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Journal of Geo-information Science >

2015 , Vol. 17 >Issue 12: 1423 - 1430

DOI: https://doi.org/10.3724/SP.J.1047.2015.01423

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A Tentative Study on Knowledge Engineering for Virtual Geographic Environments

  • LIN Hui , 1, 3, 4 ,
  • YOU Lan , 1, 2, 4, *
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  • 1. Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong 999077, CHina
  • 2. Faculty of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China
  • 3. Department of Geography and Resource Management, Chinese University of Hong Kong, Hong Kong 999077, China
  • 4. Shenzhen Research Institute, Chinese University of Hong Kong, Shenzhen 518057, China
*Corresponding author: YOU Lan, E-mail:

Received date: 2015-11-01

  Request revised date: 2015-11-24

  Online published: 2015-12-20

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Abstract

Geographic knowledge plays an important role in the researches and applications of Virtual Geographic Environments (VGE). Most researches about geographic knowledge engineering are still in the exploring stage. Geographic knowledge engineering for VGE is now a novel subject that has so far not been completely studied. As one component of the new generation of geographic information analysis, VGE has the typical features of multi-discipline, multi-collaboration, multi-interaction, multi-models and multi-sensing. It is urgent to systematically understand the features, mechanisms and key technologies in VGE knowledge engineering. This paper firstly reviewed the research status in knowledge engineering and geographic knowledge engineering from the domestic and abroad perspectives. Then, concepts are proposed regarding the geographic knowledge for VGE and VGE knowledge engineering. Furthermore, the typical features of geographic knowledge in VGE that differ from the common knowledge are discussed in depth. Focusing on the research direction and construction of VGE knowledge engineering in the near future, the key problems within four dimensions that must be resolved have been proposed and discussed. The tentative study of VGE knowledge engineering in this paper may provide a theoretical basis and reference for building the intelligent VGE system, which helps to promote the rapid transformation from the geodata to the geographic knowledge in VGE.

Key words： virtual geographic environments (VGE); geographic knowledge; knowledge engineering; model management

Cite this article

LIN Hui , YOU Lan . A Tentative Study on Knowledge Engineering for Virtual Geographic Environments[J]. Journal of Geo-information Science, 2015 , 17(12) : 1423 -1430 . DOI: 10.3724/SP.J.1047.2015.01423

1 引言

地理知识工程在地理过程模拟、预测及城市规划等应用中发挥着极其重要的作用[1]。一方面,对地观测手段的持续发展与信息增值机制的缺乏致使科研领域普遍存在“数据爆炸但知识贫乏”的现象[2-3]。另一方面,通过多源数据整合、共享、集成与挖掘,虚拟地理环境（VGE）技术对地理科学问题进行深度剖析和多感知表达,能为地理问题、地理规律、地理现象的模拟与预测提供相关知识和决策[4],近年来逐渐成为地学科研及应用的主要研究热点[5]。与传统GIS空间分析相比,虚拟地理环境实现了地理过程和环境的模拟分析与表达,改变了传统的地理科学知识的表达与获取方式,促进了地理数据、信息到知识的快速转换[6],有助于缓解数据丰富与知识匮乏之间的鸿沟。因此,探讨基于虚拟地理环境的地理知识工程是一个迫切而重要的科学问题。
知识是一种存在复杂相互关系的结构化的、相互链接的、不断增长的信息及其存在的复杂的相互关系,其增长来源于这些信息与人的交互,以及其他辅助技术对其所蕴含关系的分析或所蕴含规律的应用[7]。地理知识是知识的概念和内涵在地理学科领域的扩展与延伸。ESRI总裁Jack Dangermond提出一种泛在地理知识概念,认为地理知识是描述地球系统中自然和人文环境的信息,包括数据、模型、制图表达、地理工作流及元数据等[8]。文献[9]将地理知识进一步解释为通过对地理科学领域独特的背景、过程和结果进行解释而得到的增值信息和知识。文献[10]则认为地理知识是解决包括考古学、生态学等不同科研应用领域存在地理问题的有用地理信息。文献[11]-[12]分别从空间认知、知识共享等角度围绕地理知识展开理论和相关技术研究。总体而言,现有地理知识相关研究仍处于初步认识阶段,理论和实践还不够系统和清晰,且绝大多数重点关注传统GIS处理和分析领域。然而,作为新一代GIS框架方法,虚拟地理环境与传统GIS有显著的区别,更强调地理过程机理模型的支撑和多感知多交互的虚拟环境表达,使VGE的地理知识有着自身的特点。目前尚无针对虚拟地理环境中地理知识展开相关的研究。
综上所述,本文首先综述国内外知识工程及地理知识工程研究现状;然后,提出虚拟地理环境地理知识及虚拟地理环境知识工程的概念和定义,探讨分析VGE地理知识区别于通用知识的典型特点,并给出2种VGE地理知识的分类方法;最后,探讨和阐述未来VGE知识工程的研究和建立所需关注的重点及解决方案。

