华海的生活， 工作， 学习.

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。

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 explicit knowledge, already described, can support understanding. For example, putting a document in the context of a subject category or of a step in a business process, by using document categorization, can help a user to understand the applicability or potential value of its information. Discovery of relationships between and among documents and concepts helps users to learn by exploring an information space.

A quite different set of technologies applies to the formation of tacit knowledge through learning, especially in the domain of on-line education or distance learning. Within organizations, on-line learning has the advantage of being able to be accomplished without travel and at times that are compatible with other work. A wide variety of tools and applications support distance learning.⁸⁰ The needs of the corporate training market, emphasizing self-directed learning rather than instructor-led learning, have led to a focus on interactive courseware based on the Web or on downloaded applications. In the future, modules of self-directed training will be found in portals, along with other materials.

Information overload is a trend that motivates the adoption of new technology to assist in the comprehension of explicit knowledge. The large amounts of (often redundant) information available in modern organizations, and the need to integrate information from many sources in order to make better decisions, cause difficulties for knowledge workers and others.⁸¹ Both of these trends result directly from the large amounts of on-line information available to knowledge workers in modern organizations. Information overload occurs when the quality of decisions is reduced because the decision maker spends time reviewing more information than is needed, instead of reflecting and making the decision. Various approaches to mitigating information overload are feasible. The redundancy and repetition in the information can be reduced by eliminating duplicate or overlapping messages (related to the Topic Detection and Tracking track at TREC⁸²). An agent can filter or prioritize the messages, or compound views can make it easier to review the incoming information. Finally, visualization techniques can be applied in an attempt to help the user understand the available information more easily.

Different visualizations of a large collection of documents have been used with the goal of making subject-based browsing and navigation easier. These methods include text-based category trees, exemplified by the current Yahoo! user interface. Several graphical visualizations have also been described. Themescape⁸³ uses (among other things) a shaded topographic map as a metaphor to represent the different subject themes (by location), their relatedness (by distance), and the proportional representation of the theme in the collection (by height), whereas VisualNet⁸⁴ uses a different map metaphor for showing subject categories. Another approach is represented by the “Cat-a-Cone” system⁸⁵ that allows visualization of documents in a large taxonomy or ontology. In this system the model is three-dimensional and is rendered using forced perspective. Search is used to select a subset of the available documents for visualization.

Other visualization experiments have attempted to provide a user with some insight into which query terms occur in the documents in a results list, as was done in Hearst's TileBars⁸⁶ and the application described by Veerasamy and Belkin.⁸⁷ However, the evaluation described in the latter paper showed that the advantage of the visualization in the test task was small at best. A later study,⁸⁸ which compared text, two-dimensional, and pseudo three-dimensional interfaces for information retrieval, found that the richer interfaces provided no advantage in the search tasks that were studied. This result may explain why graphical visualization has not been widely adopted in search applications, whereas text-based interfaces are ubiquitous.

Perhaps a more promising application of visualization is to help a user grasp relationships, such as those between concepts in a set of documents as in the Lexical Navigation system described by Cooper and Byrd⁸⁹ or the relationships expressed as hyperlinks between documents.⁹⁰ This use is more promising because of the difficulty of rendering relationships textually. Furthermore, figuring out the relationships within a set of documents is a task that requires a lot of processing, and computer assistance is of great value.

Conclusion

This paper has surveyed a number of technologies that can be applied to build knowledge management solutions and has attempted to assess their actual or potential contributions to the processes underlying organizational knowledge creation using the Nonaka model. The essence of this model is to divide the knowledge creation processes into four categories: socialization (tacit knowledge formation and communication), externalization (formation of explicit knowledge from tacit knowledge), combination (use of explicit knowledge), and internalization (formation of new tacit knowledge from explicit knowledge). The value of this model in the present context is that it focuses attention on tacit knowledge (which is featured in three of the four processes) and thus on people and their use of technology.

Because all four of the processes in the Nonaka model are important in knowledge management, which aims to foster organizational knowledge creation, we might seek to support all of them with technology. Although early generations of knowledge management solutions (solutions typically integrate several technologies) focused on explicit knowledge in the form of documents and databases, there is a trend to expand the scope of the solutions somewhat to integrate technologies that can, to some extent, foster the use of tacit knowledge. Among these technologies now being applied in some knowledge management solutions are those for electronic meetings, for text-based chat, for collaboration (both synchronous and asynchronous), for amassing judgments about quality, and for so-called expertise location. These technologies are in addition to those for handling documents, such as search and classification, which are already well-established yet are still developing.

