华海的生活， 工作， 学习.

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)

中国青年报

　　何卫宁

　　-在市场竞争中，需要懂得进与退的辩证法。什么时机退，什么领域不竞争，要相机行事。有时往往需要以不争去躲避恶性竞争，用不争来获取战略优势。

　　最近，以制造精美个人电脑而著称的美国苹果电脑公司有两个新举动：一个是将公司的名字改为“苹果公司”(删去原名字中的“电脑”二字)，第二个是进军手机制造业，推出一款设计极其精巧的手机iPhone。从苹果公司的这两项惊人的举措看，苹果的新方向似乎是要退出个人电脑市场。

　　主动退出个人电脑市场的不止是
苹果公司。个人电脑业的老前辈美国的IBM公司，早在2005年5月，就把扭亏无望的个人电脑事业部卖给了中国的联想公司。对IBM来说，出卖个人电脑事业部是一个困难的决定，因为个人电脑市场本身就是由IBM一手缔造的。

　　也许有人会说，忙于退出个人电脑市场的公司都是些
恐龙级的怪物。然而，就是当今个人电脑市场里“风头正劲”的戴尔电脑公司，也遇到了麻烦。最近其
股票价格大幅下跌，原因是投资者对戴尔的业绩不满意。

　　是个人电脑市场衰败了吗？其实，个人电脑市场并没有衰败，而是正在发生着深刻的变化，这种变化体现在三个趋势上：

　　其一，是信息处理能力的渗透趋势。个人电脑的信息处理能力越来越强大，反衬出消费者身边其他电器在信息处理能力方面的弱小，于是消费者就愿意花钱去弥补那个差距。例如，新近流行的智能手机不仅能打电话，还能像个人电脑一样上互联网、接收发送电子邮件、听音乐甚至看电视。

　　其二，是个人电脑瘦身的趋势。个人电脑功能越变越强，可是使用人群却越来越庞大，很多人其实只需要电脑一小部分的功能，电脑功能的浪费现象越来越严重。例如，商业公司里的绝大部分个人电脑都被当做打字机和幻灯机来使用，商业公司的董事们总有一天会下决心消灭那个浪费。

　　其三，是消费者的偏爱在改变。以前，消费者满足于低成本个人电脑；现在，消费者越来越挑剔产品是否有创新设计，越来越强调个人电脑是否能与家庭中的其他电器相匹配，越来越要求更好的售后服务。这三个趋势的力量很强大，所有的个人电脑公司都在寻找应对策略。

　　苹果公司目前占世界个人电脑市场份额的5%，想再增加1%都是一件难事。所以，苹果公司的应对策略就是以更快的速度去推进向个人电脑的渗透进程。虽然看上去它的iPhone不像个人电脑，但实际上却是比竞争对手的设计更新颖、包含更多先进技术的“小个人电脑”。请看，苹果公司的调整策略具有极强的辩证思维，其核心思路是不是很像老子曾经说过的：有无相生，前后相随。

　　IBM的应对策略与苹果公司不同，但也有相似之处，那就是去迎接个人电脑瘦身趋势。IBM把个人电脑里的公共软件功能(比如，文字处理、电子表格、会计)向IBM计算中心转移；人们将像使用电能一样，去使用从IBM的计算中心输送过来的软件功能；一个IBM巨型电脑中心能为数十家公司的数十万个员工服务，每个公司的成本都能大幅降低。到时候，商业公司能像省电一样地去节省个人电脑费用。那么，退出个人电脑市场的IBM不再继续驻守对自己来说已经没有战略意义的阵地，这又很类似于老子讲的：曲则全，洼则盈。

　　当今个人电脑市场里的领袖戴尔公司与众不同，它认为自己不需要什么应对策略。1990年它的年收入仅有5亿美元，经 2001年的320亿美元，抵达2006年的560亿美元。被戴尔公司斩杀的知名公司很多，比如，名噪一时的美国康柏公司因抵挡不住戴尔的低价压力而败下阵去。帮助戴尔成功的有效武器是其极为高效的运作模式——在互联网上直销低价格个人电脑。很多公司都想挑战戴尔的直销模式，但是均以失败告终。所以，戴尔公司很少有什么危机感，却总是能让别人怀有危机感。