2 国内外研究综述

2.1 知识工程

1977年第五届国际人工智能大会上,美国斯坦福大学计算机系教授爱德华·费根鲍姆首次提出知识工程概念,并明确指出实现智能行为的主要手段在于知识,且多数实际情况下是特定领域的知识[13]。人工智能领域认为,知识是人们对于可重复信息间联系的认识,是信息经过加工整理、解释、挑选和改造而成的[14]。知识工程是以知识为研究对象的新兴学科,通过抽取具体智能系统中共同研究的基本问题作为核心,形成通用方法和理论。现有知识工程相关研究可分为以下5类。
（1）知识的管理
文献[15]围绕知识的发现、获取、共享及应用系统地阐述了知识的管理方法、技术和问题。文献[16]认为传统基于结构化知识库的方式搜索和访问有限,提出一种新的采用开放且低成本基于社交网的知识管理方式。文献[17]-[18]进一步研究了基于社交挖掘的知识的获取与管理。针对软件开发过程中的知识,文献[19]系统地探讨了软件工程知识的重要性、内容体系、管理方式、管理工具及支撑框架。文献[20]从知识的规划、创建、集成、组织、传输、维持以及评价等多个方面,全方位地阐述了知识管理的过程和技术。文献[21]归纳并探讨了符合知识进化内在规律的知识管理模式。文献[22]通过采用用户参与知识库的组织模式,提高知识的可见度与利用率。文献[23]探讨了以知识单元为基础和以知识关联为基础的2种知识组织方式,提出采用分类主题一体化、元数据及专家系统等知识组织策略。文献[24]从知识的形式、结构和运动状态3个角度构建了一个概念管理框架。还有部分研究从管理学角度分别提出了基于知识的组织学习、产生和传播等过程的不同管理模型[25-26]。
（2）知识的表达
伴随知识系统和管理的迅速发展,知识表示的研究不仅只是单纯地解决如何存储,而同时着重关注如何建模以支持高效的知识应用。文献[27]将知识工程方法用于复杂的医疗需求表达,设计了一种支持推理的概念图模型表达医疗专业知识。文献[28]针对地图综合中的知识提出了分类体系及知识表达规则。针对知识管理系统的全局视图和建模工具,典型代表研究有CommonKADS方法[29]。还有部分研究基于本体和语义技术对领域知识进行建模,主要有DAML+OIL、OWL、UML 等建模语言,以及 Protégé知识建模环境等建模工具[30]。
（3）知识的共享
围绕知识传播与共享,现有研究主要针对知识服务、知识库组织及构建相关理论和技术展开。文献[31]探讨了一种研究模型证明知识共享的程度与知识的创新和收益间的正比关系。文献[32]针对高校图书馆指出学科知识库的构建应包括显性知识、馆员的隐性知识及能解决用户特定问题的新的知识成果。通过将分散的知识资源整合封装为一系列适应问题需求的Web服务,可屏蔽平台、系统、语言及模型之间的差异,达到知识资源的良好共享和重用[30]。文献[33]从服务内容、服务对象和服务提供者和服务方式等方面讨论了信息Web服务与知识Web服务的区别。文献[34]针对当前知识库存在的用户交互缺乏、共享程度差、个性化空间缺乏等问题探讨了知识Web服务的实现技术。
（4）知识的协作
通过已有知识与新的信息或知识之间的协作能演化生成新的知识。文献[35]认为知识协作的关键技术包括复杂知识的表示、知识的获取与展现、知识合并与一致性维护、知识识别、查询与推理等。文献[36]对现有知识流模型进行了归纳对比,提出了一种动态知识演化流的生命周期模型。文献[37]-[40]从知识工作流的角度研究知识整合与转化方式,涵盖知识工作流的建模、表达、控制及协作过程。针对知识协作过程中的关键问题,文献[41]提出了一定通用性的知识融合框架层次模型,对其中数据及知识对象进行了形式化定义,并为协作过程建立了系统评估和参数校正的自适应机制。
（5）基于知识的平台及应用
KIM（Knowledge and Information Management Platform）是一个以语义网为模型的知识基础设施,包含数十万个概念和关系的信息,实现了自动化的文档语义标注、索引和检索[42]。文献[43]以知识为核心设计实现了应急救援的决策支持系统。GeneOntology基因本体库是以语义模型为基础的生物基因知识库,能提供RDF、OWL及数据库表等多种形式的知识[44]。文献[45]将智能对象模型、语义网与中医学概念相结合研究,提出了基于智能对象网络模型的三维可视化中医知识库。文献[46]提出了一种知识工程管理平台,支持知识的编码和个性化。文献[47]综述了大量知识工程研究,尤其针对传统知识工程和计算机辅助设计知识工程的相似性和区别。

2.2 地理知识工程

依据研究视角的不同,不同学者和组织机构对地理知识的认识也有所不同。ESRI从地理分析角度提出地理知识是收集到的描述地球上自然和人类环境的地理信息[8]。文献[1]从几何空间的角度强调地理知识是基于数学规则和图论的。Armstrong从地图综合角度提出地理知识包括几何图形知识、语义知识、过程知识和不同学科专家知识[48]。文献[9]从空间服务角度定义地理知识是通过解释地理科学领域独特的背景、过程和结果进而得到的增值信息和知识。文献[10]则认为地理知识是解决考古学、生态学等不同科研领域中存在地理问题的综合地理信息。
地理知识工程可理解为知识工程与地理信息科学结合的交叉学科,它可为解决复杂地理科学问题提供知识支持,并为构建智能GIS系统提供技术支撑。Robert Laurini指出传统知识工程不能表达地理知识中的数学内涵,并提出了一个地理知识工程概念框架,通过制定一系列约束规则来统一管理地理知识[1],该研究重点关注地理几何对象知识的管理。文献[11]从空间认知角度提出了地理知识的分类。文献[9]、[12]从知识共享角度探讨了地理空间知识服务的概念、特征和实现过程。部分研究[49-50]从卫星传感和卫星影像角度研究地理知识及其属性的获取。文献[51]-[53]从空间数据挖掘的角度研究地理知识的发现和提取方法。文献[54]-[55]采用本体论研究地理知识的表达。文献[56]利用XML定义了地理知识的表达规范。文献[57]从地理知识的思维、推理及使用探讨了地理知识的本质。文献[58]构建了一套系统的地理知识概念集合。文献[59]以欧洲的全球卫星导航系统为实例研究基于社交网的经济地理知识。文献[60]探讨了GIS科学中地理知识的不确定性。文献[61]提出将数字地球重新定义为地理知识引擎,将语义互操作认定为核心技术。为了能方便空间信息模型知识的构建,文献[62]设计实现了空间信息服务链模型知识工具GeoChaining。文献[63]实现了一个地理模型知识的共享平台GeoSquare。文献[64]简化和扩展了原有知识体（Body of Knowledge,BoK）的层次结构,清晰描述了概念之间的关系,并提出了一个地理信息科学技术知识体（GIS&T BoK）工程的计算框架。个别学者针对支持多智能体的地理过程模拟提出了一个IVGE（Informed Virtual Geographic Environments）模型的扩展方法[65]。
综上所述,迄今为止,国内外地理知识工程研究有限,仅有极个别研究基于虚拟地理环境展开,整体研究仍然处于起步阶段。作为新一代地理信息科学方法和技术框架,虚拟地理环境具有典型的多领域、多协同、多交互、多模型及多感知特点,亟需系统而深入地研究VGE地理知识工程的特点、机理及关键技术,以促进虚拟地理环境中的地理知识的快速生成和转换,缓解地理科学领域“数据丰富但知识贫乏”现象。

