华海的生活， 工作， 学习.

一家SNS网站的垮掉与晨兴失败的百万美元投资

　　全秋梅 胡卉

　　盈利模式至今是所有SNS网站的坎。MYSPACE的中国执行者罗川说：“盈利模式是渐进的，现在还不是讨论盈利模式的时候。”

　　盈利模式这样的共性问题，显然无法解释晨兴创投的失望与放弃

　　风险投资，向来以追逐高回报出名。但是有一家投资公司，正在放手让它的100万美元投入随风而去。

　　又一家“先行者”倒下

　　2007年2月底，UUZONE突然无法访问。虽然至今仍然没有官方正式宣布的“关站”，但因为至今无法访问，UUZONE本身以及其投资方没有作任何说明，被默认为“关站”。5月15日，UUZONE的全部员工被解散，正式“倒下了”。

　　UUZONE并不是倒下的第一家SNS(social network service，社会网络服务)网站。但是UUZONE的倒下，引发了更大的关注。

　　2005年，UUZONE最为风光的时刻，综合实力位列年度九大SNS之首，当时即号称注册用户有300万人。在关站之前，公开的注册用户数字是400万人。

　　从某种意义上说，UUZONE更像是一个探索者，在实践和试验SNS的概念。最早的UUZONE只有相册等一些基本功能，首页非常简单，无内容 的聚合。到后来，UUZONE的功能越来越齐全，除了电子邮件之外，几乎互联网所具备的服务UUZONE都做过，包括像博客、UU通、手机短信、彩信等服 务。它成了不少关注SNS研究者的研究对象，“一直对UUZONE很有感情，而老冒可以说是我第一个由于SNS、Web 2.0的话题而加入MSN的人。”网上常用身份“CNSNS”的孔铁山这样说，他对SNS的研究受业内广泛关注。老冒，是UUZONE的创始人冒志鸿。

　　UUZONE无法访问的状态延续到春节后，业内就将其默认为“关站”，网上是潮水般汹涌的帖子：UUZONE的关闭是否预示着Web 2.0末日时代的来临？

　　资本残酷？

　　UUZONE关站前，在2006年12月，创始人冒志鸿从UUZONE离职了。

　　有业内人士观点认为，他的离职，是资本与创始人较量中的又一次胜利。究竟原因是什么？还无法确知。老冒在对话《第一财经日报》的时候，反复强调，离职的原因在很长时间里不能透露，“要遵守双方合同的约定。”

　　老冒离职之后，投资方晨兴创投向UUZONE新派了接管人，但UUZONE实际上处于无人接管状态，随后在今年2月突然无法访问。

　　有曾经的UUZONE员工说，“投资了三年没有回报，而且无法看清楚的公司，没有风险投资愿意再继续。”

　　虽然最终是老冒离开了，但此前，UUZONE一直是一家老冒非常强势的企业。2003年12月，老冒在他的家乡南京创立了UUZONE。之后， 很快就达成了与晨兴创投的合作。2004年6月前后，晨兴创投决定在上海成立UUZONE的市场部，并且派出了投资方经理进行管理。曾经在UUZONE市 场部工作过的员工说：“这是希望老冒专注技术与产品，不要干涉市场运营。”但是，晨兴创投派出的市场部负责人只工作了半年左右就离开了。

　　老冒被诸多员工认为是比较独权的，要求绝对的权威，即使在面对资本的时候。UUZONE在他执行的阶段，除了他自己，没有其他核心决策人员。曾经网名是Freeman的员工，在他的博客中说：“没有健全的中层领导层，或者说除了老板，没有其他管理层。”

　　一个技术狂人与一家失控的公司

　　老冒是一个技术狂热者，离职时刻他在自己的博客中说，“我将用这段时间(离职之后)好好地休整和调养生息，陪家人旅行，教女儿英文和编程序。”他对本报记者说，“我非常喜欢被称作技术狂。”

　　业内人士认为，老冒作为技术狂热者，关注技术本身远远大于网站的运营。他在UUZONE全面的“独权”之后，市场这块一直是被轻视的软肋。

　　Freeman在他的博客中说：“从北极星到UUZONE，公司都缺乏强有力的市场部门。其实这也不能怪原来市场部门的同事，公司自身资金，技 术上的约束，造成了市场部门的无所作为，最后沦落为做页面的部门，有点悲哀。”北极星是UUZONE的前身，也是老冒创建的互联网企业。

　　五季咨询创始人洪波认为，UUZONE早期创业团队的目标不明确是一个致命伤，没有清晰的用户群定位，没有圈定该争取哪些用户，为哪些用户服务。什么都做，不见得用户什么都需要。“老冒是技术出身，网站功能上的偏好要多过商业上的考虑。”

　　CNSNS在他的博客中说，“今天，两年过去了，所有在当时的那张表里的网站(指他研究的SNS网站)似乎都并没有很大的进步，然而其中的变化 真是太大了。总有原因，各式各样的原因，有基本面的、有技术面的、也有管理面的。但我想说的是这些‘不利’的产生，其实并不在‘社会网络’这个主题，而在 于我们这些做‘社会网络’的人的问题。”

　　除了老冒外，现在比较多都是一些技术偏爱者，也就是更多是对互联网的理想主义者，在创建网站。不过，老冒不认同是技术狂们影响了网络商业化的推 进，而且他认为：“不幸的是，现在一些对技术偏爱者，对互联网的理想主义者，不是太多，而是太少了。事实上中国互联网行业充满了机会主义者，真正理想主义 者太少，而且中国目前的现实对理想主义者的回报太少，因此难以形成一个百花齐放积极创新的互联网氛围，这是中国互联网真正的缺憾。”

　　什么让投资方放手百万美元？

　　UUZONE关闭引发业内的恐慌，不仅在于它曾经的声势，还在于它结束的方式：无预警关站。曾经的数据显示要远弱于UUZONE的UUME、 YoYoNet以被其他网站收购或别的方式继续存在，但是曾经400万注册用户的UUZONE，至今仍然没有一丝要重新恢复站点的迹象。UUZONE的投 资方晨兴创投表示，无法对恢复站点的问题做回复。

　　晨兴创投没有透露投资的总额，UUZONE曾经公开的资料显示，第一期风险融资是百万美元，它的第一期风险投资正是晨兴创投。

　　400万名，不是一个小的会员数字，至少投资方可以包装一下转手。但是，UUZONE却直接关站了。曾经的UUZONE员工也很纳闷，无法理解投资方的想法。400万用户的SNS网站难道完全没有价值吗？是什么让一家风险投资下决心让百万美元的投入随风而去？

　　老冒离职时刻在他的博客中将UUZONE比作“生命体”。他说：“没有了老冒的UUZONE，希望能继续有光明的前途，有更大的超越和发展。”

　　但是，投资方好像对整个UUZONE早已经失去了希望，最后离开的UUZONE员工回忆说：“中间投资方曾经派人到过公司一趟，跟员工们分别沟通了一下各自的工作内容、状态，以及各自是什么打算。”然后投资方的人没有再出现。

　　2月，UUZONE突然无法访问，一些员工都是随后才知道。“你能理解我们的心情吗？我们的文章图片都没有了。”员工也是UUZONE的用户。但是，投资方在公司内没有作出任何的解释，只是发了通知，要求员工自行找工作，等待5月15日集中结算。

　　晨兴创投向本报强调：我们已经投资UUZONE三年了，至今看不到发展的前景。另一方面，UUZONE自身没有完成第二轮的融资。

　　UUZONE始终无法实现盈利。但是老冒说：“这是很多中国互联网业者都想了解的问题，实际上至今无解。”

　　他认为有几个原因：其一，SNS服务是属于网络规模效应的服务，这种规模效应中用户占人口百分比的重要性更大。在SNS取得相对成功的欧美，其 中互联网用户群体占国民人口的80%以上，而中国还不到10%，因此尽管很多SNS的注册绝对群体较大，但相对中国人口来说仍然微不足道；其二，中国互联 网的竞争环境，抄袭者过多、擦边球行为过多、流氓行为过多等，使得用户难以信任和忠实于任何一个服务提供商。

　　盈利模式至今是所有SNS网站的坎，即使是全球最大社区网站MYSPACE也还没有找到很好的盈利模式，MYSPACE的中国执行者罗川说：“盈利模式是渐进的，现在还不是讨论盈利模式的时候。”