　　不过，戴尔公司的优势最近被惠普和联想打破。为了扭转局面，戴尔照旧发动新一轮的价格战。大幅的价格下降确实像往常一样能使销售增长，但是这次销售只提高了6%，更令人沮丧的是，公司的运营利润却下跌了48%。这背后的原因并不神秘，以前，消费者对大幅降价敏感；现在，拥有了新偏爱的消费者对大幅降价变得不甚敏感。戴尔公司盲目崇拜低价格战略的效力，漠视了消费者心理的改变。这不正说明戴尔的竞争战略里缺乏的是辩证思维吗？老子曾说，水盛在器皿里太满就会溢出来，所以要适可而止；刀刃磨得太锋利就会折断，很难长久。虽说戴尔公司的刀刃并没有折断，但是被砍的东西却变得不惧怕它那把锋利的刀了。

　　总之，在市场竞争中，需要懂得进与退的辩证法。什么时机退，什么领域不竞争，要相机行事。有时往往需要以不争去躲避恶性竞争，用不争来获取战略优势。

作者：王奇珍

麦肯锡曾这样评价中国企业：成本优势的巨人却是成本管理上的侏儒。特别是人力成本方面，由于管理者与员工长期片面形成的“人本观念”，没有全面理解“以人 为本”的实质，从而进入了“人本陷阱”，造成人工成本呈现失控状态，最终导致企业利润与人工成本的失衡，也成了企业管理者最“头疼的顽疾”。

    其实，成本控制是一门花钱的艺术，而不是节约的艺术。关键是如何将每一分钱花得恰到好处，将企业的每一种资源用到最需要它的地方。如何控制好企业人工成本 呢？我们首先要从企业成本控制入手，把人工成本控制放入企业成本的“大局”之中来分析，如果只以“人工成本”论成本，必将起不到应有的效果。著名经济学家 吴敬琏说，“成本控制是一门花钱的艺术，而不是节约的艺术。以节约为成本控制基本理念的企业只是土财主式的企业，他们除了盘剥工人和在原材料上大打折扣以 外，没有什么过人之处。所以，我们需要学习现代企业应有的成本控制战略以及方法。”

    一、企业总体成本的控制

    第一步：明确战略目标

    方向正确等于成功了一半，成本控制也一样。实际上，企业降低成本的途径必须以提高(或不损坏)其竞争地位为指针。具体地说，如果某项成本措施削弱了企业的 战略地位，就应弃之不用；如果某项成本的增加有助于增加企业的竞争实力，这种成本的增加就是值得鼓励的。如果企业把成本作为战略来看待，那么成本管理就已 经不仅仅是财务部门的事情，更不仅仅是生产部门的事情，它应该是全方位、多角度、突破企业边界的成本管理体系。

    那么，如何确立这个体系呢？就是运用价值链分析手法，分析企业内部、所处行业以及竞争对手的价值链构成状况，从战略角度确定控制成本的基本方向。

    （一）、进行企业内部价值链分析

    对各个部门、各个环节进行了梳理，对每一个环节的成本与收益进行了细化。比如广告需要成本多少？创造价值多少？运输需要成本多少？创造价值多少？除此之外，还对管理部门、销售部门、采购部门等主要部门的成本与效益进行了梳理。

    （二）、进行行业价值链分析

    行业价值链是什么呢？简单地说，企业即存在于某一行业价值链的某个点，这一联系存在于行业内部为消费者提供某种最终产品或服务的相关企业之间。实际上， 上、下游与渠道企业的联接点都能够显著地影响企业成本，如供应商产品的包装能减少企业的搬运费用，而改善价值的纵向联系可以使企业与其上、下游和渠道企业 共同降低成本，提高整体竞争优势。

    （三）、分析竞争对手的价值链

    竞争对手的价值链和本企业价值链在行业价值链中处于平行位置，通过对竞争对手价值链的分析，可以测算出竞争对手的成本。然后，自己企业与之相比较，就找出了与竞争对手在作业活动上的差异，最终就可以确定扬长避短的策略，争取成本优势。

    第一，与对手差距不大的环节，提高起来成本较高，应保持其现有状态；第二，与对手差距较大的环节，加大投入；第三，有可能形成较大优势的环节，要加大投入，争取形成压倒性优势。

    这样，在对企业内外、横向、纵向的价值链进行深入分析的基础上，结合了企业的长远战略诉求，并根据所处产业竞争环境的变化，对价值链进行了适应性的重构。并在此基础上形成一份内容详实的成本控制计划。

