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学术论坛方案及流程英文回答：Academic Forum Blueprint and Procedure.An academic forum serves as a platform for individuals from diverse backgrounds to share their knowledge, insights, and experiences on a specific topic. It fosters an environment conducive to intellectual discourse, critical thinking, and the exchange of ideas. To ensure the smooth operation and effectiveness of an academic forum, a well-defined blueprint and procedure are essential.Phase 1: Planning and Preparation.1. Define Objectives: Establishing clear objectives for the forum is paramount. This includes identifying the specific topic or theme to be explored, as well as the desired outcomes.2. Set Agenda: Based on the objectives, a comprehensive agenda outlines the structure of the forum, including the sequence of events, allotted time slots for presentations, and opportunities for discussion.3. Identify Participants: The selection of participants is crucial, ensuring a diverse representation of perspectives and expertise relevant to the forum's topic.4. Venue and Logistics: Secure an appropriate venue and arrange logistical arrangements such as seating, audio-visual equipment, and refreshments.Phase 2: Implementation.1. Registration and Welcome: Participants register upon arrival, and an opening address welcomes them and sets the tone for the forum.2. Presentations: Scheduled speakers deliver presentations on various aspects of the topic, fostering knowledge sharing and stimulating discussion.3. Q&A Sessions: Allocated time for questions and answers ensures that participants engage actively and clarify any ambiguities or misconceptions.4. Moderated Discussions: Guided discussions facilitate the exchange of ideas, encourage diverse perspectives, and delve deeper into specific topics.5. Panel Discussions: Panelists engage in structured discussions, sharing their insights and perspectives onpre-determined questions.Phase 3: Wrap-Up and Evaluation.1. Closing Remarks: A concluding address summarizes the key points and highlights the outcomes of the forum.2. Feedback and Assessment: Participants provide feedback on the forum's effectiveness, which is crucial for future improvements.中文回答：学术论坛方案和流程。
分享他们的知识英语作文Title: Sharing Knowledge: A Gateway to Empowerment。
Knowledge is a treasure that grows when shared. In today's interconnected world, the act of sharing knowledge transcends boundaries and fosters collaboration and empowerment. Through sharing our knowledge, we not only enrich others but also contribute to our own growth and development. In this essay, we will explore thesignificance of sharing knowledge and its profound impact on individuals and society.First and foremost, sharing knowledge cultivates a culture of learning and collaboration. When individuals freely share their expertise, insights, and experiences, it creates an environment conducive to intellectual exchange and mutual growth. In academic settings, for instance, students learn from their peers through group discussions, presentations, and collaborative projects. Similarly, professionals in various fields exchange ideas and bestpractices to solve complex problems and innovate. By sharing knowledge, we harness the collective intelligence of a diverse group, leading to better solutions and outcomes.Moreover, sharing knowledge empowers individuals and communities. Access to information and expertise can break down barriers and enable people to make informed decisions about their lives. In today's digital age, online platforms and forums democratize access to knowledge, allowing individuals from all walks of life to learn new skills, pursue their interests, and connect with like-minded individuals. Whether it's tutorials on YouTube, educational podcasts, or online courses, the democratization of knowledge has the potential to uplift millions, especially those in underserved communities.Furthermore, sharing knowledge fosters innovation and progress. History is replete with examples of breakthroughs and discoveries that were made possible by the sharing of ideas and information. From the scientific revolution to the digital age, collaboration and open exchange have beencatalysts for progress. In the business world, companies that foster a culture of knowledge sharing are more agile and adaptive to change. By encouraging employees to share their expertise and insights, organizations can drive innovation, improve processes, and stay ahead of the competition.Additionally, sharing knowledge promotes social cohesion and empathy. When we share our knowledge and experiences with others, we foster understanding and empathy. By listening to different perspectives and engaging in meaningful dialogue, we break down stereotypes and build bridges across cultures and communities. In an increasingly polarized world, the act of sharing knowledge can promote tolerance, respect, and unity.However, despite its many benefits, sharing knowledgeis not without challenges. In some cases, there may be barriers to accessing or disseminating information, such as censorship, language barriers, or lack of infrastructure. Additionally, concerns about intellectual property rights and confidentiality may inhibit the free flow of knowledge.It is essential to address these challenges and create an environment that encourages openness, transparency, and trust.In conclusion, sharing knowledge is a powerful force for good in the world. It fosters learning, empowers individuals, drives innovation, and promotes social cohesion. As we navigate the complexities of the 21st century, let us embrace the ethos of sharing and collaborate to build a brighter and more inclusive future for all. Remember, knowledge is not diminished by sharing; instead, it grows and multiplies, enriching the lives of those who seek it.。
社会更需要专才的英语作文英文回答：In today's rapidly evolving society, the demand for specialized knowledge and skills has become increasingly paramount. The complexity of modern industries, advancements in technology, and the ever-changing global landscape necessitate a workforce that possesses a deep understanding of specific fields.Specialists are individuals who have dedicated years to developing expertise in a particular area. Through rigorous education, training, and practical experience, they acquire an unparalleled level of knowledge and proficiency. This specialized knowledge enables them to solve complex problems, make informed decisions, and drive innovation.In the realm of healthcare, specialists such as surgeons, cardiologists, and oncologists play a vital role in providing advanced medical care. Their expertise allowsthem to diagnose and treat complex conditions with precision and efficiency. Similarly, in engineering, specialists in fields such as mechanical, electrical, and computer engineering are essential for designing and developing cutting-edge technologies that shape our modern world.The business sector also benefits immensely from specialization. Financial analysts, data scientists, and marketing experts provide invaluable insights and guidance to organizations, enabling them to make strategic decisions and stay ahead of the competition. In the legal profession, specialists in areas such as corporate law, tax law, and criminal law ensure that clients receive sound legal advice and representation.Furthermore, specialization fosters collaboration and knowledge sharing. Specialists from different fields can come together to form interdisciplinary teams, bringing their unique perspectives and expertise to solve complex challenges. By leveraging their combined knowledge, they can develop innovative solutions that would not be possiblefor individuals working in isolation.However, it is important to recognize that specialization does not diminish the value of generalists. Generalists, who possess a broad understanding of multiple disciplines, play a crucial role in bridging the gaps between specialists and ensuring that knowledge is effectively communicated and applied across different fields.In conclusion, our society requires specialists in various fields to drive progress, solve complex problems, and meet the demands of the 21st century. While generalists remain essential for facilitating collaboration and interdisciplinary work, the increasing complexity of our world necessitates a workforce that is equipped with specialized knowledge and skills.中文回答：当今社会瞬息万变，人们对专业知识和技能的需求与日俱增。