　　盈利模式这样的共性问题，显然无法解释晨兴创投的失望与放弃。MYSPACE在亏损的情况下，对

　　SNS的生命力

　　让我们回到SNS最根本价值的会员本身，也许我们能找到投资方更多的心思。

　　2004年年底开始，UUZONE的用户快速攀升，“有段时间很疯狂，登录的人太多了，网站都瘫痪了。”曾经的UUZONE员工说。但是这些快 速攀升的用户，主要是被当时UUZONE推出的免费体验的UU通所吸引，一种连接网络的电话软件。“他们都是机会主义的用户，而且他们的出现，让我们忽视 了原来第一批的核心忠诚的用户。”

　　2005年是UUZONE看起来最辉煌的年头，但是在当年下半年开始，UUZONE就规定员工写博(博客)量，并且直接与奖罚挂钩。“SNS是 一个2.0，是用户生成内容的网站，但是UUZONE用户活跃度并不高，生成内容量出现了很大的困境。”上述曾经的UUZONE员工回忆。

　　除了技术上的偏爱，老冒还是一个理想主义者。他说，UUZONE不是一个适合所有互联网用户的服务，如同UUZONE的中文名字“优友地带”， 从开始就定位于“优秀朋友”这样一群年轻、教育良好的特定的人群，维持社交网络的健康、高尚、诚信的社区环境是他们一直坚守的原则。

　　但是，用户的发展轨迹偏离了老冒的最初定位。

　　5G特约评论员青熙认为，互联网更重要的是生命力。SNS网站首先是否有足够的用户体验，足够的能量来粘牢用户，低端用户是很好糊弄的，但是高端的用户，就需要一个牢固的粘连点。“SNS网站，需要有足够的信心和耐力。”

　　老冒说：“中国仍然在互联网的初级阶段，SNS属于互联网深入社会深入日常生活后才出现的服务，SNS还需要坚持一些时间才能看到前面的曙光。”

　　UUZONE的关闭，还留给我们了另一个看起来悖论的问题：SNS是2.0的标志类型网站，用户是生成内容者，用户被强调是至上的，但是这次 UUZONE的关闭毫无预警，几百万的用户曾经参与生成的数据随之不见。青熙认为：“无论如何，无预警的关闭都是对用户的一种不负责。”

　　老冒说：“数据肯定是属于用户的。可以确定的是这些数据不是消失了，随时可以恢复，但什么时候能恢复，本人无法做这样的事情。我唯一能努力的，是恳求其他人妥善处理。”

 Tag:学习     
甘永成1， 祝智庭2
（1．加拿大多伦多大学安大略教育学院，多伦多 M5S
1V6；2．华东师范大学教育信息网络中心，上海 200062）

内容提要：本文侧重从系统的整体性、智能的整体性和动态性、学习模式、知识管理这四个维度，来构建虚拟学习社区知识建构和集体智慧发展的学习构架。根据建构主义-、情境学习理论，在Internet上建立虚拟学习社区，就在于学习社区可以应用学习者的个体智慧，发挥集体智慧的优势，共同解决学习中遇到的难题，通过个体间-长期的互动、合作和知识建构，达到集体智慧的凝聚和升华。
关键词:
集体智慧，知识建构，虚拟学习社区，多元智能，e-Learning，知识管理，学习型组织

一、引言
集体智慧是集体在创造、创新和发明上共同合作的一种能力。当这个社会越来越依赖知识时，这种集体能力就变得越来越重要。集体智慧已经成为知识社会中竞争、创造和-发展的决定因素

虚拟学习社区是由具有共同兴趣及学习目的人们组成的学习团体在Internet上构建的虚拟学习环境。他们利用多种网络通讯工具，通过相互的交流、互动、讨论和-协作，以及通过协作学习等多种学习方式，共享彼此的观点、思想、资源、知识、学习经验和集体智慧，从而促进知识建构和个体智慧的发展，达到学习的目的和促进自身-学习能力的发展。

图1  支持集体智慧的四层架构
我们可以从信息通讯技术、e-Learning、知识管理、学习社区这四层架构来说明集体智慧发展的技术支持（见图1）。（1）信息通讯技术（ICT）。ICT-和Internet网络的迅猛发展为信息、知识的广泛传播提供了可靠的技术保障和底层通讯协议架构。"每一个新思想都被并入了下一个思想，从而产生一种对人类本-质和我们所生活的宇宙的积累性的新认识，这种新的智慧模式则由全世界新发展出来的网络所分享
。"（2）e-Learning。朝着数字化、网络化、多媒化和智能化的方向迈进的信息化教育，正引起一场学习方式的重大变革。 e-Learning使用网络技-术来设计、传输、选择、管理和扩展学习，将成为学习的主要方式之一和终身学习的主要平台。（3）知识管理。知识管理作为获取、存储、传播、应用知识的一种管理方-式，通过知识共享、运用集体智慧提高应变和创新能力。知识管理在知识时代具有举足轻重的作用。（4）学习社区。虚拟学习社区为学习者享有他们所需的知识提供一个-开放的学习平台，并能够对这些知识获取、分类、存储、共享。学习者在这样的学习环境中交流信息、探讨问题、提出新观念、拓展问题思路、交流学习心得、相互争论、-达成共识，进行知识建构，不仅学习到了知识，而且获得了学习能力，提高了多种智能，促进了团队合作精神，提升了集体智慧。
我们将虚拟学习社区中的集体智慧理解为：在学习过程中，学习小组或集体协力加强整体性与相互联系，以便加深我们对事物的理解，使个体间的智慧达到进一步的凝聚，-进而达到更高层次的整体性和密切联系，形成共同创造的能力。本文拟从整体性、智能、学习和知识管理这四个维度，来构建虚拟学习社区集体智慧发展的学习构架（见图-2）。
二、原理分析及讨论
（一）整体性维度
1．共性和差异性
整体性是系统最普遍和最本质的属性，共性和差异性是整体性的两个重要属性。共性是系统要素共同的、普遍的属性。这种共同、普遍的属性使各个系统要素相互联结和贯-通，形成统一的有机整体。系统要素的差异性是指它的个体性、独特性，以及系统要素与要素之间的差异性。

图2  集体智慧发展的学习框架
小组、组织、社区乃至社会是一个有机的整体。为了分析组织内成员的性质，两个最重要的视角是共性和差异性。因为只有共同的兴趣和目的，人们才能组织起来形成一个-整体，而每个成员在个性、学习风格、能力、智力水平等方面又都存在很大的差异性。正如霍华德·加德纳所言："多元智能观点的核心--不管是理论上和实践上--都-在于认真地看待人的个性差异
。"
首都师范大学王陆教授等学者，专门对学习者个性因素和学习类型与远程教学效果进行了研究，并从个性因素的五个方面（有恒性、实验性、创造能力、在新环境中的成长-能力和性别）分别进行了对比试验。他们的结论显示，学习者的个性因素在很大程度上影响网络教学效果，应该根据学习者个性因素提供不同的网络教学模式
。
2．系统论视角
系统论主张从整体性、关联性、动态性、层次结构性和自组织性等观点出发，研究系统与系统、系统与组成部分、以及系统与环境之间的普遍联系。虚拟学习社区是按照一-定的学习目的组织起来的，具有相应的组成要素、结构和特点。下面从四个方面分析其主要特点（见图3）。