    第二步：四步执行法

    执行成本控制计划可分四步走：

    第一，削减

    通常说来，在企业的整体因素基本确定的情况下，企业对成本的控制应该着眼于每项生产经营活动所产生的成本，这既包括企业为生产的产品所付出的作业劳动，同时也包括这一过程所消耗的资源。

    消除生产经营成本的第一个手段就是从减少非增值作业入手。一般情况下，企业的销售为增值作业。而大部分的仓储、搬运、检验以及供、产、销环节的等待与延误等，由于并未增加产出价值，为非增值作业，应减少直至消除。可以通过缩短采购时间和加快交货时间来解决。

    第二，明确各部门的成本任务

    公司最高层领导和财务总监应将企业的整体成本进行了详细的核算，将成本控制的压力分解到每个部门头上。

    在这方面，我们可以学习邯钢“模拟市场核算、倒推单元成本、实行成本否决、全员成本管理”的方法。具体做法就是实行成本倒推，测算出各项费用在每公斤成品 中的最高限额。然后横向分解落实到各部门，纵向分解落实到销售小组和个人，层层签订承包协议，并与奖惩挂钩，使责、权、利统一，最终在整个企业内形成纵横 交错的目标成本管理体系。由于成本控制计划极细，小到一张A4纸都要斤斤计较，为此，公司还应该专门组织一个督察小组，每天对每一个环节进行跟踪检查、记 录和打分。

    第三，精细化管理

    很多优秀的管理者都说过，没有数字进行衡量，就无从谈及节俭和控制。

    伴随着成本控制计划出台的是一份数字清单，包括各工种员工工资、电费、办公用品费、销售费用、油费、样品费等几十项费用。

    我们可以将费用分为可控费用(人事、水电、包装、耗材等)和不可控费用(固定资产折旧、采购、利息、销售费用等)。每星期、每月、每季度都由财务汇总后发 到管理者的手中，超支和异常的数据就用红色特别标识。在每周一的例会和每个月月底的总结会议中，相关部门需要对超支的部分做出解释。

    为了让员工养成成本意识，财务部还需要编写一本工作流程与成本控制手册。该手册从进货、电、水、印刷用品、劳保用品、电话、办公用品、设备和其他易耗品方 面提出控制成本的方法。但是，绝对不会机械地安排资金，有效地激励也是成本控制的好办法，成本控制奖励也可以成为员工工资的一部分。

    第四，成本管理的提前和延伸

    在制订成本控制计划时，还要树立“成本管理提前”的概念。在企业成本结构中，流程的前端与后端的成本比重逐步增加，所以成本管理不应停留在过程的耗费控制 方面，更应着眼于前端产品选择及采购的成本控制、后端的营销和顾客使用成本的控制以及跨组织的成本管理等方面，深入到企业的供应、营销及售后服务部门，超 越企业边界，相互协调地进行成本改进。从产品的选择设计开发开始，就要尽力设计满足目标成本要求、具有竞争力的产品，从源头上控制成本的发生。

    第三步：不可不察的细节

    当然，一个企业的成本控制远非几千字所能描述清楚的，以下是成本控制的一些细节，这也是在实践中可运用的部分技巧。

    细节一，现金折扣激励回款

    如果客户在30天内偿付货款，就给予2%的折扣；60天内付款，就给予1%的折扣；90天内付款，就须全数收取。采取折扣的方式鼓励销售回款。

    细节二，借助应收账款融资

    企业将应收账款出让给贷款者以筹措资金，企业可以在商品发运出去以前向贷款者申请借款。经贷款者同意，即可在商品发运以后将应收账款让售给贷款者。贷款者 根据发票金额，减去现金折扣、佣金以及主要用以冲抵销货退回和销货折扣等扣款后，将余额付给筹资企业。如摩托罗拉中国公司10亿元应收账款曾让中国工商银 行购买，这样的做法也可借鉴使用。

    细节三，年终返利打款激励

    借鉴空调业的操作技巧，生产企业的惯例是利用年终返利政策等来吸引经销商提前打款。至于企业提前打款的时间和奖励额度，一般根据企业产品情况和对资金需求 程度计算成本和收益，有些企业提前打款的时间和奖励额度(按打款额)分别为1～2个月为6‰、2～3个月为12‰、3～6个月为24‰。奖励以现金形式在 年终一次性给予奖励，对提前打款者优先供应货源。这种办法不仅解决了应收账款的问题，还起到融资的作用。