1. artificial intelligence 人工智能2. paper-tape reader 纸带阅读器3. optical computer 光计算机4. neural network 神经网络5. instruction set 指令集6. parallel processing 并行处理7. difference engine 差分机8. versatile logical element 通用逻辑元件9. silicon substrate 硅衬底10. vacuum tube 真空管11. 数据的存储与处理the storage and handling of data12. 超大规模集成电路very large-scale integrated circuit13. 中央处理器central processing unit14. 个人计算机personal computer15. 模拟计算机analogue computer16. 数字计算机digital computer17. 通用计算机general-purpose computer18. 处理器芯片processor chip19. 操作指令operating instructions20. 输入设备input device1. artificial neural network 人工神经网络2. computer architecture 计算机体系结构3. robust computer program 健壮的计算机程序4. human-computer interface 人机接口5. knowledge representation 知识表示6. 数值分析numerical analysis7. 程序设计环境programming environment8. 数据结构data structure9. 存储和检索信息store and retrieve information10. 虚拟现实virtual reality1. data field 数据字段，数据域2. learning curve 学习曲线3. third-party solution 第三方解决方案4. Windows Media Player Windows媒体播放器5. 开始按钮Start button6. 指定输入区designated input area7. 手写体识别系统handwriting-recognition system8. 字符集character set1. function key 功能键，操作键2. voice recognition module 语音识别模块3. touch-sensitive region 触敏区4. address bus 地址总线5. flatbed scanner 平板扫描仪6. dot-matrix printer 点阵打印机（针式打印机）7. parallel connection 并行连接8. cathode ray tube 阴极射线管9. video game 电子游戏10. audio signal 音频信号11. 操作系统operating system12. 液晶显示（器）LCD (liquid crystal display)13. 喷墨打印机inkjet printer14. 数据总线data bus15. 串行连接serial connection16. 易失性存储器volatile memory17. 激光打印机laser printer18. 磁盘驱动器disk drive19. 基本输入/输出系统BIOS (Basic Input/Output System)20. 视频显示器video display1. interrupt handler 中断处理程序2. virtual memory 虚拟存储（器），虚存，虚拟内存3. context switch 上下文转换，语境转换4. main memory 主存（储器）5. bit pattern 位模式6. 外围设备peripheral device7. 进程表process table8. 时间片time slice9. 图形用户界面graphical user interface10. 海量存储器mass storage1. code generator 代码生成程序，代码发生器2. abstract machine 抽象机3. program editor 程序编辑程序，程序编辑器4. configuration item 配置项5. 计算机辅助设计CAD (computer-aided design)6. 数据冗余data redundancy7. 指挥与控制系统command and control system8. 视频压缩与解压缩video compression and decompression1. storage register 存储寄存器2. function statement 函数语句3. program statement 程序语句4. object-oriented language 面向对象语言5. assembly language 汇编语言6. intermediate language 中间语言，中级语言7. relational language 关系（型）语言8. artificial language 人工语言9. data declaration 数据声明10. SQL 结构化查询语言11. 可执行程序executable program12. 程序模块program module13. 条件语句conditional statement14. 赋值语句assignment statement15. 逻辑语言logic language16. 机器语言machine language17. 函数式语言functional language18. 程序设计语言programming language19. 运行计算机程序run a computer program20. 计算机程序员computer programmer1. native code 本机（代）码2. header file 头标文件；页眉文件3. multithreaded program 多线程程序4. Java-enabled browser 支持Java的浏览器5. malicious code 恶意代码6. 机器码machine code7. 汇编码assembly code8. 特洛伊木马程序Trojan horse9. 软件包software package10. 类层次class hierarchy1. bar chart 条形图2. frequency array 频率数组3. graphical representation 图形表示4. multidimensional array 多维数组5. 用户视图user(’s) view6. 下标形式subscript form7. 一维数组one-dimensional array8. 编程结构programming construct1. inference engine 推理机2. system call 系统调用3. compiled language 编译执行的语言4. parallel computing 并行计算5. pattern matching 模式匹配6. memory location 存储单元7. interpreter program 解释程序8. library routine 库程序，程序库例行程序9. intermediate program 中间程序，过渡程序10. source file 源文件11. 解释执行的语言interpreted language12. 设备驱动程序device driver13. 源程序source program14. 调试程序debugging program15. 目标代码object code16. 应用程序application program17. 实用程序utility program18. 逻辑程序logic program19. 墨盒ink cartridge20. 程序的存储与执行program storage and execution1. messaging model 消息接发模型2. common language runtime 通用语言运行时刻（环境）3. hierarchical namespace 分层名称空间4. development community 开发界5. CORBA公用对象请求代理（程序）体系结构6. 基本组件base component7. 元数据标记metadata tag8. 虚拟机virtual machine9. 集成开发环境IDE（integrated development environment）10. 简单对象访问协议SOAP（Simple Object Access Protocol）1. procedure call 过程调用2. fault tolerance 容错3. homogeneous system 同构系统4. autonomous agent 自主主体5. 路由算法routing algorithm6. 异构型环境heterogeneous environment7. 多址通信协议multicast protocol8. 通信链路communication(s) link1. system specification 系统规格说明2. unit testing 单位（或单元、部件）测试3. software life cycle 软件生命周期（或生存周期）4. system validation testing 系统验证测试5. evolutionary development process 演化开发过程6. simple linear model 简单线性模型7. program unit 程序单元8. throwaway prototype 抛弃式原型9. text formatting 正文格式编排，文本格式化10. system evolution 系统演变11. 系统设计范例system design paradigm12. 需求分析与定义requirements analysis and definition13. 探索式编程方法exploratory programming approach14. 系统文件编制system documentation15. 瀑布模型waterfall model16. 系统集成system integration17. 商用现成软件commercial off-the-shelf (或COTS) software18. 基于组件的软件工程component-based software engineering (CBSE)19. 软件维护工具software maintenance tool20. 软件复用software reuse1. check box 复选框，选择框，校验框2. structured design 结构化设计3. building block 积木块，构建模块，构件4. database schema 数据库模式5. radio button 单选（按）钮6. 系统建模技术system modeling technique7. 模型驱动开发model-driven development8. 数据流程图data flow diagram9. 下拉式菜单drop-down (或pull-down) menu10. 滚动条scroll bar1. procedural language 过程语言2. common design structure 通用设计结构3. class and object interaction 类与对象交互4. design constraint 设计约束5. 设计模式design pattern6. 可复用软件reusable software7. 面向对象的系统object-oriented system8. 继承层次inheritance hierarchy1. end user 最终用户，终端用户2. atomic operation 原子操作3. database administrator 数据库管理员4. relational database model 关系数据库模型5. local data 本地数据6. object-oriented database 面向对象数据库7. database management system (DBMS) 数据库管理系统8. entity-relationship model (ERM) 实体关系模型9. distributed database 分布式数据库10. flat file 平面文件11. 二维表two-dimensional table12. 数据属性data attribute13. 数据库对象database object14. 存储设备storage device15. 数据类型data type16. 数据插入与删除data insertion and deletion17. 层次数据库模型hierarchical database model18. 数据库体系结构database architecture19. 关系数据库管理系统relational database management system (RDBMS)20. 全局控制总线global control bus1. nonvolatile storage system 非易失性存储系统2. equipment malfunction 设备故障3. wound-wait protocol 受伤―等待协议4. exclusive lock 排它锁，互斥（型）锁5. database integrity 数据库完整性6. 共享锁shared lock7. 数据库实现database implementation8. 级联回滚cascading rollback9. 数据项data item10. 分时操作系统time-sharing operating system1. base class 基（本）类2. data library 数据（文件）库3. data access stack 数据存取栈4. database-specific functionality 数据库特有的功能5. 默认设置default setting6. 异常处理程序exception handler7. β测试beta testing8. 桥接提供程序bridge provider1. microwave radio 微波无线电2. digital television 数字电视3. DSL 数字用户线路4. analog transmission 模拟传输5. on-screen pointer 屏幕上的指针6. computer terminal 计算机终端7. radio telephone 无线电话8. cellular telephone 蜂窝电话，移动电话，手机9. decentralized network 分散型网络10. wire-based internal network 基于导线的内部网络，有线内部网11. 光缆fiber-optic cable12. 传真机fax machine13. 无线通信wireless communications14. 点对点通信point-to-point communications15. 调制电脉冲modulated electrical impulse16. 