3 VGE地理知识及VGE知识工程

3.1 概念和定义

与传统GIS空间分析相比,虚拟地理环境更注重通过多源数据整合、共享、集成与信息挖掘,借助地理分析模型与多感知表达技术,实现地理问题分析、地理规律提炼、地理现象模拟与预测等[4]。通过多用户的分布式协同交互,VGE可实现不同领域和不同形态的专业知识的融合,从而达到地理知识的创新,这不仅改变了传统的地理知识获取、表达和更新方式,也促进了多学科思维的碰撞,有利于产生新的知识。
VGE地理知识指借助虚拟地理环境解决特定地理科学问题、解释某类地理现象过程或提取一定地理规律相关的抽象可重复的地理相关信息,包括专家经验知识、干预规则、模拟过程,以及计算增值结果等。基于VGE的地理知识可直接来源于书本知识或某领域专家长期积累的经验知识,既可以是通过VGE辅助协同获得的多学科交叉的增值知识,也可以是模拟地球自然机理过程的推导模型知识。这些地理知识既可单个应用于某个具体自然现象的场景模拟,也可通过多种知识在VGE环境中的融合演化来解释或回答某类地球科学问题。
VGE知识工程指实现基于知识的智能虚拟地理环境系统所必须的一套支撑理论、方法和技术 体系。

3.2 VGE地理知识的特点

虚拟地理环境可利用定量分析、数值模拟及交互协同等方法实现多专家用户的地理问题协作建模,模拟时空分布的自然地理过程,以及对地理环境变化进行再现和预测[5, 66-68]。其中,在整个虚拟地理环境的过程和场景中,地理知识以不同形式呈现和介入,以特定任务为导向,逐步融合演化并最终解释或回答科学问题。
地理知识是知识在地理信息科学领域的内涵延伸,具备通用知识的抽象性、可重复及可再生等特点。同时,VGE地理知识还具备以下典型特点：
（1）机理过程相关性
基于VGE地理知识的主要用途是参与地理现象、时空模式及环境变化的模拟与预测,其主要内容是围绕自然或人文地理环境相关的机理过程,如气候变化演变规律知识。而传统GIS分析地理知识主要用于各种地理数据的常规分析计算,该计算相对简单,较少推理过程,地理知识内容主要是围绕计算结果的解释。
（2）时间跨度相关性
时间相关性指VGE地理知识往往与时间范围尺度紧密相关。例如,研究政策活动对区域土地、人文、经济等的影响,需通过连续时间区间的相关数据参与演化过程模型知识的推导,该数据时间跨度的长短直接影响演化过程模型知识的精确性。同时,不同时间跨度区间的演化过程知识也会不 一样。
（3）空间跨度相关性
空间相关性指VGE地理知识往往与空间范围尺度紧密相关。虚拟地理环境常用于研究具有一定空间范围的生态、地理或人文现象,解决这些不同科学问题相对应的地理知识也是不同空间跨度。例如,研究全球气候变化与研究区域气候变化所需的地理知识的跨度明显不同。全球变化气候变化相关地理知识不一定适用于区域气候变化,反之亦然。
（4）多学科相关性
复杂的科学问题往往需要多个领域专业知识的协同解决[61]。VGE应用研究通常是复杂的地理过程相关问题,单一学科知识往往难以解决,需要多学科专业知识融合演化以产生新的针对该类科学问题的地理知识。不同地理现象或过程的研究需各种不同学科知识的参与和融合。
（5）抽象层次相关性
抽象层次相关性指VGE地理知识会伴随知识抽象程度的不同而适应不同类型范围的地理科学问题。抽象程度较高的地理知识通用性更好,能在多种地理场景间进行知识迁移。抽象程度较低的地理知识通用性较差,能解决的地理科学问题类型和领域有限。
（6）多因素相关性
多因素相关性指VGE地理知识通常涉及多种约束条件。地理现象、生态过程、自然人文相关地理科学问题的复杂性决定了VGE地理知识的复杂性,除了时空约束条件,解决特定地理科学问题往往需其他上下文约束,例如,历史背景、经济指数、地理情景、语义情景等。在同一时空上下文约束下,同一问题相关的VGE地理知识会伴随因素条件的不同而不同。
（7）不确定性
VGE地理知识的不确定性包括模糊性、不可预知性和不明确性。这种不确定性决定于地理知识的内在不稳定本质,以及问题机理过程的复杂性和因素多变性。