图3  系统论视角
（1）层次结构性/多变性。学习者的成长环境、人生经历、文化背景不尽相同，在个性特征、学习风格、学习方法上也各有特点，知识水平、认知能力有高有低，因此，-整个学习社区的学习个体具有差异性和多变性。这就好像一块未被磁化的磁铁，其磁极子是"各向异性"的。
（2）关联性/协同性。学习者之间是相互关联、相互影响的。如果不加以适当的控制，虚拟学习社区的学习环境就会遭到破坏。重要的一点就是要利用学习者在学习目的 -、学习兴趣、学习承诺方面的共性，使之产生协同作用，即产生"正相关"作用而不是"负相关"作用，使各种因素朝着学习知识、增进学习绩效、提高认知水平、提升集-体智慧的方向发展。协同性就像磁化后的磁极子产生"各向同性"的作用。
（3）交互性/动态性。合作学习是学习者之间互教互学、彼此之间交流信息的过程，也是互爱互助、情感交流、心理沟通的过程。合作中的学习活动的任务分担与成果共-享，相互交流与相互评价，使学习者能体验到一种被他人接受、信任和认同的情感。通过相互启发、激励，发展认知能力，会极大地促进个人的合作能力与团队精神的形成-。
（4）系统效应/整体效应。系统的整体功能大于内部各元素孤立功能之和。虚拟学习社区里的智慧凝聚现象正是"系统效应"的一种体现。学习者既相互独立，又相互依-赖、相互制约，有机地组成了一个学习的集体。当然，学习社区并不一定产生"正的系统效应"，也可能产生"负的系统效应"。要取得成功，就要采取一定的方法和手段-，促进 "系统效应"向正的方向发展，努力促进虚拟学习社区集体智慧的显现和结晶。
（二）智能维度
从个体智能到集体智慧是一个连续统。从智能整体性和动态性两个维度考察，它包括多元智能、个体共性智能、合作智能和集体智能四个上升的层次，以及自我组织（发散-）、相互连接（收敛）、智慧结晶（凝聚）和共同创造（创新）四种状态。
1．智慧连续统
从个体到集体、智能到智慧发展过程来看，集体智慧可分为四个维度（见图4）。（1）多元智能。霍华德·加德纳把个体所具有的智能分为语言、数理逻辑、视觉空间、-肢体运动、音乐、人际沟通、自我认识、自然观察者智能。这是一种应用我们个体智能谱系的能力。每个个体都具有由潜在的或活跃的多元智能组成的品质，显现为不同程-度的个人影像。（2）合作智能。在学习和工作过程中，与他人和集体进行有效的合作，最大限度地发挥团队的能力。合作智能是我们与所处的环境和环境的关系中产生协-同的能力。（3）集体智能。小组、团队、组织、社区、以致整个社会学习，解决问题，计划未来，理解和适应内部环境和外部世界的能力。（4）集体智慧。一个系统（-个人、小组、组织、社区、社会）有效地集成和利用参与者不同才智的能力，有效地学习、工作、解决问题的能力，使之像一个有机运转的整体。集体智慧是各种智能的凝-聚，可形成深远的洞察力和远见卓识。

图4  集体智慧连续统
从个体智能到集体智慧，这是一个由个体、小组/团队、组织、社区到社会，由低级到高级、由弱小到强大的发展过程，也是一个动态螺旋向上的发展过程。
2．智能整体性视角
集体智慧是一个系统现象。从个体智能到集体智能是一个连续统。我们可以从多元智能、个体共性智能、合作智能和集体智能这几个上升的层次来分析虚拟学习社区的智能-形态（见图5）。

图5  智能整体性视角
（1）多元智能。每个学习者在某种程度上都具备多元智能，只是表现的形式和发挥的程度不同；他们又都具有各自的优势智能和弱势智能、个性特征和学习风格。个体对-同一个事物、同一个问题的认知角度不同，观念不同，呈现多样化的趋势，这正是个体产生创造性的心理特征依据所在。
（2）个体共性智能。个体的每种智能具有相对独立性，但各种相对独立的智能是以不同的方式和程度有机地组合在一起的。从每个个体的共性来看，有两个共性部分：（-1）智能起点大致相同。虽然个体之间的智能呈现高低不同的水平，但组成一个学习社区的学习者在综合智能水平上是大体相当的。（2）认知结果大致相似。通过学习者-个体的努力和集体间的合作，在学习单元结束之后，学习者的智能都得到了相应的提高，达到了某种学科大纲所要求的目标，对学习的知识达到了大致共同的认知。
（3）合作智能。学习者遇到的每一个难题也是一个机会，它需要彼此合作来认识这种难题和提高其智能。合作的一个挑战是创造性地运用个体的差异性，因为个体之间的-差异性对学习者既有正面的积极作用，也有负面的消极作用。合作智能就是要运用各自的差异性创造合力，达到共同解决问题的目的。
（4）集体智能。集体智能表达了两种智能的联结，一是肯定了个体智能的独立性；二是肯定了个体智能在观察复杂情境时的交互性。由于存在"针对某一场景的不止一种-角度"的"复眼式观察"，因而允许我们从许多角度综合一种强烈的视觉感知，使我们把镶嵌某一对象的环境连接起来，从而使意义充满张力。于是，复眼式观察或思维展-示了比任何个体观察更宽阔和更逼真的视野
。集体智能是分布式个体智能的有效组合，是个体智能的合作和凝聚，从而使整体智能得以加强和提高。
3．智能动态性视角
集体智慧的螺旋上升的周期可以分为四个阶段：发散、收敛、凝聚和创新。整体中个体智能之间的相互作用可分为四种状态：自我组织、相互连接、智慧结晶、共同创造（-见图6）。

图6  智能动态性视角
（1）自我组织--发散。每个学习者都是一个独立的学习个体，都有自己的学习计划、学习方法和学习风格。当遇到学习难题时，他可以在虚拟学习社区里发表自己的问-题，向同伴请求帮助。他们每个人看问题的方法、视角、思路、层次和观点都可能不一样，可能用不同的方法对问题进行求解，从而产生多种结果和方案，这是一个思维发-散的过程。
（2）相互连接/依赖--收敛。学习者可以对同一问题，发表不同的观点，开展热烈的讨论，或反驳某一观点，指出其中的不足或错误；或赞同某一观点，提出改进意见-。这是学习者之间相互影响、相互联系和交互的过程。指导者在其中起着重要的作用。第一，提出具有启发性的意见和思路，积极引导学习者更深入的思考；第二，引导讨-论的气氛和议程；第三，引导讨论问题的方向。使问题的解答向收敛的方向发展，达成共识。这也就是问题求解的逐步收敛的过程。
(3 )
智慧结晶--凝聚。学习者对问题多次讨论、争论和挑战的过程，是学习者调动自己的知识和智能、积极主动思考的过程；也是学习者之间相互交流、沟通、反馈和反思的-过程；同时也是学习者理解能力逐步形成、认知水平逐步提高并最终形成共识的过程。这里的共识有两层意思：第一，对良构问题，学习者最终达成一致的求解方案（可能-不止一种解答）；第二，对劣构问题，常常没有唯一的答案。这里的共识是一种趋同，通过对问题逐步求同存异的过程，达到对问题的核心的相近理解和把握
。这是一种对事物的理解和洞悉能力的"共同"提升。
（4）共同创造--创新。学习者不仅要学习知识，更应该培养创新意识、创新思维和创新能力。学习者不能仅仅满足寻求问题的答案，还要获取更高层次的思维能力，即 -在已有的问题基础上，进行发散性和联想性思考，提出新问题、新观念。创造性地利用学习者个体的差异性，进行"头脑风暴"式的思考，这是学习社区共同创造的一大特-征。
（三）学习维度
从传统的学校教育到e-Learning也是一个连续统，实际上大部分的学习是混和式学习（Blended
Learning）。网络教育的学习模式主要有两种：一是个别化自主学习，另一种是协作学习。虚拟学习社区正是实施这两种学习方式的最佳途径（见图7）。

图7  学习模式维度
1．个别化自主学习
个别化学习是指根据学生的不同特征而因材施教，通过网络提供学习环境、学习方式、学习内容的多重选择，为每个学生提供最佳的教学支持，让学生自由选择学习内容，-制定学习计划，安排学习时间、地点，从而自我获取知识，更新知识
。它打破了传统的学习群体的结构，学习者作为独特个体，拥有了比传统教育中更多的个性化色彩和更多的个别化学习机会，能最大限度的满足不同个体的需求，使自我规-划、自我调整的独立学习变得更容易。
2．协作性学习
协作学习是指利用计算机网络建立协作学习的环境，通过小组或团队的形式组织学生学习，使教师与学生、学生与学生在讨论、协作与交流的基础上进行协作学习
。它有利于培养学生形成良好的学习态度、协作精神和人际关系，培养认知领域的某些高层次技能。
协作学习的理论来源主要有两个：（1）心理学家维果茨基的社会学习理论。根据维果茨基的观点，学生在协作交互活动中获得的能力，能够被内化而变成他们的独立发展-成果。（2）建构主义学习理论。建构主义认为，知识不是通过教师直接传授得到的，而是学习者在一定的情境下，借助其他人（教师和学习伙伴）的帮助，利用必要的学-习资源，通过意义建构的方式获得的。"情境"、"协作"、"会话"和"意义建构"是学习环境中的四大要素。协作学习把同学和教师当成一种学习资源和环境，通过协-作和互动获取知识，是建构主义学习理论的一种体现。要使协作性学习取得成功，使学生学习效果最大化，必须考虑积极的相互依赖、促进性的互动、个体职责、社交技能-、小组自加工这五大基本要素
。
（四）知识管理维度
1．知识管理宏观视角
学习的目的就是要获取知识，进而将知识转变成人生的智慧。我们可以从个人知识管理、学习型组织、组织记忆和社区文化四个方面来考察（见图8）。
（1）个人知识管理。个人的学习和工作，对知识的收集、整理、存储、应用与创新，应从知识管理的高度，结合个人的智能开发、专业发展、人生规划，不断增长自己的 -知识和智慧。教育的目的不仅是要获取知识，更重要的是促进学习能力的提高，个人智慧的增长。正如1980年诺贝尔化学奖得主保罗·博格在谈到教育对他的影响时所-说："教育对一生最大的贡献是帮助你发展好奇心和培养你寻找有创造性答案的直觉。随着时间的流逝，我们了解的许多事实都会被遗忘，但我们发现问题和解决问题的能-力却永远不会消逝
。"这就是知识与智慧之间关系的最好说明与解读。
（2）学习型组织/社区。学习型组织是一个"不断创新、进步的组织，在其中，大家得以不断突破自己的能力上限，创造真心向往的结果，培养全新、前瞻而开阔的思考-方式，全力实现共同的抱负，以及不断一起学习如何共同学习
。"
彼得·圣吉关于学习型组织的"五项修炼"，为学习社区/实践社区提供了强大的理论基础和方法论指导。要将学习社区各阶段呈现的知识联系起来，把智慧凝聚起来，需-要指导者、教师进行系统思考，运用知识管理的手段，促成知识的发现、传播、存储、应用、共享和创新，并逐步培养学习者的学习能力、思维能力和创造力。

