案例知识的全面性和内聚性,该模型能够改进话题检测和追踪的效果。     

基于知识表示的联合问答模型

基于知识库的问答系统旨在通过解析用户的自然语言问句直接在知识库中提取出答案.目前,大多数知识库问答模型都遵循实体检测和关系识别这两个步骤,但是此类方法忽略了知识库本身所蕴含的结构信息以及这两个步骤之间的联系.文中提出了一种基于知识表示的联合问答模型.首先应用知识表示模型将知识库中的实体与关系映射到低维的向量空间,然后通过神经网络将问句也嵌入相同的向量空间,同时检测出问句中的实体,并在此向量空间内度量知识库三元组与问句的语义相似度,从而实现将知识库嵌入和多任务学习引入知识库问答.实验结果表明,所提模型可以极大地提高训练速度,在实体检测和关系识别任务上的准确率达到了主流水平,证明了知识库嵌入及多任务学习可以提升知识库问答任务的性能.     

分布式智能答疑系统的知识库构建与维护研究

构建知识库是搭建分布式环境下智能答疑系统平台的关键环节。文中针对如何表示各站点知识库中的知识以及如何构建和维护知识库问题，提出用XML表示知识来构建知识库，通过使用DOM树来维护知识库，并给出了知识库的组织结构图及XML的DTD文件。实验表明，用该方法构建的知识库便于在系统平台下实现资源共享和知识库维护。     

一种组件化知识库系统的设计与实现

文章提出了一种基于Java Bean的组件化知识库模型,以及一种XML(Extensible Markup Language)标记语言和Java程序设计语言相结合的领域知识表示框架,并实现了该知识表示框架向Java Bean知识库模型自动转换的编译优化系统,方便了知识库的构建与应用。     

基于知识库的决策支持系统关键技术研究

本文将区域循环经济的特点与知识库的原理相结合,研究了决策支持系统中指标的构建和模型选择关键问题,给出了决策模型知识、符号知识、案例知识的表示方法,以及规则和推理机制、知识库的实现原型,为实现指标体系的动态组合,决策方法的智能选择提供了有效的解决方案。     

基于本体的教学知识库研究与应用

知识库是智能教学系统的基础。由于教学知识库的描述标准不统一,知识表示方法也不同,所以导致教学知识难以共享和互操作。将本体引入教学领域知识库建模过程,建立概念共享模型,提供概念语义空间,不仅可以解决智能教学系统中的知识共享和互操作问题,而且易于实现基于本体的语义检索系统,从而大大提高系统的查全率和查准率。     

基于本体的教学知识库研究与应用

知识库是智能教学系统的基础。由于教学知识库的描述标准不统一,知识表示方法也不同,所以导致教学知识难以共享和互操作。将本体引入教学领域知识库建模过程,建立概念共享模型,提供概念语义空间,不仅可以解决智能教学系统中的知识共享和互操作问题,而且易于实现基于本体的语义检索系统,从而大大提高系统的查全率和查准率。     

行业信息化知识库系统知识库设计

行业信息化知识库(KBI)有别于一般的知识库和专家系统,所以根据需求,采用了知识本体的知识表示形式,为了更合理的建设知识库,通过对行业信息化知识的行业结构分析,构建了知识模型,并依据知识表示形式以及构建的知识模型,设计了行业信息化知识库系统的知识库.该知识库是针对战略物资行业的,提出了知识库系统的总体结构,把知识库和数据库相结合,进行了知识库结构的逐步设计.     

基于框架网络结构的专家知识表示方法研究

介绍了一种基于框架网络结构模型的专家知识表示方法，讨论了这种模型的构造原理和实现方案。本方法采用知识的框架网络结构描述地学环境的实体单元，将各级专家知识的表示以指针连接，形成知识到语义的框架网。该数据结构可以实现地理实体及其相互关系的完整性描述，并使系统知识库的操作简便易行，保证了推理机制的实现。     

基于多模态表示学习的知识库补全

目前大多数知识图谱表示学习只考虑实体和关系之间的结构知识,性能受存储知识的限制,造成知识库补全能力不稳定,而融入外部信息的知识表示方法大多只针对某一特定的外部模态信息建模,适用范围有限.因此,文中提出带有注意力模块的卷积神经网络模型.首先,考虑文本和图像两种外部模态信息,提出三种融合外部模态信息和实体的方案,获得实体的...     