3.3 VGE地理知识的分类

VGE地理知识有如下2种分类方式。
（1）依据存在形式分类
根据地理知识的存在形式可分为显性地理知识和隐形地理知识。
显性VGE地理知识指可采用书面文字、图表、数学公式、结构化文档、算法程序等载体加以表达和描述的客观地理知识,如自然语言描述的气候变化规律、地理演化过程推导公式等。显性VGE地理知识是利用符号系统加以完整表述的知识,易于保存和在不同用户之间传播和共享。
隐性VGE地理知识指尚未被表述的地理知识,即未实现地理知识的形式化。隐形VGE地理知识是专业领域研究人员通过学习和实践过程产生并拥有的主观地理知识,往往参与指导专业相关研究的进行,但隐形知识难以表达、传播及共享,主要指经验知识。同一专业领域不同研究人员拥有不同的隐形地理知识,可能相类似或不一致,以主观认知为载体。通过虚拟地理环境的多维感知和协同交互等方式,内在隐形地理知识也可结合显性地理知识转化和生成能够解决特定地理科学问题的新的知识成果。
（2）依据演化阶段分类
虚拟地理环境中地理知识会按照一定的演化过程进行知识的融合创新过程。根据地理知识所在的演化过程阶段的不同,VGE地理知识可分为陈述型、推导型和结论型。
陈述型VGE地理知识：地理知识以客观事实、声明或规则等形式直接参与地学问题的计算过程,主要包括输入知识、专家经验、干预规则等。
推导型VGE地理知识：地理知识演化推导的主要过程知识,该类知识可通过陈述型地理知识的融入而实现知识的更新,主要包括面向特定地球科学问题的计算过程模型,如气候变化模拟模型、土地利用变化模型等。该类知识一旦形成,其稳定性和重用性较好,是驱动VGE地理知识创新的主导力量。
结论型VGE地理知识：地理知识经过演化推导过程后的最终结论,用于解释具体地理现象、回答特定地球科学问题或指导行为决策,如碳排放量对全球气候变暖的影响阈值,降雨量与某城市下水管道设计之间的定性或定量关系等。
在面向不同任务的知识演化过程中,VGE地理知识所处阶段可能不相同。其中,陈述型知识和结论性知识通常可互换位置,但推导型地理知识因其知识创新的主导驱动本质而相对稳定。

4 VGE地理知识工程的关键问题

VGE地理知识工程应为实现基于知识的智能虚拟地理环境系统提供必须的支撑理论、方法和技术体系。本文认为构建VGE地理知识工程需重点研究解决以下关键问题：
（1）VGE知识的表达与建模
虚拟地理环境对VGE知识表达的研究不再只是单纯的地理知识在计算机中的存储和表示问题,而是更加关注如何依据问题需求快速正确地检索、匹配、获取和使用地理知识。VGE知识的表达需了解表达语言及规范问题。VGE地理知识的建模需面向特定地理科学问题关注其中地理情景、时空情景、用户背景及多领域知识的表达与协同等。通过面向特定地理问题场景建立的VGE地理知识概念模型可用来解决一类特定上下文情景的地理科学问题。同时,VGE地理知识内在的复杂性本质决定了建模知识的难度,还需辅助知识建模的工具或平台,降低知识构造的复杂度。另外,如何表达和使用非形式化的专家知识（即对隐性VGE知识的建模）是当前知识建模研究的挑战问题。
（2）VGE知识库的构建和管理
通过构建VGE地理知识库,可实现地理知识的快速检索、获取、组织、整合、推理和共享等。VGE地理知识库的构建需首要设计元知识模式,元知识是描述VGE地理知识的元信息,元知识模式的设计直接影响地理知识库的存储、检索、推理和组织。VGE地理知识通常涉及多学科领域,如何采用统一的元知识模式描述不同学科领域相关知识是后续知识检索及推理研究的基础。虚拟地理环境中的推导型过程模型知识往往是多源、多尺度、异构知识的整合协同,如何统一描述、存储和管理是难点,需研究面向地理分析与模拟过程的通用VGE地理知识模型。VGE地理知识的检索是在构建元知识及本体关系网络的基础上,通过语法、语义、语境的匹配,实现面向地理过程问题求解的智能化查询方式。该检索需涉及本体库建立、语义自动提取和标注、语义匹配、语义推理等。VGE知识库是实现智能化虚拟地理环境系统的核心,知识库的建立和管理直接影响了虚拟地理环境的智能化程度。
（3）VGE知识的可视化方法体系
不同专业领域、不同时空跨度、不同地理情景、不同抽象层次的VGE地理知识需采用不同的可视化表达方式,如全球气候变化模式的模拟需要长达百年的气候数据基于时空维度的三维动态可视化,而洪水灾难预警决策支持则需超过报警阈值点的地理位置分布专题图。同时,不同知识背景和认知习惯的用户群体,所需的VGE地理知识、详细程度和优先顺序也不相同,即专家用户、普通用户和决策者对待同一地理现象问题的需求关注不相同。需针对VGE地理知识建立符合不同知识需求解决不同类型问题的知识可视化表达技术体系。另外,VGE地理知识的多因素相关性也对虚拟地理环境的可视化提出了需求,如何实现知识的多维可视化是虚拟地理环境可视化方法的挑战。
（4）VGE知识的协同机制
虚拟地理环境的问题求解过程通常是多学科专业知识融合、演化及协同的过程。不同地理现象或过程的研究需各种不同学科知识的参与和融合。不同领域专家的隐形地理知识和客观存在的显性地理知识都会加入虚拟地理环境的问题求解过程。如何让不同来源、不同形式、不同专业、不同类型的VGE地理知识协调有序地融合创新是提高地理知识重用性的关键。同时,虚拟地理环境技术在地理模型的异构性和专业性、数据模型的领域局限性和不兼容性、设备依赖性和分布式实时交互性等方面的问题也会直接影响VGE地理知识的协同。VGE地理知识的协同机制研究是智能化虚拟地理环境平台的主要支撑技术。

5 结论

作为新一代GIS框架方法,虚拟地理环境实现了地理过程和环境的多维动态模拟分析与表达,改变了传统的地理科学知识的表达与获取方式。利用虚拟地理环境实现地理知识快速获取和融合是缓解当前“数据丰富但知识贫乏”现象的可行方法之一。
目前,地理知识工程的相关研究仍处于初步认识阶段,理论和实践还不够系统和清晰,且绝大多数重点关注传统GIS处理和分析领域。然而,虚拟地理环境与传统GIS有显著的区别,其更强调地理过程机理模型的支撑和多感知多交互的虚拟环境表达,这使得VGE的地理知识有自身的特点。目前尚无针对虚拟地理环境中地理知识展开相关研究。本文综述了国内外现有知识工程及地理知识工程研究现状,并提出基于VGE的地理知识概念,认为基于VGE的地理知识工程是构建基于知识的智能虚拟地理环境系统所需的一套支撑理论、方法和技术体系。VGE地理知识除具备通用知识的抽象性、可重复及可再生等特点外,还具备机理过程相关性、时间跨度相关性、空间跨度相关性、多学科相关性、抽象层次相关性、多因素相关性及不确定性等典型特点。针对未来VGE知识工程的研究和建立,本文探讨和阐述了4个亟需解决的关键问题,包括VGE知识的表达与建模、VGE知识库的构建和管理、VGE知识的可视化方法体系及知识协同机制。

The authors have declared that no competing interests exist.