图8  知识管理的宏观视角
（3）组织/社区文化。虚拟社区之所以能将成员培养成稳定的、忠实的用户，其最大特点就是由共同命运感、归属感及自我价值的实现构成的文化维系力。如果不注意社-区文化的培育，学习社区同样会失败。比如，有的学习者不愿意将自己的知识与大家共享、不愿意将自己的知识进行总结后提交到知识库中去，还有一些人甚至不习惯到知-识管理系统中去获取知识等等。所以，必须十分重视变革管理，促进学习社区文化创新和学习者的观念转变。
（4）组织/社区记忆。组织记忆是指在组织中建立知识库，以储存组织所累积的方法知识及其他知识资产，并促使这些知识资产增进知识密集的工作历程之效能与效率
。组织/社区记忆是建立学习社区的重要阶段，需要将不同来源的知识分类、整理、提炼并加以存储，将分散知识提升为组织/社区记忆。
2．知识管理微观视角
知识管理主要是通过知识的分享而达到价值倍增效应的，而虚拟社区显然是实现知识共享的最直接方法
。知识管理的典型应用就是实践社区或知识社区
。研究虚拟学习社区里的知识转化，是以个体知识与集体知识的转化为对象的（见图9）。
（1）外化。学习者针对大家共同有兴趣的课题，分享自己特殊的经验、感受和观点，让参与讨论的成员将其个人的隐性知识表达出来，促使成员在学习社区的互动中，产 -生创新的观念。在个人知识"外化"过程中，隐性知识会通过隐喻、模拟、图表或观念的方式表达出来，经过模式化后就形成"观念性知识"。
（2）综合。学习者经过多次交流和互动后，其知识是零碎的、散乱的。如果不加以归纳、整理、综合，也就达不到知识共享、知识转化的目的。综合的过程就是把已获得 -的信息和知识进行排序、增减、分类、综合，存入知识数据库系统，并可以通过多种途径检索，随时随地查找所需的信息。同时，这个过程能够产生新的、更加系统化的知-识。

图9  知识管理的微观视角
（3）内化。内化的过程实质上是一个学习的过程。当通过社会化、外化、综合获得的集体知识，被内化成个人的隐性知识，并形成一种共享的心智模式和技术诀窍的时候-，它们才会变成有价值的知识。学习者个人是通过内化过程不断积累和丰富自己的知识的。
（4）社会化。社会化是一个共同分享各人的经验，转而创造新的隐性知识（增值），如共享心智模式、技能和诀窍的过程。
上述四种知识转化模式是前后连续、螺旋上升的。一方面，学习共同体通过充分的对话和共同的探索，将公共知识转化为个人知识；另一方面，将个人问题转化为公共问题-，并借助公共知识解决个人困境，这构成一种加速个人学习和创新的良性循环。
三、总结
从系统整体性、智能整体性和动态性、学习模式、知识管理这四个维度，来构建虚拟学习社区知识建构和集体智慧发展的学习构架，是必要和可行的途径：（1）整体性维-度。共性和差异性则是整体性的两个重要属性。共性是系统要素共同的、普遍的属性，差异性是系统要素的个体性、独特性与要素之间的差异性。从系统论视角（层次结构-性/多变性、关联性/协同性、交互性/动态性、系统/整体效应）研究虚拟学习社区，就是要使其诸要素产生"正的系统效应"，努力促进其集体智慧的显现和结晶。（-2）智能维度。从个体智能到集体智能是一个连续统。我们可以从多元智能、个体共性智能、合作智能和集体智能这几个上升的层次来分析虚拟学习社区的智能形态。而智-能动态性则显示集体智慧的螺旋上升周期可以分为四个阶段：即发散、收敛、凝聚和创新，相对于整体中个体智能的相互作用便显示为四种状态：即自我组织--发散、相-互连接--收敛、智慧结晶--凝聚、共同创造--创新。（3）学习维度。网络教育的学习模式主要有两种：一是个别化自主学习，另一种是协作学习。虚拟学习社区正-是实施这两种学习方式的最佳途径。（4）知识管理维度。本文从知识管理的宏观视角（个人知识管理、学习型组织、组织记忆和社区文化）考察了虚拟学习社区中的学习-技术，从知识管理的微观视角（外化、综合、内化、社会化）考察了虚拟学习社区中的知识转化过程。

A Learning Framework for Knowledge Building and Collective Wisdom
Advancement in Virtual Learning Communities

Yongcheng Gan1, Zhiting Zhu2

作者：陈禹

　　“社区”一词在英文中是community，费孝通教授译为“社区”。人是社会性的动物，人总是要通过各种各样的联系或关系，结成各种不同类型的群体，例如血缘和亲属关系、居住区域的远近、相同的兴趣爱好、相同的职业、相同的观点等等，这就是各种社区的由来。

　　在历史上，随着人和人交往方式的发展和进化，人们的联系范围越来越大。据研究，在类人猿的群体中，个体之间的交流是需要通过直接的接触和抚摸实现的。这就限制了群体的规模，因为能够直接地、物理地接触的个体是很有限的。语言的出现，使得人能够“振臂一呼，应者云集”，迅速地和许多人交流，群体或者社区的规模也随之扩大了许多。文字的出现，则给了文明的发展以更大的推动力，我们至今可以通过前人的著作与几千年前的圣哲心灵相通。

　　现在，我们又正处在一个新的台阶上。现代信息技术，特别是Internet的迅速发展和普及，使人类社会的交流能力再一次得到了前所未有的提升。遥远的地理分割，地球两边的时差，甚至语言文字的差别都已经不再成为人们交流和通信的障碍。任何人，不管他在地球的哪一个偏远的角落，原则上都可以根据自己的兴趣和要求，在几乎可以忽略不计的时间内，在网上找到和自己兴趣一致的“虚拟社区”，找到志同道合的伙伴，进行所希望的讨论和交流。一句话，信息时代为人类社会的社区的发展，开辟了前所未有的发展空间和潜在可能性。这将给人类社会带来的深远影响，今天还只是初现端倪。

　　“知识社区”就是这种发展趋势的一个非常值得注意的产物。就个人的理解和认识而言，所谓知识社区，就是在现代信息技术的支持下，以知识的创造和传播为目标的、现实的载体和虚拟的联系相结合的、具有空前灵活性和创造力的一种新型的、科学人的社区。在这个新的名称下，既包括在传统机构的基础上发展起来的数字图书馆、数字博物馆、虚拟实验室等，也包括在网络上发展起来的各种论坛、讨论室、博客、WIKI等。这个新的名称，既概括了在这些名称下物理上分散、逻辑上集中的海量信息资源，又是指通过这些形式集中起来的、具有共同兴趣的人群。

　　这样的讨论可以分为两个角度。一方面是技术的角度。知识社区是建立在大量新技术的基础之上的，没有这样的技术，各种类型的知识社区是不可能建立起来，更不可能维持和发展起来的。因此，需要对于支持知识社区的有关技术给予充分的重视和关注，这包括从存储技术、处理技术、通信技术、检索技术到显示和加工等一系列先进技术，还包括系统规划、系统设计、系统管理等有关的软技术在内。这些技术对于实现知识社区的理念都是必不可少的。