通信卫星communication(s) satellite17. 电报电键telegraph key18. 传输媒体transmission medium (或media)19. 无绳电话cordless telephone20. 金属导体metal conductor1. bit map 位图，位映象2. parallel port 并行端口3. direct memory access (DMA) 直接存储器存取4. universal serial bus 通用串行总线5. general-purpose register 通用寄存器6. 电路板circuit board7. 串行通信serial communication8. 数码照相机digital camera9. 存储映射输入/输出memory-mapped I/O10. 有线电视cable televisionUnit Seven/Section CI. Fill in the blanks with the information given in the text:1. Transfer2. cells3. label4. integratedII. Translate the following terms or phrases from English into Chinese and vice versa:1. high-definition TV 高清晰度电视2. frame relay 帧中继3. data rate 数据（速）率4. metropolitan area network 城域网5. 机顶盒set-top box6. 多模光纤multi-mode fiber7. 协议堆栈protocol stack8. 虚拟路径标识符VPI (virtual path identifier)1. file server 文件服务器2. carrier sense 载波检测，载波监听3. protocol suite 协议组，协议集4. peer-to-peer model 对等模型5. bus topology network 总线拓扑网络6. inter-machine cooperation 机器间合作，计算机间合作7. Ethernet protocol collection 以太网协议集8. proprietary network 专有网络9. utility package 实用软件包，公用程序包10. star network 星形网络11. 局域网local area network (LAN)12. 令牌环token ring13. 无线网络wireless network14. 封闭式网络closed network15. 环形拓扑结构ring topology16. 客户机/服务器模型client/server model17. 网络应用程序network application18. 进程间通信interprocess communication19. 打印服务器print server20. 广域网wide area network (W AN)1. routing path 路由选择通路2. dual-ring topology 双环形拓扑结构3. extended star topology 扩展星形拓扑结构4. backbone network 基干网，骨干网5. mesh topology 网格拓扑结构6. 同轴电缆coaxial cable7. 逻辑拓扑结构logical topology8. 无冲突连网环境collision-free networking environment9. 树形拓扑结构tree topology10. 目的地节点destination node1. destination address 目的地址2. performance degradation 性能退化（或降级）3. four-interface bridge 4接口网桥4. common bus 公共总线，公用总线5. 数据链路层data-link layer6. 协议转换器protocol converter7. 开放式系统互连OSI (Open Systems Interconnection)8. 物理地址physical address1. cell phone 蜂窝电话，移动电话，手机2. IP address 网际协议地址，IP地址3. autonomous system 自主系统4. dial-up connection 拨号连接5. network identifier 网络标识符6. binary notation 二进制记数法7. mnemonic name 助记名，缩写名8. Internet-wide directory system 因特网范围的目录系统9. name server 名称服务器10. Internet infrastructure 因特网基础结构11. 助记地址mnemonic address12. 网吧cyber cafe13. 宽带因特网访问broadband Internet access14. 顶级域名top-level domain (TLD)15. 因特网编址Internet addressing16. 点分十进制记数法dotted decimal notation17. 因特网服务提供商Internet service provider (ISP)18. 专用因特网连接dedicated Internet connection19. 主机地址host address20. 硬件与软件支持hardware and software support1. incoming message 来报，到来的报文2. application layer 应用层3. utility software 实用软件4. sequence number （顺）序号，序列号5. remote login capabilities 远程登录能力6. 端口号port number7. 软件例程software routine8. 传输层transport layer9. 文件传送协议FTP（File Transfer Protocol）10. 万维网浏览器Web browser1. wildcard character 通配符2. Copy command 复制命令3. search operator 搜索算符4. home page 主页5. 回车键Enter key6. 搜索引擎search engine7. 嵌入代码embedded code8. 超文本标记语言Hypertext Markup Language1. mailing list 邮件发送清单，邮件列表2. proprietary software 专有软件3. cc line 抄送行4. bcc line 密送行5. forwarded e-mail messages 转发的电子邮件6. e-mail convention 电子邮件常规7. click on an icon 点击图标8. confidential document 密件，秘密文件9. classified information 密级信息10. recovered e-mail message 恢复的电子邮件11. 常用情感符commonly used emoticon12. 已删除电子邮件deleted e-mail13. 电子系统electronic system14. 附件行Attachments line15. 版权法copyright law16. 电子邮件网规e-mail netiquette17. 信息高速公路information superhighway18. 签名文件signature file19. 电子数据表程序spreadsheet program20. 文字处理软件word processor1. web-authoring software 网络写作软件2. template generator 模版生成程序3. navigation page 导航页面4. corporate logo 公司标识5. splash page 醒目页面，过渡页6. 导航条navigation bar7. 节点页面node page8. 网站地图site map9. 可用性测试usability testing10. 图形交换格式gif（Graphics Interchange Format）Unit Ten/Section CI. Fill in the blanks with the information given in the text:1. technical2. participation3. openness; sharing4. prosumers; wikiII. Translate the following terms or phrases from English into Chinese and vice versa:1. instant messaging 即时通信，即时消息2. content syndication 内容聚合3. user-friendly interface 用户友好界面，方便用户的接口4. Weblog-publishing tool 博客（或网志）发布工具5. 应用程序编程接口application programming interface (API)6. 基于因特网的外包Internet-based outsourcing7. 软件升级software upgrade8. 版本号version number1. customized marketing strategy 定制的营销策略2. B2G transaction 企业对政府交易3. mobile telephone 移动电话4. dot-com bust 网络不景气5. smart card 智能卡，灵巧卡6. digital piracy 数字盗版7. dot-com boom 网络繁荣8. C2C transaction 消费者对消费者交易9. Web auction site 拍卖网站10. fingerprint reader 指纹读取器11. 射频识别装置radio-frequency identification (RFID) device12. 电子数据交换electronic data interchange (EDI)13. 库存管理技术inventory management technology14. 知识产权intellectual property15. 条形码bar code16. 货币兑换currency conversion17. 电子图书electronic book18. 视网膜扫描仪retina scanner19. 个人数字助理personal digital assistant (PDA)20. 企业对企业电子商务B2B electronic commerce1. software suite 软件套件2. text box 文本框3. virtual checkout counter 虚拟付款台4. static catalog 静态目录5. browser session 浏览器会话期6. 动态目录dynamic catalog7. 购物车软件shopping cart software8. 供应链supply chain9. 企业资源计划软件enterprise resource planning (ERP) software10. 税率tax rateUnit Eleven/Section CI. Fill in the blanks with the information given in the text:1. credit2. downloading; in-store3. malls4. hackersII. Translate the following terms or phrases from English into Chinese and vice versa:1. privacy policy 隐私政策2. identity theft 身份（信息）盗取3. affiliate marketing 联属网络营销4. postal money order 邮政汇票5. 零售网站retail website6. 信用卡credit card7. 货到付款cash on delivery8. 安全套接层Secure Sockets Layer1. encryption program 加密程序2. deletion command 删除命令3. authorized user 授权的用户4. backup copy 备份5. voltage surge 电压浪涌6. circuit breaker 断路器7. electronic component 电子元件（或部件）8. data-entry error 数据输入错误9. electronic break-in 电子入侵10. power line 电力线，输电线11. 检测程序detection program12. 电源power source13. 破坏性计算机程序destructive computer program14. 计算机病毒computer virus15. 软件侵权software piracy16. 硬盘驱动器hard-disk drive17. 病毒检查程序virus checker18. 主存储器primary storage19. 电子公告板electronic bulletin board20. 浪涌电压保护器surge protector1. phishing attack 网络钓鱼攻击2. graphics card 显（示）卡3. heuristic analysis 试探性分析4. infected file 被感染文件5. virus dictionary 病毒字典6. 数据捕获data capture7. 恶意软件malic ious software8. 病毒特征代码virus signature9. 防病毒软件antivirus software10. 内存驻留程序memory-resident program1. system platform 系统平台2. install back doors 安装后门3. email attachment 电子邮件的附件4. vulnerability assessment tool 脆弱性评估工具5. 网络安全措施network security measure6. 系统维护人员system maintenance personnel7. 邮件交换记录MX record8. 非法闯入系统hack a system。
如何把艺术融入农资专业英语作文The integration of art into the field of agricultural resources is a fascinating and multifaceted concept that holds immense potential for enriching both the academic and practical realms of this discipline. As the world grapples with the pressing challenges of sustainable food production, environmental conservation, and the ever-evolving needs of modern agriculture, the incorporation of artistic elements can serve as a powerful catalyst for innovation, communication, and holistic understanding.At the core of this endeavor lies the recognition that agriculture is not merely a technical pursuit but a complex interplay of natural systems, human ingenuity, and cultural expression. By embracing the creative and emotive dimensions of art, agricultural professionals can unlock new avenues for problem-solving, knowledge dissemination, and community engagement.One of the primary ways in which art can be integrated into agricultural resources is through the visual representation of data and information. The field of data visualization has long beenrecognized as a powerful tool for communicating complex scientific and technical concepts to diverse audiences. By employing artistic techniques such as infographics, interactive