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Wang Z, Wang N.Knowledge sharing, innovation and firm performance[J]. Expert Systems with Applications, 2012,39(10):8899-8908.This study investigates the quantitative relationship between knowledge sharing, innovation and performance. Based on the literature review, we develop a research model positing that knowledge sharing not only have positive relationship with performance directly but also influence innovation which in turn contributes to firm performance. This model is empirically tested using data collected from 89 high technology firms in Jiangsu Province of China. It is found that both explicit and tacit knowledge sharing practices facilitate innovation and performance. Explicit knowledge sharing has more significant effects on innovation speed and financial performance while tacit knowledge sharing has more significant effects on innovation quality and operational performance.

DOI

[32]
刘佳. 高校图书馆学科知识服务模式研究[D].长春:吉林大学,2007.

[33]
王富强,王光霞,林蓉,等.空间知识服务概念辨析[J].测绘科学,2013,28(6):10-12.本文利用图情领域最新研究成果,提出了空间知识服务的基本概念, 并从服务内容、服务对象和服务提供者、服务方式等3个方面对地理信息服务和空间知识服务进行了辨析.为了实现空间知识服务,今后需要在用户知识需求建模、 地理空间知识网络构建、知识检索、知识组合和知识可视化等方面做更深入的研究.

[34]
司莉. 基于知识构建的知识服务的实现[J].图书馆论坛,2009,29(6):216-220.在分析当前知识服务系统的基础上，提出知识元标引与链接技术、主动推送的知识导航与服务技 术、支持语义的知识检索技术、知识重组与整合技术是知识服务中知识构建的核心技术。实现知识服务的关键要素在于知识服务用户的迫切需求、知识服务人员成为 内容主题专家、建设基于本体的知识库与交互功能较强的知识服务平台，还必须有行之有效的战略学习机制。知识服务系统成功运作给我们的启示主要有：功能强大 的服务平台是知识服务生命力的保证、开发融入知识创新过程的系列产品才能实现可持续发展、知识服务必须与营销战略相结合、吸收与支持用户参与的资源建设能 够使服务增值。

[35]
代印唐. 基于语义网络的知识协作关键技术的研究[D].上海:复旦大学,2009.

[36]
Nissen M E.An extended model of knowledge-flow dynamics[J]. Communications of the Association for Information Systems, 2002,16(8):251-266.ABSTRACT The modern enterprise depends upon timely and effective flows of knowledge through its organizations for success. But knowledge is not evenly distributed through the enterprise, and a dearth of information systems is available to enable such timely and effective flows. Further, the few theoretical knowledge-flow models available have not yet been developed to a point where they can effectively inform the design of information systems and business processes to support knowledge flow in the enterprise. A survey of current practice shows that such system and process design is accomplished principally by trial and error, one of the least effective approaches known. The research described in this article builds upon and extends current theory about knowledge flow. It focuses in particular on investigating flow dynamics to inform the design of information systems and business processes to enhance the flow of knowledge through the enterprise. Leveraging the good understanding of flows in other domains, we strive to extend theory that can lead to "devices" of considerable utility in the enterprise knowledge domain. The result is a four-dimensional, dynamic model that can be used to classify and visualize a diversity of knowledge-flow patterns through the enterprise. These patterns can, in turn, be analyzed to inform the design of useful information systems and business processes. The implications of this dynamic model are explored and a number of hypotheses are generated to motivate and guide future research into the phenomenology of knowledge flow.

[37]
窦万春,苏丰,蔡士杰,等.面向知识应用和交互的工作流系统建模与控制[J].计算机研究与发展,2003,40(2):342-350.针对基于知识应用和交互的复杂工作流系统,对知识应用特性进行了 分析,并构造了对应知识聚合与信息再生的过程单元.利用过程单元之间的关联分析,对面向知识应用和交互的工作流系统建模进行了深入的研究.在对一个基于知 识应用的具体实例进行分析的基础上,探讨了基于知识应用和交互的工作流系统中的控制问题,提出了一种基于区域控制和交互的控制策略,并给出了控制分解所遵 循的基本原则.最后,讨论了工作流技术的发展方向和热点技术.

[38]
Holsapple C W, Singh M.The knowledge chain model: activities for competitiveness[J]. Expert Systems with Applications, 2001,20(1):77-98.Today, there is a growing recognition by researchers and practitioners about the importance of managing knowledge as a critical source for competitive advantage. Various assertions about competitiveness through knowledge management (KM) are consistent with results of empirical studies and lessons learned on the knowledge highways and byways. In spite of these macro-level contentions and success stories, there has been little investigation of a systematic means for studying connections between KM activity and competitiveness. This paper advances a knowledge chain model that identifies and characterizes KM activities an organization can focus on to achieve competitiveness. The model is analogous to Porter's value chain and is grounded in a descriptive KM framework developed via a Delphi-study involving international KM experts. It is comprised of five primary activities that an organization's knowledge processors perform in manipulating knowledge resources, plus four secondary activities that support and guide their performance. Each activity is discussed in detail, including examples. Evidence is provided from the literature illustrating each activity's role in adding value to an organization to increase its competitiveness through improved productivity, agility, reputation, and innovation. In conclusion, we present some observations about avenues for future research to extend, test, and apply the model in business practices.