　　另一方面是社会文化发展的角度。知识社区作为一种新的社会组织形态，它在全社会的科学和教育中应当占据什么样的位置，发挥什么样的作用；它对于推动科学和技术的创新应当如何发挥作用；它对于新型人才的培养应当如何发挥作用，包括对于在校学生的学习和在职人员的培训；作为一种以前从来没有过的新生事物，对于知识社区应当如何实现有效管理和积极引导，特别是对于青少年，如何防止和化解不健康的、消极的因素。此外，有关知识产权的保护、网络安全的保障、网上的诚信环境的建设等问题也是与知识社区的发展有密切关系的。从更深的层次来看，知识社区的发展必将逐步和现实的社区建设结合起来，和各级各类学校的改造和建设结合起来，为建设高度文明、高度民主的、新型的和谐社会发挥积极的作用。

　　导语：北京时间3月26日，Web2.0已经逐渐成为全球互联网用户日常生活中密不可分的一部分，那么它的未来又会怎样呢？它将于什么时候被更新鲜的事物所取代？著名Web2.0专家内特·托金顿(Nat Torkington)近日给出了答案。

　　托金顿认为，Web2.0的发展轨迹如下：

　　2004年：Web2.0横空出世并获得命名。

　　2006年：互联网用户成为《时代周刊》年度人物，表明Web2.0获得广泛认可。

　　2007年：Web2.0发展至当前阶段。

　　2008年：Firefox 3.14159发布，这一版本加入了Ajax网络应用离线支持。也许一些人会将之称为“Web3.0”，但这一名词早已用来描述语义网络。因此，博客圈将称之为“Web2.86”，而媒体最终称之为“Web286”。

　　2009年：语义网络研究人员开发出一款演绎计算器，可以解决网络中任意采用数学知识编码的问题。学生将广泛采用这一计算器，用于完成自己的家庭作业。不过，这一计算器要求待解决问题必须以TeX标示语言表达。种种迹象表明，Web3.0即将到来。

　　2009年：维基引发的热潮促使Firefox 4同本地

操作系统桌面整合在一起，从而提供了一个全新的跨平台维基环境。开发人员继续打造语义网络，“Web3.0”这一名词仍然为他们保留。不过，博客将跳过Web3.0，直接进入Web3.1阶段。

　　2010年：语义网络开发者发布一个新的XML格式，它标志着Web3.0最终完成。

　　2010年：由于Ajax、Flex和其它网络开发者所打造的用户界面更加复杂，标准化的呼声越来越高。W3C无力完成这一工作，但主要 Ajax工具包中的通用API和维基将率先出现在Firefox 5之中，IE 12于几个月后跟进。由于这一阶段完全符合蒂姆-奥雷利(Tim O'Reilly)对于互联网操作系统的构想，他可能会称之为“Web 95”。

　　2011年：语义网络研究人员将推出一款游戏，从而促使更多人为一个RDF数据商店“添砖加瓦”。这一数据商店将成为Web3.0的入口。

　　2012年：一直秘密开发的Mozilla瘦客户端系统正式推出，它是一款基于Linux的完整可启动平台。开发人员将通过开发两个版本来解决 GNOME和KDE之争，但这样的做法最初可能会给瘦客户端的普及带来阻碍。最终，这两个项目将会整合到一起，否则将错失击败Windows的最佳时机 (2011年有望成为“Linux桌面年”)。基于GNODE的Firefox 6将横扫整个世界，这段时期将被称为“Web 98”。

　　2013年：Firefox 6将占据绝大部分市场，但随后会成为恶意代码的主要目标。Mozilla开发团队将致力于修复漏洞和加入新功能，艰难地开发Firefox 7。他们最终可能会收购Opera，并发布基于OpenSolaris操作系统的企业版Firefox。网络市场的分裂可能会引发混乱，因此微软已于 2012年转型为单一的服务公司，而IE 13成为绝唱。Mozilla将承诺发布一款名为“No Trouble”的网络盈利工具，但最终无疾而终。这段时期将被称为“Web NT”。

　　2013年：语义网络开发人员将推出一个面向Java 6企业版的全新FDF数据库。各方面共同庆祝Web3.0的到来。

　　2015年：Mozilla将终止Opera产品线，并将其剩余功能整合到Firefox之中。为了赢回客户的信任，他们将在设计和用户界面方面投入巨资。最终他们将更加注重用户体验，而这段时期也被称为“Web XP”。

　　2020年：经过多年的开发，以及同恶意软件对抗，Mozilla最终大幅削减了Firefox 7的功能。他们将跳过Firefox 7，直接发布Firefox X。Firefox X将支持面向博客、即时信息和新通信系统的RSS。这一新通信系统名为“Crack”，它可以让人上瘾，从而将大大促进Firefox X销量的增加(由于Mozilla为开发Firefox 7投入了大量资金，因此将对Firefox X收费)。通过首次公开招股所得收益，Mozilla将设立一个Mozilla慈善基金，专门用于治疗疾病、消除贫穷、以及为第三世界国家提供救助。六个月之后，很多Crack用户被发现死在计算机屏幕前(由于太过上瘾，所以他们无法离开计算机)。公众和华尔街将对Mozilla大加谴责，但已经于事无补。最终，知识工人将从世界上消失，我们再次回到弱肉强食的原始社会。随着文明沦丧，吟游诗人将为人们描述一个碧水蓝天的新世界，并称之为“Web Vista”时代。

　　2022年：最后一名语义网络开发人员公布了一个基于RDF表达方式的Sudoku解决方案。由于最后一台笔记本失效，Web3.0最终未能到来。(奥托)

The goal of this paper is to provide an overview of technologies that can be applied to knowledge management and to assess their actual or potential contribution to the basic processes of knowledge creation and sharing within organizations. The aim is to identify trends and new developments that seem to be significant and to relate them to technology research in the field, rather than to provide a comprehensive review of available products.

Knowledge management (see, for example, Davenport and Prusak¹) is the name given to the set of systematic and disciplined actions that an organization can take to obtain the greatest value from the knowledge available to it. “Knowledge” in this context includes both the experience and understanding of the people in the organization and the information artifacts, such as documents and reports, available within the organization and in the world outside. Effective knowledge management typically requires an appropriate combination of organizational, social, and managerial initiatives along with, in many cases, deployment of appropriate technology. It is the technology and its applicability that is the focus of this paper.

To structure the discussion of technologies, it is helpful to classify the technologies by reference to the notions of tacit and explicit knowledge introduced by Polanyi in the 1950s^2,3 and used by Nonaka^4,5 to formulate a theory of organizational learning that focuses on the conversion of knowledge between tacit and explicit forms. Tacit knowledge is what the knower knows, which is derived from experience and embodies beliefs and values. Tacit knowledge is actionable knowledge, and therefore the most valuable. Furthermore, tacit knowledge is the most important basis for the generation of new knowledge, that is, according to Nonaka: “the key to knowledge creation lies in the mobilization and conversion of tacit knowledge.”⁵ Explicit knowledge is represented by some artifact, such as a document or a video, which has typically been created with the goal of communicating with another person. Both forms of knowledge are important for organizational effectiveness.⁶

These ideas lead us to focus on the processes by which knowledge is transformed between its tacit and explicit forms, as shown in Figure 1.⁵ Organizational learning takes place as individuals participate in these processes, since by doing so their knowledge is shared, articulated, and made available to others. Creation of new knowledge takes place through the processes of combination and internalization. As shown in Figure 1, the processes by which knowledge is transformed within and between forms usable by people are

• Socialization (tacit to tacit): Socialization includes the shared formation and communication of tacit knowledge between people, e.g., in meetings. Knowledge sharing is often done without ever producing explicit knowledge and, to be most effective, should take place between people who have a common culture and can work together effectively (see Davenport and Prusak,¹ p. 96). Thus tacit knowledge sharing is connected to ideas of communities and collaboration. A typical activity in which tacit knowledge sharing can take place is a team meeting during which experiences are described and discussed.
• Externalization (tacit to explicit): By its nature, tacit knowledge is difficult to convert into explicit knowledge. Through conceptualization, elicitation, and ultimately articulation, typically in collaboration with others, some proportion of a person's tacit knowledge may be captured in explicit form. Typical activities in which the conversion takes place are in dialog among team members, in responding to questions, or through the elicitation of stories.
• Combination: (explicit to explicit): Explicit knowledge can be shared in meetings, via documents, e-mails, etc., or through education and training. The use of technology to manage and search collections of explicit knowledge is well established. However, there is a further opportunity to foster knowledge creation, namely to enrich the collected information in some way, such as by reconfiguring it, so that it is more usable. An example is to use text classification to assign documents automatically to a subject schema. A typical activity here might be to put a document into a shared database.
• Internalization (explicit to tacit): In order to act on information, individuals have to understand and internalize it, which involves creating their own tacit knowledge. By reading documents, they can to some extent re-experience what others previously learned. By reading documents from many sources, they have the opportunity to create new knowledge by combining their existing tacit knowledge with the knowledge of others. However, this process is becoming more challenging because individuals have to deal with ever-larger amounts of information. A typical activity would be to read and study documents from a number of different databases.