    细节四，延长应付账款期限

    赊账通常被视为现金的来源，因为只要你在一个比较合适的期限内延长付款时间，你就相当于有了一笔无利息的贷款。

    细节五，区分人工工资与人工成本

    两者的区别在于生产效率。杰克?韦尔奇认为：支付更高工资的同时，使人工成本最低是完全有可能的。即使工资在增长，但如果总体生产效率上升幅度大于工资增长，总人工成本相对总产值的比例也下降了。

    细节六，做好淡旺季的人资衔接

    企业一般用淡季裁员的方式来解决人工闲置，并充分利用《劳动法》相关规定中“以完成某项工作为期限”等依据，减少裁员所支付的补偿费用。

    细节七，消除人员重叠

    彻底清查公司各部门间是否职能相互重叠的现象，从而导致无谓的人力成本浪费。将相互重叠的职能整合起来，通过共享作业或服务来降低人力成本。

    细节八，循环取货

    学习上海通用的办法：上海通用的运货车每天早晨从厂家出发，到第一个供应商那里装上准备的原材料，然后到第二家、第三家，依次类推，直到装上所有的材料，然后再返回。这样做的好处是，省去了所有供应商空车返回的浪费，充分节约运输成本。

    细节九，转移库存

    对于那种季节性，特别是持续时间比较短暂的产品，在旺季来临时往往需要有大量的存货以应对骤增的销量，这就会对库存产生极大的压力，同时占用大笔的流动资 金。一个可以借鉴的解决办法就是：要求各经销商在旺季来临前，如果提前两个月提货付款，产品按原出厂价的70%计算；如果提前一个月提货付款，按原出厂价 的85%计算；如果到了旺季来时再提货，就必须按原出厂价的全价付款。这种办法只要折扣收益低于库存成本和资金成本，就有利可图，而且还一同解决了应收账 款的难题，加快了资金周转。

    细节十，不采购多余功能的设备

    如果通过分析，我们可以知道公司计算机在80%的时间里都是做文档处理工作，只有20%的时间才真正使用一台计算机的全部功能。公司可以采购低档次的PC机，为企业节约了大量投资。

    二、人工成本的控制

    人工成本的控制要通过人工成本指标的分析，建立企业人工成本分析的控制体系，即从人工成本的增长状态进行弹性控制，从人工成本的水平状态进行比率控制；同时加强企业人工成本控制的对策，寻求企业人工成本控制有效途径，以保证企业利润和职工收入实现“双赢”。

    （一）、人工成本范围及指标体系

    1、人工成本概念及范围

    人工成本是指企业在一定时期内，在生产、经营和提供劳务活动中因使用劳动力而支付的所有直接费用和间接费用的总和。

    按我国劳动部颁发的(1997)261号文件规定，人工成本范围包括：职工工资总额、社会保险费用、职工福利费用、职工教育经费、劳动保护费用、职工住房费用和其他人工成本支出。其中，职工工资总额是人工成本的主要组成部分。

    2、人工成本的指标体系

    常用的人工成本分析指标有三类：人工成本总量指标、人工成本结构指标、比率型指标。

    人工成本总量指标反映的是企业人工成本的总量水平。人工成本结构指标是指人工成本各组成项目占人工成本总额的比例，它可反映人工成本投入构成的情况与合理 性。人工成本分析比率型指标是进行企业人工成本分析控制常用的指标，是一组能够将人工成本与经济效益联系起来的相对数。

    （二）、企业人工成本的控制体系

    1、人工成本弹性控制思路

    企业人工成本的弹性控制体系是考察人工成本的增长状态，即从动态的角度通过对人均人工成本变动幅度分别与人均增加值、人均销售收入、人均总成本变动幅度的比值——即弹性的控制，把人工成本水平的提高控制在经济效益和投入产出水平所能允许的范围之内。

    2、人工成本水平状态控制思路

    企业人工成本的比率控制体系是从水平状态考察人工成本，即从分配水平的角度控制人工成本，旨在使企业在分配方面更好地兼顾个人、企业、国家三者的利益关 系，保证企业的持续、稳定发展。人工成本的水平状态主要是从人工成本的比率指标来考察的，以行业平均的劳动分配率、人事费用率、人工成本占总成本比重这三 个比率指标为参照，衡量企业与行业对应比率指标的偏差程度。显然，企业的这三项比率指标应当低于行业平均水平，且这三项比率指标都不能为负值，所以，计算 出的综合偏差率应大于0且小于等于1，符合这一条件的企业是人工成本比率控制较好的企业。反之则认为该企业在人工成本的比率方面失控了。