DOI

[39]
Koh J, Kim Y G.Knowledge sharing in virtual communities: An e-business perspective[J]. Expert Systems with Applications, 2004,26(2):155-166.Thanks to availability of the Internet, virtual communities are proliferating at an unprecedented rate. In-depth understanding of virtual community dynamics can help us to address critical organizational and information systems issues such as communities-of-practice, virtual collaboration, and knowledge management. In this article, we develop a virtual community activity framework, integrating community knowledge sharing activity into business activities in the form of an e-business model. We examine how the level of community knowledge sharing activity leads to virtual community outcomes and whether such community outcomes are related to loyalty toward the virtual community service provider. Based on a field survey of 77 virtual communities currently operating in <span class="interref" data-locatorType="url"><a href="/science?_ob=RedirectURL&_method=externObjLink&_locator=url&_issn=09574174&_origin=article&_zone=art_page&_plusSign=%2B&_targetURL=http%253A%252F%252Ffreechal.com" target="externObjLink" onClick="var parms = 'status=yes,location=yes,' + 'scrollbars=yes,resizable=yes,directories=yes,' + 'toolbar=yes,menubar=yes,' + 'width=400,height=600' + ',screenX=10,screenY=10';var externalWin; externalWin=window.open('','externObjLink',parms); externalWin.focus()">Freechal.com</a></span> one of Korea's largest Internet community service providers, we found that the level of community knowledge sharing activity is related to virtual community outcomes and such outcomes are significantly associated with loyalty to the virtual community service provider. These results imply that the level of community knowledge sharing activity may be a proper proxy for the state of health of a virtual community. Implications of the findings and future virtual community research directions are discussed.</p>

DOI

[40]
维明. 智能协作信息技术[M].北京:电子工业出版社,2002.

[41]
缑锦. 知识融合中若干关键技术研究[D].杭州:浙江大学,2005.

[42]
Popov B, Kiryakov A, Kirilov A, et al.KIM-semantic annotation platform[A]. In: The Semantic Web-ISWC 2003[C]. Berlin, Heidelberg: Springer, 2003:834-849.

[43]
Fogli D, Guida G.Knowledge-centered design of decision support systems for emergency management[J]. Decision Support Systems, 2013,55(1):336-347.This paper focuses on the design of decision support systems for emergency managers in charge of planning, coordinating and controlling the actions carried out to respond to a critical situation. A novel knowledge-centered design methodology is proposed and demonstrated through the application in a concrete case study in the field of pandemic flu emergency management. Knowledge-centered design is based on a rational and structured approach to the elicitation and modeling of the knowledge concerning the target environment, the application domain, the intended users, their tasks, and the specific activities that the decision support system is expected to provide. Our proposal aims at overcoming some of the limitations of user-centered and activity-centered design in the specific context of decision support systems. Knowledge-centered design is based on an iterative process that goes through four main phases, namely: target environment identification, domain understanding, user characterization, and functional analysis. The paper illustrates each phase in detail and discusses the application in the proposed case study. (C) 2013 Elsevier B.V. All rights reserved.

DOI

[44]
Ashburner M, Ball C A, Blake J A, et al.Gene Ontology: tool for the unification of biology[J]. Nature genetics, 2000,25(1):25-29.Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

DOI

[45]
赵静,庄天戈,刘红菊,等.基于语义网络方法的三维可视人中医知识库[J].上海交通大学学报,2005,39(4):517-521.一个内涵丰富的解剖图谱应该同 时包括清晰的图像模型与对应的知识库模型.针对三维可视人中医知识库的设计,从智能对象模型、语义网络结构的知识表示体系、中医学概念的显示方式和针灸临 床知识的扩充多个方面进行了研究,探讨了腧穴和相关人体解剖结构间关系的组织形式.借助智能对象网络模型的中医知识库,实现了能表现多重医学知识的三维可 视人体图谱.

DOI

[46]
Bermell-Garcia P, Verhagen W J C, Astwood S, et al. A framework for management of Knowledge-Based Engineering applications as software services: Enabling personalization and codification[J]. Advanced Engineering Informatics, 2012,26(2):219-230.Literature on Knowledge-Based Engineering (KBE) has identified challenges concerning the personalization and codification of knowledge for new product development, such as maintaining the quality, accessibility and traceability of knowledge for inspection, review and re-use, as well as managing the life-cycle of KBE applications and the knowledge contained within these applications. This paper reports on the development of a framework that realizes the management of Knowledge-Based Engineering (KBE) applications as software services, and in doing so supports the codification and personalization of knowledge that is used in performing knowledge-intensive product development tasks. The developed framework supports the elicitation and structuring of design and manufacturing knowledge, provides the capacity to run KBE applications as remote software services, and facilitates the distribution and lifecycle management of KBE applications and the underlying knowledge. A &lsquo;learning by doing&rsquo; approach is supported where knowledge can both be personalized and codified as design progresses and new insights are gained. The framework has been successfully applied in an industrial use case that considers the conceptual design of composite aircraft wing covers.

DOI

[47]
La Rocca G.Knowledge based engineering: Between AI and CAD. Review of a language based technology to support engineering design[J]. Advanced Engineering Informatics, 2012,26(2):159-179.Knowledge based engineering (KBE) is a relatively young technology with an enormous potential for engineering design applications. Unfortunately the amount of dedicated literature available to date is quite low and dispersed. This has not promoted the diffusion of KBE in the world of industry and academia, neither has it contributed to enhancing the level of understanding of its technological fundamentals. The scope of this paper is to offer a broad technological review of KBE in the attempt to fill the current information gap. The artificial intelligence roots of KBE are briefly discussed and the main differences and similarities with respect to classical knowledge based systems and modern general purpose CAD systems highlighted. The programming approach, which is a distinctive aspect of state-of-the-art KBE systems, is discussed in detail, to illustrate its effectiveness in capturing and re-using engineering knowledge to automate large portions of the design process. The evolution and trends of KBE systems are investigated and, to conclude, a list of recommendations and expectations for the KBE systems of the future is provided.