displays, and data-driven installations, agricultural researchers and educators can effectively convey the nuances of soil composition, crop yields, weather patterns, and other crucial data points in a captivating and accessible manner.Moreover, the integration of art can enhance the aesthetic appeal and user experience of agricultural resources, making them more engaging and memorable for learners and practitioners. The design of educational materials, such as textbooks, online courses, and interactive exhibits, can incorporate elements of graphic design, typography, and multimedia to create a visually compelling and immersive learning environment. This approach not only captures the attention of the audience but also fosters a deeper emotional connection with the subject matter, ultimately enhancing knowledge retention and fostering a greater appreciation for the complexities of agricultural systems.Beyond the realm of visual representation, the incorporation of art can also manifest in the realm of storytelling and narrative-building. Agricultural professionals can harness the power of creative writing, poetry, and even performative arts to convey the human narratives that underpin the industry. By sharing the personal experiences,challenges, and triumphs of farmers, researchers, and other stakeholders, these artistic expressions can humanize the technical aspects of agriculture, fostering empathy, understanding, and a sense of shared purpose among diverse stakeholders.Furthermore, the integration of art can serve as a bridge between agricultural practices and community engagement. Public art installations, interactive murals, and community-based projects can bring the intricacies of agricultural systems to the forefront of public consciousness, sparking conversations and inspiring collective action towards sustainable and equitable food production. These artistic interventions can also serve as platforms for cultural exchange, where traditional agricultural practices and local knowledge are celebrated and preserved, further enriching the tapestry of agricultural resources.The potential for integrating art into agricultural resources extends beyond the confines of academic and professional domains. By fostering collaborations between artists, farmers, and agricultural scientists, new avenues for cross-pollination and interdisciplinary innovation can emerge. These collaborative efforts can give rise to novel approaches to sustainable land management, innovative product design, and the development of immersive educational experiences that captivate and inspire the next generation of agricultural stewards.In conclusion, the integration of art into agricultural resources is a multifaceted and transformative endeavor that holds immense promise for the future of this vital industry. By embracing the creative and emotive dimensions of art, agricultural professionals can enhance communication, foster community engagement, and drive innovation in the pursuit of sustainable and equitable food systems. As we navigate the complexities of modern agriculture, the strategic incorporation of artistic elements can serve as a powerful tool for cultivating a deeper understanding, appreciation, and stewardship of the natural world upon which we all depend.。
翻译美学视角下的《秘密花园》两汉译本比较研究摘要《秘密花园》是20世纪美国女作家弗朗西斯﹒霍其森﹒伯内特的代表作。
它不仅是一部经典儿童著作，还具有极高的美学价值。
自1911年在美国首次出版以来，就引起国内外学者的热切关注。
迄今为止，已有许多汉语译本出版。
尽管国内外学者对《秘密花园》的研究已取得了一些成果，但对其翻译的深入﹑系统的研究却并不多见。
而从翻译美学的角度对这部作品译作的研究则更是寥寥无几。
鉴于此，本文以李文俊先生和张润芳女士的译本为基础，尝试从翻译美学的新视角对这部经典儿童著作进行一个全面﹑系统的研究。
这部小说有着浓郁的审美价值，笔者认为在这方面大有深入挖掘的空间。
本文主要依据傅仲选先生和刘宓庆先生的翻译美学理论，将其应用于《秘密花园》的原作及两汉语译本文本分析。
笔者依据这部小说中体现的美学特色，从翻译美学所涉及的形式系统美学成分（内在美学成分）和非形式系统的美学成分（外在美学成分）两个方面探讨了《秘密花园》两汉译本中美学意蕴的审美再现。
其中内在美学成分的探讨主要从语音，词汇和句法三个层次，外在美学成分的分析主要从意象和文化两个层次。
本文以充分的实例分析论证翻译美学在儿童文学翻译中的有效阐释力，比较了两个译本的得失。
并且从翻译美学的角度对翻译美学的两大审美主体—译者和译本读者进行了比较分析和探讨。
通过对《秘密花园》原作和译本的美学价值的系统对比分析，发现总体而言，两译本都较好地再现了原文的美学元素，不过李文俊先生的译本在这方面要稍胜一筹。
当然，两个译本都还存在完善的空间。
本文为《秘密花园》译作的研究提供了一个新视角。
并且通过将美学理论应用于翻译理论和实践的研究，也证明了翻译美学是一门能够用来评判翻译作品美学价值，操作性较强的理论。
翻译美学的理论不仅能够指导翻译实践，还能成为评判译作好坏的美学标准，对于翻译批评和翻译实践都有着极大的帮助。
翻译中许多美学问题都和审美标准有着一定的联系，了解这些审美标准有助于指导翻译实践，欣赏美学元素，并对译作做出美学评判。
UNIT 1Artificial intelligence：人工智能Paper-tape reader：纸袋阅读机Optical computer：光学计算机Neural network：神经网络Instruction set：指令集Parallel processing：并行处理Difference engine：差分机Versatile logical element：通用逻辑器件Silicon substrate：硅基Vacuum tube：真空管The storage and handing of data：数据的存储与处理Very large-scale integrated circuit：超大规模集成电路Central processing unit：中央处理器Personal computer：个人计算机Analogue computer：模拟计算机Digital computer：数字计算机General-purpose computer：通用计算机Processor chip：处理器芯片Operating instruction：操作指令Input device：输入设备artificial neural network 人工神经网络computer architecture 计算机体系结构robust computer program 健壮的计算机程序human-computer interface 人机界面knowledge representation 知识表示numerical analysis 数值分析programming environment 程序设计环境data structure 数据结构information storage and retrieval 存储和检索信息virtual reality 虚拟现实Web page 网页touch screen 触摸屏blade server 刀片服务器removable storage device 可移动存储设备sound card 声卡storage server 存储服务器portable media player 便携式媒体播放器hard disk 硬盘UNIT 2function key 功能键voice recognition module 语言识别模块touch-sensitive region 触感区，触摸区address bus 地址总线flatbed scanner 平板扫描仪dot-matrix printer 点阵打印机parallel connection 并行连接cathode ray tube 阴极射线管video game 电子游戏audio signal 音频信号liquid crystal display 液晶显示（器）inkjet printer 喷墨打印机data bus 数据总线serial connection 串行连接laser printer 激光打印机volatile memory 易失性存储器disk drive 磁盘驱动器BIOS(Basic Input Output System ) 基本输入/ 输出系统video display 视频显示器interrupt handle 中断处理程序virtual memory 虚拟内存context switch 上下文切换main memory 主存bit pattern 位模式Peripheral device 外围设备Process table 进程表time slice 时间片graphical user interface 图形用户界面bulk storage 海量存储器code generator 编码发生器abstract machine 抽象机program editor 程序编辑器configuration item 配置项CAD Computer Aided Design 计算机辅助设计redundant date 数据冗余command and control system 指挥与控制系统video compression and decompression 视频压缩与解压缩UNIT 3storage register 存储寄存器function statement 函数语句program statement 程序语句object-oriented language 面向对象语言assembly language 汇编语言intermediate language 中间语言relational language 关系语言artificial language 人工语言data declaration 数据声明Structured Query Language 结构化查询语言executable program 可执行程序program module 程序模块conditional statement 条件语句assignment statement 赋值语句logic language 逻辑语言machine language 机器语言Functional language 函数式语言Programming language 程序设计语言run a computer program 运行计算机程序computer programmer 计算机程序员native code 本机代码header file 页眉文件multithreaded program 多线程程序Java-enabled browser Java的浏览器malicious code 恶意代码machine code 机器码assembler code 汇编码Trojan horse 特洛伊木马程序software package 软件包class hierarchy 类层次design pattern 设计模式reusable software 可复用软件object-oriented system 面向对象的系统software bug 软件错误real time 实时inheritance hierarchy 继承层次Unit 4inference engine 推理机system call 系统调用compiled language 编译语言parallel computing 并行计算pattern matching 模式匹配memory location 内存单元interpreter program 解释程序library routine 库程序intermediate program 中间程序source file 源文件interpreted language 解释执行的语言device driver 设备驱动程序source program 源程序debugging program 调试程序object code 目标代码application program 应用程序utility program 实用程序logic program 逻辑程序ink cartridge 墨盒program storage and execution 程序的存储与执行check box 复选框structured design 结构化设计building block 构建模块database schema 数据库模式radio button 单选钮system modeling technique 系统建模技术model-driven development 模型驱动开发data flow diagram 数据流程图drop-down menu 下拉式菜单scroll bar 滚动条procedure call 过程调用fault tolerance 容错homogeneous system 同构系统autonomous agent 自主主体routing algorithm 路由算法heterogeneous environment 异构型环境multicast protocol 多址通信协议communication link 通信链路Unit 5system specification 系统说明书unit