Figure 1

These processes do not occur in isolation, but work together in different combinations in typical business situations. For example, knowledge creation results from interaction of persons and tacit and explicit knowledge. Through interaction with others, tacit knowledge is externalized and shared.⁷ Although individuals, such as employees, for example, experience each of these processes from a knowledge management and therefore an organizational perspective, the greatest value occurs from their combination since, as already noted, new knowledge is thereby created, disseminated, and internalized by other employees who can therefore act on it and thus form new experiences and tacit knowledge that can in turn be shared with others and so on.⁷ Since all the processes of Figure 1 are important, it seems likely that knowledge management solutions should support all of them, although we must recognize that the balance between them in a particular organization will depend on the knowledge management strategy used.⁸

Table 1 shows some examples of technologies that may be applied to facilitate the knowledge conversion processes of Figure 1. These technologies and others are discussed in this paper. The individual technologies are not in themselves knowledge management solutions. Instead, when brought to market they are typically embedded in a smaller number of solutions packages, each of which is designed to be adaptable to solve a range of business problems. Examples are portals, collaboration software, and distance learning software. Each of these can and does include several different technologies.

The approach to the technology of knowledge management in this paper emphasizes human knowledge. Sometimes in computer science “knowledge management” is interpreted to mean the acquisition and use of knowledge by computers, but that is not the meaning used here. In any case, automatic extraction of deep knowledge (i.e., in a form that captures the majority of the meaning) from documents is an elusive goal. Today the level of automatic extraction is deemed to be rather shallow because only a subset of the meaning, sometimes a very limited one, can be captured, ranging from recognition of entities such as proper names or noun phrases to automatic extraction of ontological relations of various kinds (e.g., References 9 and 10), and there is no system that can reason (in the sense of deducing something new from what it already knows) over the extracted knowledge in a way that even approaches the capabilities of a human. As an example of the current state of the art in applications for extracting knowledge automatically, Figure 2 shows a system¹¹ for analyzing reports of appellate court decisions to find the precedents they may affect. Court opinions are analyzed to find language that refers to other cases that the opinion may modify or invalidate. The candidate cases are retrieved from a database of law reports and are presented to an analyst for final judgment. The results are used to enrich the database with appropriate cross-references. Here the approach is that a template defines the fragment of knowledge to be sought, and the system tries to fill it by extracting information from the text. However, the candidate pieces of extracted knowledge must still be presented to a human for review and final decision, so that the value of the system is in increasing the productivity of the human analysts. For the foreseeable future, knowledge management in business will be about human knowledge in its various forms.

Figure 2

The use of technology in knowledge management is not new, and considerable experience has been built up by the early pioneers. Even before the availability of solutions such as Lotus Notes**¹² on which many contemporary knowledge management solutions are based, companies were deploying intranets, such as EPRINET,¹³ based on early generations of networking and computer technology that improved access to knowledge “on line.” Collaboration and knowledge sharing solutions also arose from the development of on-line conferencing and forums¹⁴ using mainframe computer technology. Today, of course, intranets and the Internet are ubiquitous, and we are rapidly approaching the situation where all the written information needed by a person to do his or her job is available on line. However, that is not to say that it can be used effectively with the tools currently available.

It is important to note that knowledge management problems can typically not be solved by the deployment of a technology solution alone. The greatest difficulty in knowledge management identified by the respondents in a survey¹⁵ was “changing people's behavior,” and the current biggest impediment to knowledge transfer was “culture.” Overcoming technological limitations was much less important. The role of technology is often to overcome barriers of time or space that otherwise would be the limiting factors. For example, a research organization divided among several laboratories in different countries needs a system that scientists with common interests can use to exchange information with each other without traveling, whereas a document management system can ensure that valuable explicit knowledge is preserved so that it can be consulted in the future. Two caveats must be stated at this point. First is the point made by Ackerman¹⁶ that in many respects the state of the art is such that many of the social aspects of work important in knowledge management cannot currently be addressed by technology. Ackerman refers to this situation as a “social technical gap.” Second, the coupling between behavior and technology is two-way: the introduction of technology may influence the way individuals work. People can and do adapt their way of working to take advantage of new tools as they become available, and this adaptation can produce new and more effective communication within teams (e.g., the effect of introducing solutions based on Lotus Notes on process teams in a paper mill described by Robinson et al.¹⁷ or the adaptations made by people in a customer support organization studied by Orlikowski¹⁸ after Notes was introduced).

Other surveys of technology for knowledge management can be found in the book, Working Knowledge by Davenport and Prusak,¹ and in a paper by Jackson.¹⁹ Prospects for using artificial intelligence (AI) techniques in knowledge management have been discussed recently by Smith and Farquhar.²⁰

In the following sections of this paper the technologies that support the processes of Figure 1 are described in more detail and illustrated with examples drawn largely from current research projects.

Tacit to tacit

The most typical way in which tacit knowledge is built and shared is in face-to-face meetings and shared experiences, often informal, in which information technology (IT) plays a minimal role. However, an increasing proportion of meetings and other interpersonal interactions use on-line tools known as groupware. These tools are used either to supplement conventional meetings, or in some cases to replace them. To what extent can these tools facilitate formulation and transfer of tacit knowledge?

Groupware. Groupware is a fairly broad category of application software that helps individuals to work together in groups or teams. Groupware can to some extent support all four of the facets of knowledge transformation. To examine the role of groupware in socialization we focus on two important aspects: shared experiences and trust.

Shared experiences are an important basis for the formation and sharing of tacit knowledge. Groupware provides a synthetic environment, often called a virtual space, within which participants can share certain kinds of experience; for example, they can conduct meetings, listen to presentations, have discussions, and share documents relevant to some task. Indeed, if a geographically dispersed team never meets face to face, the importance of shared experiences in virtual spaces is proportionally enhanced. An example of current groupware is Lotus Notes,¹² which facilitates the sharing of documents and discussions and allows various applications for sharing information and conducting asynchronous discussions to be built. Groupware might be thought to mainly facilitate the combination process, i.e., sharing of explicit knowledge. However, the selection and discussion of the explicit knowledge to some degree constitutes a shared experience.

A richer kind of shared experience can be provided by applications that support real-time on-line meetings—a more recent category of groupware. On-line meetings can include video and text-based conferencing, as well as synchronous communication and chat. Text-based chat is believed to be capable of supporting a group of people in knowledge sharing in a conversational mode.²¹ Commercial products of this type include Lotus Sametime** and Microsoft NetMeeting**. These products integrate both instant messaging and on-line meeting capabilities. Instant messaging is found to have properties between those of the personal meeting and the telephone: it is less intrusive than interrupting a person with a question but more effective than the telephone in broadcasting a query to a group and leaving it to be answered later.

In work on the Babble system,²² chat was evaluated by at least some users as being “… much more like conversation,” which is promising for the kind of dialog in which tacit knowledge might be formed and made explicit. However, not all on-line meeting systems have the properties of face-to-face meetings. For example, the videoconferencing system studied by Fish et al.²³ was judged by its users to be more like a video telephone than like a face-to-face meeting. Currently, rather than replacing face-to-face meetings, many on-line meetings are found to complement existing collaboration systems and the well-established phone conference and are therefore probably more suited to the exchange of explicit rather than tacit knowledge. On-line meetings extend phone conferences by allowing application screens to be viewed by the participants or by providing a shared whiteboard. An extension is for part of the meeting to take place in virtual reality with the participants represented by avatars.²⁴ One research direction is to integrate on-line meetings with classic groupware-like applications that support document sharing and asynchronous discussion. An example is the IBM-Boeing TeamSpace project,²⁵ which helps to manage both the artifacts of a project and the processes followed by the team. On-line meetings are recorded as artifacts and can be replayed within TeamSpace, thus allowing even individuals who were not present in the original meeting to share some aspects of the experience.