DOI

[48]
Armstrong M P.Knowledge classification and organization[A]. In: Map Generalization: Making Rules for Knowledge Representation[M]. UK: Longman Group, 1991:86-102.

[49]
Aldridge C.Geographic knowledge: Bringing geographers to their sensors[C]. Proceedings of Thirteenth Annual Colloquium of the Spatial Information Research Centre, F, 2001.

[50]
Crowther P.A visual geographic knowledge classification and its relationship to the KADS model[A]. In: Advanced Topics in Artificial Intelligence[C]. Berlin, Heidelberg: Springer. 1999:478-479.

[51]
Ester M, Kriegel H-P, Sander J.Spatial data mining: A database approach[C]. Proceedings of the Advances in spatial databases, F, 1997.

[52]
Mennis J, Peuquet D J.The role of knowledge representation in geographic knowledge discovery: A case study[J]. Transactions in GIS, 2003,7(3):371-391.ABSTRACT With the advent of massive, heterogeneous geographic datasets, data mining and knowledge discovery in databases (KDD) have become important tools in deriving meaningful information from these data. In this paper, we discuss how knowledge representation can be employed to significantly enhance the power of the know- ledge discovery process to uncover patterns and relationships. We suggest that geographic data models that support knowledge discovery must represent both observational data and derived knowledge. In addition, knowledge representation in the context of KDD must support the iterative and interactive nature of the knowledge discovery process to enable the analyst to iteratively apply, and revise the parameters of, specific analytical techniques. Our approach to knowledge rep- resentation and discovery is demonstrated through a case study that focuses on the identification and analysis of storms and other related climate phenomena embedded within a spatio-temporal data set of meteorological observations.

DOI

[53]
Miller H J, Han J.Geographic Data Mining and Knowledge Discovery[M]. Boca Raton, FL: CRC Press, 2009.

[54]
Karalopoulos A, Kokla M, Kavouras M.Comparing representations of geographic knowledge expressed as conceptual graphs[A]. In: GeoSpatial Semantics[M]. Berlin, Heidelberg: Springer, 2005:1-14.

[55]
Teller J, Lee J R, Roussey C.Ontologies for Urban Development[M]. Berlin, Heidelberg: Springer, 2007.

[56]
Mani I, Doran C, Harris D, et al.SpatialML: Annotation scheme, resources, and evaluation[J]. Language Resources and Evaluation, 2010,44(3):263-280.SpatialML is an annotation scheme for marking up references to places in natural language. It covers both named and nominal references to places, grounding them where possible with geo-coordinates, and characterizes relationships among places in terms of a region calculus. A freely available annotation editor has been developed for SpatialML, along with several annotated corpora. Inter-annotator agreement on SpatialML extents is 91.3 <i>F</i>-measure on a corpus of SpatialML-annotated ACE documents released by the Linguistic Data Consortium. Disambiguation agreement on geo-coordinates on ACE is 87.93 <i>F</i>-measure. An automatic tagger for SpatialML extents scores 86.9 F on ACE, while a disambiguator scores 93.0 F on it. Results are also presented for two other corpora. In adapting the extent tagger to new domains, merging the training data from the ACE corpus with annotated data in the new domain provides the best performance.

DOI

[57]
Golledge R G.The nature of geographic knowledge[J]. Annals of the Association of American Geographers, 2002,92(1):1-14.The nature of geographic knowledge today is very different from what it was fifty years ago. It has evolved from phenomenal (declarative) to intellectual (primed by cognitive demands). Surges of interest in systematic specialties and technical innovations in representation and analysis have changed the nature of geographic knowledge, advanced geographic vocabulary, defined and examined geographic concepts, and developed spatially explicit theories relating to human and physical environments. Explorations of interactions between these domains has generated a new interest in integrated science. This interest has produced a unique way of examining human-environment relations, and has provided the basis for a vastly different underlying knowledge structure in the discipline. But the future still challenges and significant problems face geography if it is to remain a viable academic discipline in the new information technology society.

DOI

[58]
Goodchild M F, Yuan M, Cova T J.Towards a general theory of geographic representation in GIS[J]. International Journal of Geographical Information Science, 2007,21(3):239-260.Geographic representation has become more complex through time as researchers have added new concepts, leading to apparently endless proliferation and creating a need for simplification. We show that many of these concepts can be derived from a single foundation that we term the atomic form of geographic information. The familiar concepts of continuous fields and discrete objects can be derived under suitable rules applied to the properties and values of the atomic form. Fields and objects are further integrated through the concept of phase space, and in the form of field objects. A second atomic concept is introduced, termed the geo‐dipole, and shown to provide a foundation for object fields, metamaps, and the association classes of object‐oriented data modelling. Geographic dynamics are synthesized in a three‐dimensional space defined by static or dynamic object shape, the possibility of movement, and the possibility of dynamic internal structure. The atomic form also provides a tentative argument that discrete objects and continuous fields are the only possible bases for geographic representation.

DOI

[59]
Balland P A, Suire R, Vicente J.Structural and geographical patterns of knowledge networks in emerging technological standards: evidence from the European GNSS industry[J]. Economics of Innovation and New Technology, 2013,22(1):47-72.The concentration and dispersion of innovative activities in space have been largely explained and evidenced by the nature of knowledge and the geographical extent of knowledge spillovers. One of the empirical challenges is to go beyond this by understanding how the geography of innovation is shaped by particular structural properties of knowledge networks. This paper contributes to this challenge, focusing on the particular case of global navigation satellite systems at the European level. We exploit a database of R&D collaborative projects based on the fifth and sixth European Union Framework Programs, and apply social network analysis in economic geography. We study the properties both of the network of organizations and the network of collaborative projects. We show that the nature of the knowledge involved in relationships influences the geographical and structural organizations of the technological field. The observed coexistence of a relational core/periphery structure with a geographical cluster/pipeline one is discussed in the light of the industrial and geographical dynamics of technological standards.