testing 单元测试software life cycle 软件生命期system validation testing 系统验证性测试evolutionary development process 锦化发展过程linear model 简单线程模型program unit 程序单元throwaway prototype 抛弃型原型text formatting 文本格式system evolution 系统演变System Design Examples 系统设计范例Requirements Analysis and Definition 需求分析与定义Exploratory Programming Methodology 探索式编程方法System documentation 系统文件编制waterfall model 瀑布模型System Integration 系统集成commercial off-the-shell software 商用现成软件components based software engineering 基于组件的软件工程software maintenance tool 软件维护工具software reuse 软件复用black box testing 黑盒测试acceptance testing 验收测试code execution path 代码执行路径test harness 测试工具，测试框架equivalence partitioning 等价划分capture / playback tools 捕获/回放工具video resolution 视频分辨率white box testing 白盒测试test script 测试脚本use case 用例procedural language 过程化语言common design structure 通用设计结构class and object interaction 类与对象交互design constraint 设计约束UNIT 6end user 终端用户atomic operation 原子操作database administrator 数据库管理员relational database model 关系数据库模型local data 本地数据object-oriented database 面向对象数据库database management system 数据库管理系统entity-relationship model 实体关系模型distributed database 分布式数据库flat file 平面文件two-dimensional table 二维表data attribute 数据属性storge device 存储设备database object 数据库对象data type 数据类型data insert and delete 数据插入与删除hierarchical database model 层次数据库模型database architecture 数据库体系结构relational database management system 关系数据库管理系统global control bus 关系控制总线nonvolatile storage system 非易失性的存储系统equipment malfunction 设备故障wound-wait protocol 受伤等待协议exclusive lock 独占锁database integrity 数据库完整性shared lock 共享锁database implementation 数据库实现cascading rollback 级联回滚data item 数据项time sharing operating system 分时操作系统data set 数据集data warehouse 数据仓库Web community 网络社区sensor network 传感器网络、感知网data mining 数据挖掘social network 社交网络。
Knowledge Representation,Sharing andRetrieval on the WebPhilippe Martin and Peter EklundDistributed System Technology Centre,Australiaphilippe.martin@.auAbstract.By“knowledge retrieval”,we refer to the automatic retrieval of statements permitting a tool to make logical inferences and answer queries precisely and cor-rectly,as opposed to retrieving documents or statements“related to”the queries. Given the ambiguity of natural language and our current inability to make computers “understand”it,the knowledge has to be manually encoded and structured using a formal graphic/textual language and ontologies(structured catalogs of categories and associated constraints of use).The Web currently contains a lot of data,more and more structured data(databases, structured documents)and simple metadata but very little knowledge as defined above, i.e.very few knowledge representations.Moreover,this knowledge has been encoded using various languages and unconnected or loosely connected ontologies,and following different representation conventions.Hence,currently,not only knowledge sources are rare but each require the development of a special wrapper for their knowledge to be interpreted and hence retrieved,combined or exploited.This article reviews various projects concerning knowledge representation,sharing and retrieval on the Web,then details requirements for a“Semantic Web”and il-lustrates them with notations,conventions and cooperation rules from our own tool, WebKB-2.Knowledge retrieval mechanisms and interfaces used in WebKB-2are also given as illustrations.Table of Content1Introduction2Elements and Landmarks of Knowledge Representation and Sharing on the Web 2.1Exchange Formats and Programming Interfaces2.2Ontologies and Knowledge Bases2.3Ontology Servers2.3Knowledge Within Web Documents3Requirements for a Viable Semantic Web3.1Need for a Standard Library of Ontological Primitives3.2Need for Expressive Notations3.3Need for High-level(and Expressive)Notations3.4Need for Lexical/Structural/Ontological Conventions3.5Need for Flexible Ways to Refer to a Category3.6Need for a Shared Natural Language Ontology3.7Need for More Centralization4Mechanisms for the Cooperatively Editing a Shared KB5Search Interfaces and Mechanisms5.1Searching Categories and Links5.2Accessing or Adding Graphs Via Generated Interfaces5.3Mechanisms for Searching Graphs6Conclusion1IntroductionData indexed by formal terms(categories)or organized by inclusion links is called“structured data”.Beliefs,definitions,facts or rules represented using a formal language and an ontology(i.e.a catalog of concepts and relations,with constraints of use and organized by semantic links)is most often called“know-ledge”(or knowledge representations/statements).The Word Wide Web cur-rently contains a lot of data,more and more structured data(on-line databases, structured documents)and simple metadata but very little knowledge1.Since the semantics of natural language sentences or structured data cannot be automatically extracted,people have to explicit semantics via knowledge representations.Then,tools may make logical inferences to answer queries pre-cisely and correctly(as opposed to retrieving data/documents“related to”the queries)and without requiring the users(or application developers)to know the exact schema(structure and/or indexation categories)used in each source of data.However,knowledge sharing,merging and retrieval are only possible if the categories used in the knowledge representations are connected by semantic links,directly(by belonging to a same ontology)or indirectly(by belonging to interconnected ontologies).A common expectation for the“Semantic Web”2is that many small,spe-cialized(and possibly competing)RDF schemas(ontologies in RDF3)will be developed,and that in order to make knowledge representations,people or businesses will select some schemas,re-use them,and create new RDF schemas to define terms they have not found[5].Then,according to Tim Berners-Lee4, future Web search engines may be able tofind various statements related to certain queries and logically combine a few of them to answer the query;“while nothing will make the combinatorial explosion go away,many real life problems can be solved using just a few(say two)steps of inference out on the Web”.This vision seems unrealistic since it is unlikely that different people will create statements that can be logically matched or combined when they use unconnected or loosely connected ontologies,when only a few ontological prim-itives are standardized(in the RDF world,these are the categories of RDFS and DAML+OIL5),and when no lexical/ontological/structural/semantic con-ventions are adopted.Like today,future Web search engines would mostly rely on term lexical matching,and applications would have to write a special wrapper for each knowledge source they want to utilize(furthermore,even wrappers 1Although describing the same facts as we do,some researchers unfortunately use the words“knowledge”instead of“data”and“ontology”instead of“knowledge”, as in[3]:“The WWW can be viewed as the largest knowledge base that has ever existed.However,its support in query answering and automated inference is very limited”.The terms we use are chosen to be quite unambiguous for a member of the knowledge acquisition/representation community,and their meanings are consistent with the ones given in the“free on-line dictionary of computing”at /foldoc/2/2001/sw/3/RDF/4/DesignIssues/Semantic.html5/2001/03/daml+oil-indexcannot compensate for badly structured or impoverished knowledge).Matching6 or combining statements,and hencefinding knowledge relevant to a query(or even only“related”to a particular object)is a problem even in a single large knowledge base(KB)such as CYC7where knowledge providers are trained knowledge engineers following conventions,using a unique large