Some of the limitations of groupware for tacit knowledge formation and sharing have been highlighted by recent work on the closely related issue of the degree of trust established among the participants.²⁶ It was found that videoconferencing (at high resolution—not Internet video) was almost as good as face-to-face meetings, whereas audio conferencing was less effective and text chat least so. These results suggest that a new generation of videoconferencing might be helpful in the socialization process, at least in so far as it facilitates the building of trust. But even current groupware products have features that are found to be helpful in this regard. In particular, access control, which is a feature of most commercial products, enables access to the discussions to be restricted to the team members if appropriate, which has been shown²² to encourage frankness and build trust.

Another approach to tacit knowledge sharing is for a system to find persons with common interests, who are candidates to join a community. In Foner's Yenta System,²⁷ the similarity of the documents used by people allowed the system to infer that their interests were similar. Location of other people with similar interests is a function that can be added to personalization systems, the goal of which is to route incoming information to individuals interested in it. There are obvious privacy problems to overcome.

Expertise location. Suppose one's goal is not to find someone with common interests but to get advice from an expert who is willing to share his or her knowledge. Expertise location systems have the goal of suggesting the names of persons who have knowledge in a particular area. In their simplest form, such systems are search engines for individuals, but they are only as good as the evidence that they use to infer expertise. Some possible sources of such evidence are shown in Table 2.

The problem with using an explicit profile is that persons may not be motivated to keep it up to date, since to them it is just another form to fill in. Thus it is preferable to gather information automatically, if possible, from existing sources. For example, a person's resume or a list of the project teams that he or she has worked on may exist in a company database. Another automatic approach is to infer expertise from the contents of documents with which a person's name is associated. For example, authorship (creation or editing) of a document presumably indicates some familiarity with the subjects it discusses, whereas activities such as reading indicate some interest in the subject matter. Two approaches to using document evidence for expertise location suggest themselves: either the documents can be classified according to some schema, thus classifying their authors; or when a user submits a query to the expertise location system, it searches the documents, transforms the query to a list of authors (suitably weighted), and returns the list as the result of the expertise search.

The current state of the art is to use the first three sources of evidence listed in Table 2: explicit profiles, evidence mined from existing databases, and evidence inferred from association of persons and documents. For example, the Lotus Discovery Server** product contains a facility whereby an individual's expertise is determined using these techniques,²⁸ while it and the Tacit Knowledge Systems KnowledgeMail** product²⁹ analyze the e-mail a person writes to form a profile of his or her expertise. Given the properties of on-line discussions, discussed below, it is reasonable to suppose that a fourth source of evidence could be the content of the questions answered by a person in such a system, with the added advantage that such a person is already willing to be helpful. This example is a simple case of the social interaction dimension in expertise location which, as found in empirical studies (e.g., Reference 30), is an important factor but is not yet reflected in available applications, perhaps because of the difficulty of capturing aspects such as the expert's communication skills, in order to rate how useful he or she is likely to be.

Tacit to explicit

According to Nonaka, the conversion of tacit to explicit knowledge (externalization) involves forming a shared mental model, then articulating through dialog. Collaboration systems and other groupware (for example, specialized brainstorming applications³¹) can support this kind of interaction to some extent.

On-line discussion databases are another potential tool to capture tacit knowledge and to apply it to immediate problems. We have already noted that team members may share knowledge in groupware applications. To be most effective for externalization, the discussion should be such as to allow the formulation and sharing of metaphors and analogies, which probably requires a fairly informal and even freewheeling style. This style is more likely to be found in chat and other real-time interactions within teams.

Newsgroups and similar forums are open to all, unlike typical team discussions, and share some of the same characteristics in that questions can be posed and answered, but differ in that the participants are typically strangers. Nevertheless, it is found that many people who participate in newsgroups are willing to offer advice and assistance, presumably driven by a mixture of motivations including altruism, a wish to be seen as an expert, and the thanks and positive feedback contributed by the people they have helped.

Within organizations, few of the problems experienced on Internet newsgroups are found, such as flaming, personal abuse, and irrelevant postings. IBM's experience in this regard is described by Foulger.¹⁴ Figure 3 shows a typical exchange in an internal company forum, rendered here using a standard newsgroup browsing application. It illustrates how open discussion groups are used to contribute knowledge in response to a request for help. Note both the speed of response and the fact that the answerer has made other contributions previously. The archive of the forum becomes a repository of useful knowledge. Clearly the question answerer in this case has made a number of contributions and could be considered to be an expert. Although the exchange is superficially one of purely explicit knowledge, the expert must first make a judgment as to the nature of the problem and then as to the most likely solution, both of which bring his or her tacit knowledge into play. Once the knowledge is made explicit, persons with similar problems can find the solution by consulting the archive. A quantitative study³² of this phenomenon in the IBM system showed that the great majority of interchanges were of this question-and- answer pattern, and that even though a large fraction of questions were answered by just a few persons, an equal proportion were answered by persons who only answered one or two questions. Thus the conferencing facility enabled knowledge to be elicited from the broad community as well as from a few experts.

Figure 3

Explicit to explicit

There can be little doubt that the phase of knowledge transformation best supported by IT is combination, because it deals with explicit knowledge. We can distinguish the challenges of knowledge management from those of information management by bearing in mind that in knowledge management the conversion of explicit knowledge from and to tacit knowledge is always involved. This leads us to emphasize new factors as challenges that technology may be able to address.

Capturing knowledge. Once tacit knowledge has been conceptualized and articulated, thus converting it to explicit knowledge, capturing it in a persistent form as a report, an e-mail, a presentation, or a Web page makes it available to the rest of the organization. Technology already contributes to knowledge capture through the ubiquitous use of word processing, which generates electronic documents that are easy to share via the Web, e-mail, or a document management system. Capturing explicit knowledge in this way makes it available to a wider audience, and “improving knowledge capture” is a goal of many knowledge management projects. One issue in improving knowledge capture is that individuals may not be motivated to use the available tools to capture their knowledge. Technology may help by improving their motivation or by reducing the barriers to generating shareable electronic documents.

One way to motivate people to capture knowledge is to reward them for doing so. If rewards are to be linked to quality rather than quantity, some way to measure the quality of the output is needed. Quality in the abstract is extremely difficult to assess, since it depends on the potential use to which the document is to be put. For example, a document that explains basic concepts clearly would be useful for a novice but useless to someone who is already an expert. If we focus on usefulness as a measure of quality, and if we substitute “use” for “usefulness,” then we have something that IT systems can measure. In fact, portal infrastructures that mediate access to documents can easily accumulate metrics of document use, and hence can estimate usefulness and quality. The next generation of products will include such features.²⁸

Another measure of quality is the number of times a document has been cited, as in the scholarly literature, or the number of times it has been hyperlinked to, as on the Internet. A citation or hyperlink is evidence that the author of the citing or linking document thought that the target document is valuable. The most valuable or authoritative documents can be detected in Internet applications by analyzing the links between Web pages, thus measuring the cumulative effects of numerous value judgments (e.g., see References 33 and 34). The numeric quality estimate that can be derived is useful in information retrieval, where it can be used to boost the position of high-quality documents in the search results list. This method has been applied to citation analysis in scientific papers by the ResearchIndex search engine^35,36 and to Web search by the Google search engine.³⁷

Citation analysis of this kind detects quality assessments made in the course of authoring documents. Quality judgments by experts are another way to capture their knowledge. There are, of course, many deployed solutions in which documents undergo a quality review through a refereeing process, often facilitated by a workflow application. In this case, the quality judgment acts as a gate, and documents judged to be of low quality are not distributed. However, technology also makes it feasible to record judgments as annotations of existing documents.³⁸ Here, the association of an annotation with a document is recorded in some infrastructure, such as a special annotation server that the user's browser accesses to find annotations of the Web page being viewed. Numeric data stored in databases can also be annotated³⁹ to record various interpretations, judgments, or cautions. Annotations may also support collaboration around documents,⁴⁰ although, as in other applications where the underlying documents may be altered, the annotation system needs to be robust in the face of changes.

Although the most common way to capture knowledge by far is to write a document, technology has made the use of other forms of media feasible. Digital audio and video recordings are now easily made, and an expert may find that speaking to a camera or microphone is easier or more convenient than writing, particularly if the video is of a presentation that has to be made in the ordinary course of business, or if the audio recording can be made in an otherwise unproductive free moment. It is also now relatively easy to distribute audio and video over networks. However, nontext digital media have the disadvantage of being more difficult to search and to browse than text documents and, hence, are less usable as materials in a repository of knowledge. Browsing of video has been improved by summarization techniques that automatically produce a gallery of extracted still images, each of which represents a significant passage in the video.⁴¹ If the video is of someone giving a presentation, images of the speaker alone will not convey as much as a summary that includes images of any visual aids, such as slides or charts, that accompany the narrative. Several systems that key a recording of a presentation to the slides have been described.^42-44

Although video searching systems have been built that use image searching⁴⁵ of extracted frames,^46,47 they are hampered by the difficulty of composing a semantically meaningful image query. A more fruitful approach to searching is to extract text from the multimedia object, if possible. Although in some cases the video may contain text (on images of text slides), in most cases the challenge is to convert speech to text.