DOI

[60]
Couclelis H.The certainty of uncertainty: GIS and the limits of geographic knowledge[J]. Transactions in GIS, 2003,7(2):165-175.Considerable effort has been devoted over the years to fighting uncertainty in geographic information in its different manifestations. Thus far, research on handling inaccuracy, fuzziness, error and related issues has focused for the most part on problems with spatial data and their direct products, typically representations of spatial objects or fields. This paper seeks to broaden the discussion of uncertainty in the geospatial domain by shifting the focus from information to knowledge. It turns out that there is a surprising number of things that we cannot know (or questions we cannot answer) that are not the result of imperfect information. Forms of not knowing are pervasive in domains as diverse as mathematics, logic, physics, and linguistics, and are apparently irreducible. This being the case it may help to explore how these realms of ignorance may affect our efforts. The paper distinguishes three different modes or forms of geospatial knowledge production, and argues that each of them has built鈥搃n imperfections, for reasons of logical principle and not just empirical fact. While much can and needs to be done to manage and resolve uncertainties where possible, I argue for accepting that uncertainty is an intrinsic property of complex knowledge and not just a flaw that needs to be excised.

DOI

[61]
Janowicz K, Hitzler P.The Digital Earth as knowledge engine[J]. Semantic Web, 2012,3(3):213-221.The Digital Earth [13] aims at developing a digital representation of the planet. It is motivated by the need for integrating and interlinking vast geo-referenced, multi-thematic, and multi-perspective knowledge archives that cut through domain boundaries. Complex scientific questions cannot be answered from within one domain alone but span over multiple scientific disciplines. For instance, studying disease dynamics for prediction and policy making requires data and models from a diverse body of science ranging from medical science and epidemiology over geography and economics to mining the social Web. The na"ive assumption that such problems can simply be addressed by more data with a higher spatial, temporal, and thematic resolution fails as long as this more on data is not supported by more knowledge on how to combine and interpret the data. This makes semantic interoperability a core research topic of data-intensive science. While the Digital Earth vision includes processing services, it is, at its very core, a data archive and infrastructure. We propose to redefine the Digital Earth as a knowledge engine and discuss what the Semantic Web has to offer in this context and to Big Data in general.

DOI

[62]
Wu H, You L, Gui Z.Diy geospatial web service chains: Geochaining make it easy[C]. Proceedings of the ISPRS Workshop, Guilin, F 2011.

[63]
Wu H, You L, Gui Z, et al.GeoSquare: Collaborative geoprocessing models' building, execution and sharing on azure cloud[J]. Annals of GIS, 2015,21(4):287-300.Collaborative geoprocessing models have become one of the major solutions to significantly enhance the capacity to derive knowledge over a network, which are critical for the support of comprehensive analyses in a virtual geographic environment (VGE). With the emergence and growing maturity of the cloud computing infrastructure, a cloud-based platform for collaborative geoprocessing models promises to provide a pattern for the next generation of geoprocessing collaboration in the GIS realm. However, the problems with the existing collaborative geoprocessing models remain numerous, including the following: heterogeneity in description specifications hinders different geoprocessing services in collaborative work; the heterogeneity in messages mechanisms makes the cooperation among the geoprocessing services difficult and an integrated geoprocessing model framework centring on the collaborative model's lifecycle is absent. To address these problems, this article proposes a cloud-based framework for building, executing and sharing collaborative models called GeoSquare: (1) a lifecycle model was designed for convenient and flexible collaborative geoprocessing; (2) a collaboration mechanism was implemented to solve specification heterogeneity; (3) a collaboration method and its proxy were used to resolve the heterogeneity in message communication and (4) to acquire better scalability, some elastic cloud features were utilized in the framework. A GeoSquare prototype was implemented on the Microsoft Azure Cloud to demonstrate the applicability and availability. Results show that users can build, execute, publish and share collaborative geoprocessing models with high efficiency in GeoSquare. GeoSquare provides a novel collaborative geoprocessing pattern enabling further geographic research in a cloud infrastructure.

DOI

[64]
Ahearn S C, Icke I, Datta R, et al.Re-engineering the GIS&T Body of knowledge[J]. International Journal of Geographical Information Science, 2013,27(11):2227-2245.

[65]
Mekni M, Environnement E T, Moulin B.Informed virtual geographic environments for knowledge representation and reasoning in multiagent geosimulations[C]. Cognitive, 2011.

[66]
Lin H, Batty M, J Rgensen S E, et al. Virtual environments begin to embrace process-based geographic analysis[J]. Transactions in GIS, 2015,19(4):493-498.Virtual environments are computer-based digital spaces that we can observe, participate in, and experience in person. Virtual environments were initially viewed as “mirror worlds” (Gerlenter 2004), although they are being extended as this virtuality broadens to include scenarios that cannot be found in real life.

DOI

[67]
Lin H, Chen M, Lu G.Virtual geographic environment: A workspace for computer-aided geographic experiments[J]. Annals of the Association of American Geographers, 2013,103(3):465-482.A virtual geographic environment (VGE) is a type of workspace for computer-aided geographic experiments (CAGEs) and geographic analyses. By supporting geo-visualization, geo-simulation, geo-collaboration, and human participation, it provides open virtual environments that correspond to the real world to assist computer-aided geographic experiments involving both the physical and human dimensions. Based on a discussion of how VGEs can contribute to CAGEs and geographic analyses, this article proposes a clear, systematic framework for VGEs. Four subenvironments are discussed according to their different functions, pertinent issues, and corresponding solutions: (1) the data environment, (2) the modeling and simulation environment, (3) the interactive environment, and (4) the collaborative environment. Furthermore, a case on the simulation of air pollution and its analysis at different geographic scales is used to demonstrate VGEs' ability to facilitate computer-aided geographic experiments.

DOI

[68]
Chen M, Chen C, Lin H.Developing dynamic Virtual Geographic Environments (VGE) for geographic research[J]. Environmental Earth Sciences, 2015,74:6975-6980.

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