ontology and an expressive knowledge representation language.Yet,even in this ideal case, some choices in the ontological and structural conventions have led to knowledge which is not explicit enough to be exploited in many applications8.The above cited vision also seems undesirable because,as we will show,more centralized approaches involving large-scale knowledge servers can(i)permit a large number of users(people or agents)to cooperatively build large knowledge bases(KBs)with explicit,expressive,normalized,highly inter-connected state-ments and categories,and hence permit knowledge retrieval by simple hypertext navigation or provide reasoning services at selected levels of effectiveness and completeness,and(ii)exploit the KB to ease,guide and cross-check the insertion of new knowledge by each user and her re-use/annotation/correction of other users’knowledge.These features are permitted by the incremental insertion of knowledge into centralized repositories when it is developed,instead of afterwards by Web search engines(knowledge in isolation is not knowledge but merely data; hence,loosely connected schemas/RDF documents cannot be logically combined and their re-use requires the development of an ad-hoc wrapper for each one). To keep the advantages of the decentralized approach of the Web,categories and statements in a knowledge server must be referable via URLs(and then exported in a standard language such as KIF),and be allowed to refer to other objects on the Web via URLs.These are easy-to-achieve constraints.For efficiency reasons,all Web-users cannot use the same knowledge server but they can use several general knowledge servers(e.g.managed by portal com-panies)and more specialized knowledge servers dedicated to specific domains. By(partly)mirroring one another’s content,general and specialized servers would share a similar general ontology like WordNet9or CYC’s ontology,and competing specialized knowledge servers would also share some similar content10. Thus,it would not matter where a Web user publishes informationfirst,no unique server would have to be relied upon,and hence this“more centralized”approach would maintain the advantages of the current decentralized approach, 6For example,the statement“John is owner of a duplex in Southport”can easily be identified as a specialization of the query statement“a person is owner of an apartment in a city part of the Gold Coast”provided that“John”has been declared as a“person”,“duplex”as a specialization of“apartment”and“Southport”as being a“city”which is“part”of the“Gold Coast”.7/tech.html#cycl8For example,actions/processes are represented as n-ary relations instead of concept nodes with explicit thematic relations to the related objects.As shown in Section3.4, this decreases the possibility of matching or combining statements about processes. 9/˜wn/10The similarity of the KBs also permits the processes of mirroring and answering queries involving several KBs.without its problems.(A similar architecture for distributed KBs and a small-scale implementation is discussed in CYC11).In this article,wefirst review some projects about knowledge representation, sharing and retrieval on the Web.Subsequently,we show that within a KB as well as across the Web,knowledge sharing and exchange implies that knowledge providers use of a unique set of ontological primitives,follow lexical/structural/se-mantic recommendations,and use(directly or via interfaces)high-level expres-sive knowledge representation languages that ease the adoption of the recom-mendations and lead to comparable12knowledge representations.Thus,we argue that the Semantic Web implies the standardization of such elements,and show the elements adopted and implemented in our public knowledge server and annotation tool,WebKB-213.We summarize the protocols used in WebKB-2 to permit the asynchronous cooperative building of the KB by the users,and finally present search interfaces and mechanisms.2Elements and Landmarks of Knowledge Representation and Sharing on the WebRDF was not thefirst step towards knowledge sharing on the Web,and in many aspects can be seen as a backward step.The list of elements and landmarks below is organised thematically but also chronologically.It is by no means exhaustive but the selected landmark tools or ontologies continue to be the most known or usable nowadays.Readers that are familiar with thefield of knowledge sharing and the Semantic Web can restrict their attention to Subsection2.3only.2.1Exchange Formats and Programming InterfacesKIF(Knowledge Interchange Format)14is a low-level but expressive language (first order logic plus contexts and sets)originally designed in1992to per-mit translations between more specialized knowledge representation languages (KRLs).It has become the de-facto standard for expressing the semantics of KRLs in a computable way.It is complemented by the Ontolingua library15 which formalizes(in KIF)various elements necessary for expressing the seman-tics of KRLs,e.g.sets,relations,functions,numbers and frames.By comparison, 11/applications.html#dai12Category/statement comparability is an important notion in this article since know-ledge retrieval or inferencing is based on statement comparison and combination.A statement(resp.a category)is comparable to another statement(resp.category)if it generalizes it or specializes it.Logic generalizations are logic deductions.Some generalizations are simply structural simplifications and not logic deductions.This is detailed at the beginning of Section3.4.13Usable at 14/kif/dpans.html15:5915/RDF is also a low-level language but far less expressive,unsuited for logical inferences16and as an interlingua(this will be discussed in Section3).KQML(Knowledge Query and Manipulation Language)17is a KIF-based message format and message-handling protocol designed in1994to support run-time knowledge sharing among agents.It has been re-used or extended by various other agent communication languages.GFP(Generic Frame Protocol)18(1995)is a set of functions that supports a generic Application Programming Interface(API)for frame representation systems(FRSs).Various FRSs have implemented a GFP server,e.g.Loom19and SRI20.The GKB-Editor(Generic Knowledge Base Editor)21is a GFP client that permits the graphical browsing and editing of FRSs that have GFP servers.GFP was extended and replaced by OKBC(Open Knowledge Base Connectivity)22 in1998.2.2Ontologies and Knowledge BasesOntologies are catalogs of categories with their associated complete or partial formal definitions which can also be seen as“constraints of use”.Complete definitions are definitions of necessary and sufficient conditions(to be instance of the category).Partial definitions may be prototypes(listing“probable”rela-tionships),definitions of sufficient conditions,definitions of necessary conditions (e.g.