Speech recognition. Improvements in the accuracy of automatic speech recognition (ASR) hold out the promise of usable speaker-independent recognition with unconstrained vocabulary in the foreseeable future. Figure 4 shows progress with time in a number of standardized speech recognition tasks. Word error rates were reported in the Speech Recognition Workshop conferences of the National Institute of Standards and Technology. The accuracy varies with the difficulty of the task. The resource management task involves reading speech with a 1000-word vocabulary. Broadcast news uses recordings with an approximately 20K word vocabulary, whereas the CallHome and switchboard are telephone (lower speech quality) recognition tasks with unconstrained vocabulary. In all cases the accuracy shows steady improvement with time.

Figure 4

Accuracy for speech recorded under controlled conditions is already acceptable, but the error rate for poor quality recordings (for example, from the telephone) is still high enough to cause problems for applications unless the vocabulary is constrained. However, the trends depicted in Figure 4 show that future improvements can reasonably be expected and will lead to new ways to capture knowledge.

Although perfect or near-perfect transcription produces a text transcript that can be browsed like any other piece of text, ways to make an imperfect transcript usable as a browsing aid are being investigated.^48,49 In this work even an imperfect transcript supports browsing because certain words and phrases, which are judged to be significant and for which the estimated accuracy of ASR is high, are highlighted. Such techniques can be used to make the replay of audio more usable even where the transcript as a whole is unreadable because of the density of errors. The highlights can be used to find the passage of interest.

Search. The most important technology for the manipulation of explicit knowledge helps people with the most basic task of all: finding it. Since the trend in most organizations is for essentially all documents to become available in electronic form on line, the challenge of on-line access has been transformed into the challenge of finding the materials relevant for some task. Furthermore, the total amount of potentially relevant information, including what is on the Internet and company intranets and what is available from commercial on-line publishers, continues to grow rapidly. Thus text search, which only 10 years ago was a tool primarily used by librarians to search bibliographic databases, has become an everyday application used by almost everyone. Not surprisingly, the new uses of text search have motivated new work on the technology.

Another driving factor in the use of on-line explicit knowledge is the diversity of sources from which it is available. It is not uncommon for users to have to look in several databases or Web sites for potentially relevant information. Since there is little standardization, users have to cope with different user interfaces, different search language conventions, and different result list presentations. Portals—described in another paper in this issue⁵⁰—are a popular approach to reducing the complexity of the user's task. The key aspect that allows a portal to do this is that it maintains its own meta-data about the information to which it gives access. In the current state of the art, the meta-data may be quite simple, consisting of a list of sources and a search index formed from the content of the sources. Even this simple function provides great value because it relieves the user of the need to visit all the sources to find out whether they contain relevant information. The user is therefore made more productive, and the quality of his or her work is improved. Most portal systems use a single search index, which requires that the documents in the domain of interest have to be retrieved by “spidering” or “crawling” at indexing time. The alternative, using distributed search as in, for example, the Harvest project,⁵¹ has not proved to be popular for knowledge management applications, perhaps because advances in hardware have made it cheaper to build a central index. Recent developments in peer-to-peer applications, such as Gnutella⁵² and the collaboration application Groove,⁵³ have promoted a new interest in distributed search, which may lead to new advances.

The index that is built by a text search engine consists of a list of the words that occur in the indexed documents, along with a data structure (the inverted file) that allows the documents in which the words occurred to be determined efficiently at search time.⁵⁴ Users can therefore use query words that they expect to occur in the documents. The problem is that not all the documents will use the same words to refer to the same concept and, therefore, not all the documents that discuss the concept will be retrieved. In a world of information overload this situation is not usually a problem, but for applications where it is important to have high recall, an alternative approach can be used in which documents are assigned meta-data that describe the concepts they discuss in a controlled vocabulary. This is a classical approach used in bibliographic databases. However, where searches are being done by untrained end users rather than librarians, the evidence is that searching with natural language gives better results than does searching with a controlled vocabulary.⁵⁵

The most common problem in a search is that a query retrieves many documents that are irrelevant to the user's needs, known as the problem of search precision (a measure of accuracy). Precision is of paramount importance in a world of “info-glut.” However, results from TREC (Text REtrieval Conference)⁵⁶ indicate that the accuracy of natural language search engine technology has reached a plateau in recent years. What are the prospects of improvements to the search function that will benefit knowledge management systems? Two areas of potential improvement can be identified: increased knowledge of the user and of the context of his or her information need, and improved knowledge of the domain being searched.

The notion that increased knowledge of the user can be beneficial comes from the realization that in almost all search systems today the only information about the user's information need that is available to the system is the query. The most common query submitted to Web-based search services is two words, and the average query length is only about 2.3 words.⁵⁷ Obviously, this amount is not much information. A challenging research area is to gather better information about the context of a search and to build search engines that can use this information to good advantage.

The goal of gathering and using more information about the domain being searched is one that is well-established, but progress so far has been limited. It is common to use a thesaurus—a kind of simple domain model—as an adjunct to a search, although this is more common in systems designed for specialists. Expansion of a query with synonyms is known to improve the recall in a text search, but expansion is only effective in well-defined domains where the ambiguity of words, and the validity of term relationships, is not an issue. To improve precision in broad-domain searching by reducing the ambiguity of ordinary words using thesauri or other structures such as ontologies has been a goal of much research, with many negative results (e.g., Reference 58). Recently, however, some encouraging findings have been obtained.⁵⁴ Using WordNet⁵⁹ (a large manually built thesaurus that is widely available), combined with automatically built data structures encoding co-occurrence and head-modifier relations, Mandala et al.⁶⁰ showed significant improvements in average precision, a measure of accuracy, as shown in Figure 5. The results were obtained using TREC data, from queries derived from the search topics using the title field, the title and description fields, or all the fields in the topic. Woods et al.⁶¹ also reported improvements by using a different approach to encoding knowledge of the domain, in this case a semantic network that integrated syntactic, semantic, and morphological relationships.

Figure 5

Taxonomies and document classification. Knowledge of a domain can also be encoded as a “knowledge map,” or “taxonomy,” i.e., a hierarchically organized set of categories. The relationships within the hierarchy can be of different kinds, depending on the application, and a typical taxonomy includes several different kinds of relations. The value of a taxonomy is twofold. First, it allows a user to navigate to documents of interest without doing a search (in practice, a combination of the two strategies is often used if it is available). Second, a knowledge map allows documents to be put in a context, which helps users to assess their applicability to the task in hand. The most familiar example of a taxonomy is Yahoo!,⁶² but there are many examples of specialized taxonomies used at other sites and in company intranet applications.

Manually assigning documents to the categories in a taxonomy requires significant effort and cost, but in recent years automatic document classification has advanced to the point where the accuracy of the best-performing algorithms exceeds 85 percent (F₁ measure) on good quality data.⁶³ This degree of accuracy is adequate for many applications and is in fact comparable to what can be achieved by manual classifiers in a well-organized operation,⁶⁴ although the accuracy of automatic classification over different types of data varies quite widely.⁶⁵ An attractive feature of the current generation of automatic classifiers is their inclusion of machine-learning algorithms that train themselves from example data, whereas the previous generation required construction of a complex description of the category in the form, for example, of an elaborate query. Selecting documents as training examples is a simpler task.

Automatic classification, although simple in concept, is capable of surprisingly refined distinctions, given enough training data. For example, it has been known for some time (see the brief review in Kukich⁶⁶) that automatic essay marking systems can assign grades to student essays with an accuracy and consistency only slightly worse than human graders, and recently it has been shown that a document classifier can perform well in this application.⁶⁷ Table 3 shows the results of comparing two human graders and an automatic classifier. The automatic classifier performed very nearly as well as the human graders, both in accuracy and consistency, even though the test essays were on unconstrained subjects.

Despite the power of automatic classification, there are many challenges in implementing solutions using taxonomies. The first challenge is the design of the taxonomy, which has to be comprehensible to users (so that they can use it for navigation with no or minimal training) and has to cover the domain of interest in enough detail to be useful. There are a number of strategies for building a taxonomy,⁶⁸ including the use of doc