Despite these trends, there are still significant shortfalls in the ability of technology to support the use of tacit knowledge—for which face-to-face meetings are still the touchstone of effectiveness. As Ackerman has pointed out, this lack of ability is not just because the designers of the applications do not appreciate how important the human dimension is (although that is true in some cases). We simply do not understand well enough how to accommodate this dimension in computer-supported cooperative work. Many of the factors that mediate effective face-to-face human-human interactions are not well understood, nor do we have good models for how they might be substituted for or synthesized in human-computer interactions. We can expect gradual progress in this direction, perhaps aided by improvements in the general fidelity with which people's faces, expressions, and gestures are rendered in (for example) high-bandwidth videoconferencing, but there can be no assurance of an immediate breakthrough because of the complexity of the problem and the current shortfall in the basic understanding of its elements.

However, the survey in this paper has highlighted many factors that provide grounds for some optimism when we consider how technology can help in knowledge management. Technology can assist teams, who in today's world may meet only occasionally or even never, to share experiences on line in order to be able to build and share tacit knowledge, and more generally to work effectively together, even if the efficiency is less than in face-to-face meetings. From the perspective of tacit knowledge formation and sharing, the relative informality of text-based chat is probably superior to more structured discussions, which may, however, be effective for sharing explicit knowledge. The importance of limiting access to team members has been highlighted by recent work. The chat archive, and other recordings of on-line meetings, have the added advantage of being able to help in the socialization of people who miss parts of the original interaction. It is also encouraging that recent work by Olson and Olson and their collaborators has shown that studio-quality video is helpful in some tasks related to knowledge management, such as collaboration (in some cases) and trust building.²⁶

Another encouraging use of technology is to help persons who need to share knowledge to find each other. Expertise location systems are in their infancy in industrial practice but hold out the promise of being able to identify individuals with the right knowledge. Even without actually identifying a person, unrestricted forums and bulletin boards have been shown to be effective in eliciting assistance both from experts and from the broader community. It seems likely that appropriate integration of this approach with chat on the one hand and expertise location on the other will result in more effective access to and communication of the knowledge in an organization.

Another way to tap the knowledge of experts is through capturing their judgments, expressed as annotation, hyperlinks, citations, and other interactions with documents. Portal infrastructures, which mediate and can collect metrics on the interaction of people and documents, are ideal for amassing this kind of information. Currently, portal products are just becoming capable of accumulating meta-data of this kind. Another trend is for their meta-data to become richer and to support a broader range of tasks. In particular, the meta-data can support the formation of new tacit knowledge from the explicit knowledge indexed by the portal, for example, by situating documents within a new conceptual framework represented by a knowledge map. It is becoming cheaper to use several different frameworks for this purpose, and thus to match them better to the needs of different groups of users, because the accuracy of automatic text classification is improving and, for some classes of content such as news stories, is already as good as the accuracy of human indexers.

Technology will clearly become more helpful in dealing with information overload. Techniques such as summarization can reduce the load of persons attempting to find the right documents to use in some task. There is some promise, as yet unfulfilled, that intelligent agents may in the future help persons to prioritize the messages they receive. And the meta-data stored by portals can be used to draw visualizations of large amounts of information, although, contrary to intuition, graphical visualizations seem not to be better than their text-based equivalents, at least for information retrieval tasks.

Finally, it should be emphasized again that this paper has dealt with human knowledge, not with the formation or use of expert systems or similar knowledge-based systems that aim to replace human reasoning with machine intelligence. The current capability of machine intelligence is such that, for the great majority of business applications, human knowledge will continue to be a valuable resource for the foreseeable future, and technology to help to leverage it will be increasingly valuable and capable.

**Trademark or registered trademark of Lotus Development Corporation, Microsoft Corporation, or Tacit Knowledge Systems.

Cited references

Accepted for publication June 15, 2001.

   作者：孙定

知识管理解决方案的核心内容是制定知识管理策略。知识管理策略要解决观念问题，要突破信息时代形成的思维定式，更新知识，使观念向知识时代校正。知识管理 策略还要解决机构的文化问题，使机构具有知识时代所要求的组织学习能力并建立知识共享机制。接下来是选择适当的产品，开发知识管理项目。因此，知识管理市 场具有咨询服务需求与技术产品需求共生的特点。前者解决知识更新、观念更新、策略制定、文化改造、调整机制等问题；后者解决具体实现的问题。正是基于知识 管理的这种特点，重要的知识管理供应商都同时提供咨询服务和技术产品。

　　知识管理产品与服务的另一特点是种类繁多，每个供应商都有自已的一套说法，这些说法互不相同，甚至差别巨大。这是由两个原因造成的：首先，目前 无论在学术上还是在实际应用中，知识管理都处于非常早期的阶段，其定义有数百种，学术上也有很多不同的观点，供应商当然是各取所需；其次，供应商都是从自 已原先的领域进入知识管理领域，拥有不同的技术和产品，而知识管理本身与其说是一种新技术不如说是一种新观念，大量现有产品与技术都与知识管理相关，供应 商所做的只是根据知识管理的需求，重新定位现有的产品。