“subtype of”and“instance of”links from a category to another),etc. Categories may be relation types(called“properties”in RDF;they include functional relation types or“functions”),concept types(called“classes”in RDF) and individuals(class instances that are not classes themselves).Some ontologies are about mathematical entities(e.g.sets,relations,func-tions,numbers,sequences and bags),or about relationships from/to physical dimensions(e.g.space,time and matter),or about a particular domain(e.g. elevators and chemical elements).They are often called“theories”,are generally small,and may include,generalize,specialize or compete with other theories. Since1993,the Ontolingua server23has hosted a library of such ontologies and permitted Web users to add new theories or combine theories to create knowledge bases.Some ontologies classify all the concepts of a natural language or a particular domain,via links such as“subtype of”,“instance of”and“part of”.They are often called lexical ontologies and may be large.For example,WordNet24[11]is a“lexical database for English”that was Web-accessible as early as1990,and now connects about337,200words to about109,400concept types,and organizes 16For example,see www-rdf-logic mailing list archive at/Archives/Public/www-rdf-logic/17/kqml/18/˜gfp/19/isd/LOOM/LOOM-HOME.html20/˜sipe/21/˜gkb/22/˜okbc23:5915/24/˜wnthese types via various kinds of links,e.g.specialization,exclusion,similar, member,part and substance.Some ontologies classify relation types(e.g.spatial/temporal/thematic rela-tion types)and/or very general concept types(e.g.the notions of situation,state, process,spatial entity,physical entity)mainly via“subtype of”links.They are often called top-level ontologies.Examples are John Sowa’s ontologies25(1984 and2000)and the Generalized Upper Model26(1994).Top-level ontologies may be used for structuring the lop layers of lexical ontologies.For example,Sensus[6]was created in1994by semi-automatically merging WordNet,LDOCE(the Longmann Dictionary of Contemporary En-glish)and two top-level ontologies:the Generalized Upper Model and Ontos. Similarly,in1995,we have used Sowa’sfirst top-level ontology to structure WordNet top layers and hence permit semantic checking on the use of WordNet categories[7].In1998,HPKB upper27was created by combining Sensus top-level ontology with CYC top-level ontology28.Categories of lexical ontologies may be used as generalizations for the cate-gories in theories(which are generally much more precisely defined)and hence permit the retrieval and comparison of these categories and theories.This was also a goal for our work in1995.A knowledge base(KB)is composed of one ontology(or several intercon-nected ontologies)plus additional statements using these ontologies.The classi-fication of certain statements as being inside or outside the ontology is only an implementation dependent issue.Such a distinction does not need to be made in WebKB-2.When this distinction is made in other tools,statements that involve individuals and no universal quantifier,are likely to be considered as not belonging to the ontologies.We do not make any distinction when we use the word“knowledge”.2.3Ontology ServersOntology servers can also be called KB servers but the emphasis on the ontology highlights the fact that they permit Web users to modify the ontology part of the KB,which other KB servers do not allow(this technical limitation/simplification is also why database servers do not allow the interactive modification of the database schema).The Ontolingua server29was probably thefirst ontology server(1993)and remains active.It permits the use of KIFfiles or an HTML interface.The reading and editing of each“theory”may be restricted to a group of users but, apart from locking/session mechanisms,no particular support for synchronous or asynchronous cooperation between users is provided.Ontosaurus30(1996)is also an ontology server with an HTML interface and permits each user to build or edit theories.It exploits the Loom31FRS.25/˜sowa/ontology/26http://www.darmstadt.gmd.de/publish/komet/gen-um/node1.html27See HPKB-UPPER-LEVEL-LATEST in Ontolingua28/cyc-2-1/cover.html29:5915/30/isd/ontosaurus.html31/isd/LOOM/LOOM-HOME.htmlThe Co4system32(1996)permits some asynchronous cooperation between users via protocols modeled on submission procedures for academic journals, i.e.on peer-reviewing.The result is a hierarchy of KBs,the uppermost con-taining the most consensual knowledge while the lowermost KBs are the KBs of contributing users.This approach leverages some problems of interconnecting and comparing independently developed ontologies but doubtfully scales to large numbers of users.Tadzebao and WebOnto33(1998)support some synchronous cooperation between co-temporal users(they can exchange multimedia messages and be warned of each other’s actions).As opposed to most other ontology servers,WebKB-134[8](1998)does not store knowledge onto the server disk but can load and interpret Web-accessiblefiles that combine text,images and knowledge in various formats (mainly Conceptual Graphs[12]and Formalized English35).The knowledge parts are isolated via delimiters,e.g.the XHTML tags<KR language="CG">and <KR>.(Approaches where knowledge is encoded within HTML tags or via XML tags are discussed in the next section).WebKB-1has an indexation language permitting users to index any part of any Web-accessiblefile by a knowledge statement.It also has a language of commands that permits lexical based queries, knowledge-based queries.In answer to queries,istead of knowledge statements, the document elements indexed by the knowledge statements may be displayed. Commands can be used within the documents where they can be associated to hyperlinks or combined to create scripts that can be used to solve problems36. Thus,WebKB-1is also a knowledge-based private annotation tool and a light-weight directed Web robot.WebKB-237[10](2001)inherits most of the features of WebKB-1(although its indexation and command languages are more limited)and also permits users to store knowledge into a unique KB on the server disk.As opposed to most other ontology servers,the knowledge from the various users is not stored into various loosely connected ontologies but tightly integrated into a same ontology/KB that has WordNet as backbone(thus,categories and statements are easier to retrieve,compare and re-use).Lexical problems are avoided by prefixing each category identifier with the identifier of its source(user,organization,document, ontology,...).Lexical facilities are provided by the distinction between category identifiers(which are unique)and category names(which may be shared).The cooperative building of the KB is supported via the enforcement of editing rules (presented in Section4).This type of asynchronous cooperation is likely to be more scalable in the numbers of users and knowledge quantity than Co4’s and leads to a better integration of the knowledge.WebKB-2also departs from other ontology servers by the size of the ontology it can manage.At present,WebKB-2 integrates various top-level ontologies plus the part of WordNet1.7concerning nouns(i.e.108,000nouns connected to74,500categories organized by various 32http://ksi.cpsc.ucalgary.ca/KAW/KAW96/euzenat/euzenat96b.htm33http://ksi.cpsc.ucalgary.ca:80/KAW/KAW98/domingue/35/doc/languages/36/kb/sisyphus1.htmlkinds of links).One of the few other KB systems that can manage large and dynamically modifiable ontologies is Parka-DB system38(1994).Many RDF parsers exist but we do not know of any ontology server capable of really exploiting RDF,although Corese39does convert some RDF input into simple Conceptual Graphs40(CGs)and then is able to retrieve them by looking for specializations of a query graph.RDF export is simpler to implement than RDF exploitation,but RDF exports are either restricted to very simple know-ledge or are ad-hoc,needs the use of extensions,and therefore requires a special interpretation to be re-used.Ontobroker(discussed in the next subsection)does some exports in RDF.WebKB-2can export links between categories in RDF. In[9](2000),we proposed conventions and extensions to RDF/XML for the represention of various knowledge representation cases related to the use of contexts,negation,universal quantification,collections,intervals,declarations and