    （三）、企业人工成本管理的对策

    1、提高对加强人工成本管理的认识

    人工成本管理仍然是企业管理中的一个薄弱环节。提高对加强人工成本管理的认识问题，首先是从战略上，认识到它是关系企业多方位市场竞争中生死存亡的重要战 略因素；其次是从分配的角度，认识到它是正确处理企业、职工二者利益的重要经济杠杆，它是调节劳动者这个利益主体的经济行为，从而调节劳动力资源的配置， 形成企业的激励和动力机制的经济因素；第三是从管理上，认识到它是关系人才资源开发，关系企业经济效益的提高，关系到对活劳动消耗进行监督、投放的重要工 作。

    2、精减人员、合理定岗定编，控制劳动力的投入

    精减人员、合理定岗定编是加强用人管理的基础，也是节约活劳动、降低人工成本的基础工作。若企业冗员太多，必然造成人工成本投入不合理和人工成本的无效益增长，职工收入水平反而难以提高。

    3、加强人工成本的比率控制

    目前在比率控制方面存在着在低水平的基础上收入过分向个人倾斜的问题。例如，有的企业劳动分配率、人事费用率和人工成本占总成本比重都高于行业平均水平， 主要在于企业所创造的增加值中绝大部分用在了人工费用，而用于扩大再生产的积累所剩无几，明显存在收入过分向该企业职工倾斜的问题。

    加强比率控制的措施主要表现在：(1)控制标准的细化，细化到行业内各种不同类型企业，建立行业内各种类型企业的人工成本分析与控制体系，即以各类型企业 平均的劳动分配率、人事费用率、人工成本占总成本比重这三个比率指标为参照，来考察其所属企业与之对应比率指标的偏差率，从水平状态考察企业人工成本的比 率控制情况；(2)加强宏观调控，在政策上对比率控制采取相应的措施。对于比率控制较好的企业，在保持人工成本合理比例的基础上，允许适度地提高工资总 额，在增加职工收入、调动劳动者积极性的同时，保证利润目标的实现，提高企业的经济效益。这样，使企业人工成本的比率指标始终保持在有竞争力的水平之上， 既有利于人力资源的开发、利用，又能形成良性的经营循环。对于比率失控的企业，必须使其将过高的人工成本比率指标降下来，调整好人工成本与增加值、销售收 入、总成本的比例关系。加加强对工资总额的控制，建立工资的控制体系，将人工成本控制指标纳入对经营者目标责任制的考核内容，对人工成本双向失控的企业要 追究企业领导人的责任等等。

    4、加强人工成本的弹性控制

    加强弹性控制，保持人均人工成本增长低于人均增加值及人均销售收入的增长幅度，使人工成本与产出效益保持合理比例，这是人工成本控制的核心问题，也是人工 成本控制的最关键的预警线。人工成本是一种消耗要素，这种消耗的必要性必然是它为企业带来产出的大小，从企业资本经营的角度考察，人工成本决策的首要依据 是经济效益的高低，人工成本支出的阂限值必然是收益＞成本。

    为加强人工成本的弹性控制，在措施上可从以下四方面着手：

    （1）、建立企业人工成本弹性分析与控制体系，总结经验与教训，不断提高人工成本管理水平。弹性控制有效的企业要总结经验，在继续巩固已有成效的基础上， 通过人工成本的控制寻求进一步改进企业经营管理的途径。弹性控制好的企业，说明其对人工成本的投入带来了相应的产出效益，是增收增效的人工成本，既提高了 职工收入，调动了劳动者的积极性，又使企业的整体经济效益有了提高，有利于企业的生存和发展。人工成本弹性失控的企业要进行因素分析，寻找失控的具体原因 进行改进。有的企业在人均人工成本增长的同时，人均增加值、人均销售收入、人均总成本也有所增长，但增长幅度却低于人均人工成本的增长幅度。说明在对人工 成本投入的过程中，并未带来经济效益的同步增长，也就是人工成本的相对投入量过高，这就需要企业在今后的生产经营过程中，结合人工成本的弹性控制体系，采 取相应措施，进一步减少无效的人工成本消耗，以利于企业的生存和发展。

    （2）、从生产经营上找途径，增加产出，即增加值与销售收入的增长。例如开拓市场，扩大销售额，扩大高增加值产品的生产；加大科技投入，调整产品结构，采取有利的产品组合战略，增加产品中的科技含量等。