基于本体的分布式实例推理技术研究

为了克服单一实例库知识的局限性,实现分布式环境下多数据源的知识重用和共享,提出了一个分布式实例推理系统框架.系统通过本体服务器建立和维护实例库之间的本体知识,其中基本本体为知识的表示提供了全局约束和基础,实例推理服务器可以在基本本体框架下定义领域本体来灵活表达各自的领域知识,而本体目录则为知识的检索提供了向导.本体的引入解决了不同实例库之间知识的互理解和互操作性,能够有效地实现多实例库的协同推理.系统采用Web Service技术构建,是一个开放的系统框架,具有很强的可扩展性.     

An investigation of the K-tree search algorithm for efficient case representation and retrieval

This paper presents a new method of retrieving cases from a case-base on the K-tree search algorithm. Building an automated CBR system relies on representing knowledge in an appropriate form and having efficient case retrieval methods. Using the Intelligent Business Process Reengineering System (IBPRS) architecture as a base, we discuss a model-based case representation approach to solve the knowledge elicitation bottleneck problems. In addition to presenting the model-based case representation method, we introduce a K-tree search method to transform the case base into a tree structure, and discuss how it can be applied to the case retrieval process in IBPRS. The basic idea of the algorithm is to use various attribute values defined in the case label as general information for the case matching and retrieval.     

Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks

Question answering (QA) over knowledge base (KB) aims to provide a structured answer from a knowledge base to a natural language question. In this task, a key step is how to represent and understand the natural language query. In this paper, we propose to use tree-structured neural networks constructed based on the constituency tree to model natural language queries. We identify an interesting observation in the constituency tree: different constituents have their own semantic characteristics and might be suitable to solve different subtasks in a QA system. Based on this point, we incorporate the type information as an auxiliary supervision signal to improve the QA performance. We call our approach type-aware QA. We jointly characterize both the answer and its answer type in a unified neural network model with the attention mechanism. Instead of simply using the root representation, we represent the query by combining the representations of different constituents using task-specific attention weights. Extensive experiments on public datasets have demonstrated the effectiveness of our proposed model. More specially, the learned attention weights are quite useful in understanding the query. The produced representations for intermediate nodes can be used for analyzing the effectiveness of components in a QA system.     

A model of fuzzy spatio-temporal knowledge representation and reasoning based on high-level Petri nets

In many application areas there is a need to represent human-like knowledge related to spatio-temporal relations among multiple moving objects. This type of knowledge is usually imprecise, vague and fuzzy, while the reasoning about spatio-temporal relations is intuitive. In this paper we present a model of fuzzy spatio-temporal knowledge representation and reasoning based on high-level Petri nets. The model should be suitable for the design of a knowledge base for real-time, multi-agent-based intelligent systems that include expert or user human-like knowledge. The central part of the model is the knowledge representation scheme called FuSpaT, which supports the representation and reasoning for domains that include imprecise and fuzzy spatial, temporal and spatio-temporal relationships. The scheme is based on the high-level Petri nets called Petri nets with fuzzy spatio-temporal tokens (PeNeFuST). The FuSpaT scheme integrates the theory of the PeNeFuST and 117 spatio-temporal relations.The reasoning in the proposed model is a spatio-temporal data-driven process based on the dynamical properties of the scheme, i.e., the execution of the Petri nets with fuzzy spatio-temporal tokens. An illustrative example of the spatio-temporal reasoning for two agents in a simplified robot-soccer scene is given.     

摩托车智能设计中知识库系统的研究与实现*

根据摩托车设计知识的特点和分类,应用面向对象的思想实现了实例知识的表示并构建了层次实例库,采用CLIPS语言实现规则知识的表示并创建了规则库。同时运用了元知识的思想实现各个子知识库之间的管理和知识的利用,建立了知识库管理系统,有效地实现了摩托车设计经验知识的获取、查询和维护,为摩托车智能设计提供了知识支持。     

Heterogeneous knowledge representation: integrating connectionist and symbolic computation

Heterogeneous knowledge representation allows combination of several knowledge representation techniques. For instance, connectionist and symbolic systems are two different computational paradigms and knowledge representations. Unfortunately, the integration of different paradigms and knowledge representations is not easy and very often is informal. In this paper, we propose a formal approach to integrate these two paradigms where as a symbolic system we consider a (logic) rule-based system. The integration is operated at language level between neural networks and rule languages. The formal model that allows the integration is based on constraint logic programming and provides an integrated framework to represent and process heterogeneous knowledge. In order to achieve this we define a new language that allows expression and modelling in a natural and intuitive way the above issues together with the operational semantics.