defite2001,we extended this work,taken into account the recent developments of the DAML+OIL top-level ontology,and showed how each of these cases could be represented in Formalized English,KIF and Conceptual Graphs and,to a certain extent,RDF/XML41.We have begun to implement the import and export of RDF/XML in WebKB-2with respect to these conventions and extensions.2.4Knowledge Within Web DocumentsSHOE42(1996)was thefirst(well known)language and server permitting the in-sertion of knowledge within HTML documents.Its claims,approach and notation were(and still are)surprisingly similar to the views and reasons of the W3C for the“Semantic Web”43(1998)and its recommended notation,RDF/XML.XML wasfirst published as a W3C recommendation in1998.The RDF model and its XML notation,RDF/XML,were designed to permit the representation and sharing of knowledge(which is difficult to do directly with XML)44.They were published as a W3C recommendation in1999.In2000,the syntax of SHOE was slightly modified to be XML-compliant.Unlike RDF/XML documents,SHOE documents can be any HTML document into which some SHOE markups have been added.However,since it is XML-based,that knowledge is difficult to read and write by people.Furthermore,the knowledge within a document is restricted to be about the object represented by the document(e.g.if afile is about a certain person,its URL becomes an identifier for that person and the knowledge lists relationships from that person to other objects).Hence,it seems there must be as many documents as individuals.Furthermore,categories cannot be declared/defined and used in the same document.Many knowledge-oriented 38/projects/plus/Parka/parka-db.html39http://www-sop.inria.fr/acacia/soft/corese.html40/˜delugach/CG/41See /doc/translations.html42/projects/plus/SHOE/43/DesignIssues/Semantic.html44/DesignIssues/RDF-XML.htmlXML-based notations(with associated parsers and sometimes inference engines) now exist,e.g.Rule-ML45,OML46,DAML47and RDF/XML.Noticing that XML-like notations force the author of a text document to duplicate the information into a difficult to write formalized version,the authors of Ontobroker[3]48(1998to2000)have opted for a tight integration to HTML: instead of introducing new tags,they ask the document authors to insert an attribute called”onto”into anchor tags and use the value to formalize the destination of the anchor.For example,<a onto="‘http://www.iiia.csic.es/~richard/‘:Researcher"></a>means that Richard(identified by his home page URL)is a researcher.If this metadata is in Richard’s home page,it may be abbreviated:<a onto="page:Researcher"></a>. Under the same conditions,<a href="http://www.iiia.csic.es/"onto="page[affiliation=href]">IIIA</a> means that Richard’s affiliation is http://www.iiia.csic.es/.Each document had to be registered to the Ontobroker server(which accessed the registered docu-ments from time to time).The ontology could not be define in the document but had to be defined in the Ontobroker server.More precisely,modifications to the ontology had to be submitted to the person authorized to modify the ontology and discussed by the people using it.To our knowledge,the ontology within On-tobroker’s web-accessible server was very small(a few dozens categories mainly about research domains and researcher/student levels).Hence,the users could simply index their research domains and professional status with the available categories.They could not actually represent the content of their research,or anything else,since they could not declare new categories.Because of these restrictions and the poor expressivity of the KRL illustrated above,Ontobroker (claimed by its authors to be the“first Semantic Web server”)was mainly used as a small database server.This is not to say that Ontobroker could not have been used as a normal ontology server since it could also parse Frame-Logics,a first-order logic frame-oriented language,but it seems that this language could only be used as a query language by Web users.To sum up,the way Ontobroker was proposed to Web users was probably the most unscalable and unusable way imaginable.As introduced above,WebKB-1(1998)and WebKB-2(2001)exploit a third way to store knowledge in HTML documents:high-level expressive and easily readable knowledge representations that are separated from the rest of the docu-ment by special delimiters.(Examples are given in Section3.2).If necessary,the representations may also be hidden by enclosing them between HTML comment tags.The user may mix category declarations/definitions and other statements, as long as a category is declared before being used.With WebKB-2,the user may also re-use the categories of the shared KB(about77,000at the beginning of 2002)via their identifiers or,when there is no ambiguity,via one of their names. Then,when satisfied with the content of the document,the user may commit it to the KB(even with high-level languages,knowledge modelling is not unlike 45http://www.dfki.uni-kl.de/ruleml/46/OML/OML-Examples.html47/48/programming:it requires various syntactic and semantic checking,corrections and sometimes re-organizations).HTML hyperlinks and some other HTML tags,especially the definition tag, can be viewed as a high-level,easily readable but poorly expressive way of encoding knowledge.For example,in WebKB-1,the following statements in Formalized English(FE),Frame-CG(FCG)and HTML were equivalent:FE:The car that has for owner John has for weight1750kg.FCG:[the car,owner:John,weight:1750kg]HTML:<dl><dt>The car<dd>owner:John<dd><dl><dt>weight<dd>1750kg</dl></dl> We have not re-used this idea in WebKB-2because of its limited interest given the possibility of using Formalized English.However,using HTML elements as a way to avoid writing(too much)RDF/XML is an idea currently explored49 by Dan Connolly(W3C).3Requirements for a Viable Semantic WebAs highlighted in the introduction,we think the vision of the Semantic Web as a collection of documents that re-use barely connected RDF schemas is not only unrealistic but undesirable.In this section,we list some elements that are necessary for the realization of the Semantic Web.3.1Need for a Standard Library of Ontological PrimitivesRDF is not a particularly expressive language even with the semantic augmenta-tions provided by the“standard”schemas RDFS and DAML+OIL.For instance, we have not found any(non ad-hoc)way to represent simple sentences like “5persons dance together”50or“51%of people are women”in RDF.Many logic-related problems with RDF can be found in the www-rdf-logic mailing list archive51.The lack of expressiveness of RDF and the absence of standard ontological primitives force knowledge providers to represent information in a biased or impoverished way or invent their own(mutually incompatible)extensions.Both cases make knowledge exploitation,sharing and re-use difficult.Many formal specification languages such as Z52come with a mathematical toolkit,i.e.functions and relations related to the building blocks for knowledge 49/2000/07/hs78/50There is no“set”class nor“size”property/relation in RDF,RDFS or DAML+OIL.There is a“cardinality”property in DAML+OIL but it is about the number of relations that instances of a certain class can have.Representing“together”is alsoa problem since there is neither a way to represent that an universally quantifiedvariable is within the scope of an existentially quantified variable,nor a special keyword to specify a“collective”interpretation for a collection(RDF only proposes the“distributive”and“cumulative”interpretations).51/Archives/Public/www-rdf-logic/52/˜mike/zrm/。