    （3）、加速转变经济增长方式，加强集约型经营，不断提高生产技术水平，提高劳动生产率，降低单位产品的人工消耗，降低产品的物耗成本，降低总成本，在少增加投入或不增加投入的基础上提高企业产销总量，增加利润总额。

    （4）、严格限制、减少无效消耗人工成本支出，减少冗员、堵塞漏洞，最大限度降低人力资源的无效损耗。

    5、发挥工资激励作用，规范人工成本结构

    在人工成本结构中，工资是最有激励作用的因素，也是构成人工成本的主要部分。可见，工资总额水平的控制以及各类人员工资水平合理拉开档次，充分体现按劳分配、效率优先的原则，是当前人工成本控制的关键性环节。

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 document clustering to propose candidate subcategories. However, human input is probably required to ensure that the taxonomy reflects business needs (e.g., it emphasizes some aspect that may be significant but is not a strong theme in the documents). Thus, clustering can be seen as an adjunct to human effort. One usability challenge is to ensure that the user of a taxonomy editor can understand the clusters that are proposed, using automatically generated labels. The labels typically contain words or phrases that are chosen to represent the documents in the cluster; recently a technique for using extracted sentences has been proposed.^69,70

Taxonomies have proved to be a popular way in which to build a domain model to help users to search and navigate, so much so that the trend seems to be for each group of users of any size to have their own taxonomy. This popularity is understandable because as on-line tools become central to individuals' work, they naturally want to see the information displayed within a schema that reflects their own priorities and worldview, and that uses the terminology that they use. This trend is likely to lead to a proliferation of taxonomies in knowledge management applications. It follows that there will be an increasing focus on the need to map from one taxonomy to another so as to bridge between the schemas used by different groups within an organization.

Portals and meta-data. As already mentioned, portals provide a convenient location for the storage of meta-data about documents in their domain, and two examples of such meta-data, search indexes and a knowledge map or taxonomy, have been discussed. In the future, increasing use of natural language processing (NLP) in portals is likely to generate new kinds of meta-data. The general trend is for more structured information—meta-data—to be automatically generated as part of the indexing service of the portal. It is efficient to generate these meta-data when the document has been retrieved for text indexing. The value of the meta-data is in encapsulating information about the document that can be used to build selected views of the information space, such as a list of the documents in a given subject category, or mentioning a geographic location, through a database lookup in response to a user click. This makes exploration of the information easier and more rewarding, in effect providing the user with a new experience based on the exploration on which new tacit knowledge can be built as part of the internalization process to be discussed later.

Summarization. Document summaries are examples of meta-data of this kind. The value of a summary is that it allows users to avoid reading a document if it is not relevant to their current tasks. Figure 6 shows results from Tombros and Sanderson⁷¹ who showed that users performing a simple information-seeking task had to read many fewer full documents when they used a system that provided summaries than when the system provided document titles alone. Automatic generation of summaries is an active area of research. Commercially available summarizers use the sentence-selection method, originated by Luhn in 1958,⁷² in which an indicative summary is constructed from what are judged to be the most salient sentences in a document. However, the summary may be incoherent, e.g., if the selected sentences contain anaphors. Construction of more coherent summaries, implying the use of natural language generation, currently requires that the subject domain of the documents be severely restricted, as for example, to basketball games.⁷³ Summarization of long documents containing several topics is improved by topic segmentation⁷⁴ and can be further condensed for presentation on handheld devices,⁷⁵ whereas summarization of multiple documents, either about the same event⁷⁶ or in an unconstrained set of domains,⁷⁰ is another challenge being addressed by current research. For other recent work see References 77 through 79.

Figure 6

Explicit to tacit

Technology to help users form new tacit knowledge, for example, by better appreciating and understanding explicit knowledge, is a challenge of particular importance in knowledge management, since acquisition of tacit knowledge is a necessary precursor to taking constructive action. A knowledge management system should, in addition to information retrieval, facilitate the understanding and use of information. For example, the system might, through document analysis and classification, generate meta-data to support rapid browsing and exploration of the available information. It seems likely that the future trend will be for information infrastructures to perform more of this kind of processing in order to facilitate different modes of use of information (e.g., search, exploration, finding associations) and thus to make the information more valuable by making it easier to form new tacit knowledge from it. Other processing of explic