　　这里着重讨论一些重要的、具有不同特点的知识管理产品与服务。

　　Lotus:以专取胜

　　虽说Lotus与IBM本是一家，知识管理论调也一样，但各自有各自的知识管理产品，所以还是要分开说。

　　在所有知识管理解决方案厂商中，Lotus给人印象最为深刻。知识管理所必需的文档管理和群件技术在1998年前后已经是Lotus的主打产 品。而Lotus Notes本身是一个可完成多种应用的平台，虽然不是浏览器界面，但在原理上已经很接近企业门户，这些都是Lotus进入知识管理市场的先天优势。这两年 知识管理的兴起，对Lotus来说实在是一个天赐良机。Lotus在知识管理上狠下一番功夫，拼命赌一把也就在情理之中了。

　　Lotus、IBM研究中心、IBM知识管理研究所共同对Lotus专业服务以及IBM全球知识管理服务机构在全球的2万个客户的知识管理实践 进行了调查，以Lotus现有技术为基本出发点，制定出独特的理论框架，并确立了知识管理产品策略。第一个产品K-Station企业门户和其配套产品 Discovery Server已经完成。

　　Lotus认为，仅仅将知识管理局限在从海量信息中提取有用资料是不够的，还要找到具有专业知识的人，这些人还要交流、互动、进行创造性的工 作。于是，Lotus将数据、资料及处理过程定义为“事物（Thing）”、将建立在网上的虚拟工作环境定义成“场所（Place）”、将员工、客户、专 家、合作伙伴等定义成“人（People）”，而在人、场所、事务之间建立有机关联才是理想的知识管理环境。　

　　其中，K-Station已经具有知识管理系统必备的知识管理功能，Discovery服务器则是对前者的增强。

　　在K-Station中，每个人都有自已的场所——个人场所（Personal Place）。个人场所为担任不同角色的人员提供定制的日常工作环境。在个人场所中可进行电子邮件处理、管理日程、讨论、获取订阅资料、编辑文档等操作。 沟通场所（Community Place）为由相关人员组成的小组提供了共享与共同工作的环境。所有个人文档都被加上了基于场所的标签，并按场所将文档进行分类归档。这种机制为文档的 共享和检索提供了方便。在场所中可以看到何人正在线上，并列出共享场所的清单，在线上的人可以相互进行即时的消息沟通。目前，K-Station必须在 Domino环境下运行，因此系统中至少要有一个Domino服务器。

　　微软：追求通俗

　　微软一方面将现有产品基本上都贴了知识管理的标签，一方面也在开发新一代知识管理产品。微软的新一代知识管理产品正在进行第三版β测试，其产品 代号为“Tahoe（太湖）”。与Lotus不同，微软没在知识管理理论上标新立异，在这一点上，微软比Lotus“通俗”得多。

　　按照微软的说法，Tahoe是集文档管理、文档索引/检索和协同工作于一身的企业门户。Tahoe的文档管理包括版本控制、文档的作者与密码属 性管理、文档发布控制、签发控制等功能。在文件索引方面，Tahoe可以进行全文检索，也可以对网站、文件系统、Exchange服务器、Lotus服务 器等多种信息源进行检索。

除此之外，在Tahoe系统中还可以采用人工方法对文档进行分类处理，在处理过程中，Tahoe的分类助理可以学习人工分类规则，当样本达到一定数量，分类助理就可以自动进行分类。

　　Tahoe由文档服务器、索引服务器和检索服务器组成。这些服务器既可以安装在一台机器上，也可以分装在三台机器上。使用时，既可以以WWW方式进入Tahoe，也可以通过MS Office中的Tahoe插件进入，还可以直接从Windows文件系统进入。

　　微软的策略是只提供知识管理系统平台，而针对各行各业的知识管理解决方案由经微软认证的合作伙伴提供。

　　Lotus与微软在知识管理领域各有所长:Lotus像一个老谋深算的智者，微软更像一个门槛精的小伙。Lotus先建立一个严格的体系，然后 再一步步推进;微软则不太在意体系，缺什么就做什么。Lotus的难点在于它所建立的体系是否能被各界人士认同，而微软的问题是体系不严密。

　　IBM:挖掘文本

　　在文本挖掘软件中，IBM的Text Miner很有代表性，其主要功能是特征抽取、文档聚集、文档分类和检索。

　　Text Miner的特征抽取器能从文档中抽取人名、组织名和地名以及由多个字组成的复合词。此外，特征抽取器还能抽取表达数字的词汇，例如，“钱”、“百分 比”、“时间”等。抽取完特征以后，有相似特征的文档就被自动聚集成一个集合。利用这一功能，知识管理系统可以从大量文档中找到相关文档。Text Miner还可以对文档进行自动分类。

　　Autonomy:去除冗余

　　在中国，知道Autonomy公司及其技术的人不多。但实际上，Autonomy及其CEO迈可·林奇（Mike Lynch）在知识管理界的知名度很高。迈可·林奇1991年毕业于剑桥大学，主修神经网络。他受模式识别所用的概率算法的启发，创立了 Neurodynamics公司，以概率论中的贝叶斯公式和香农的信息论作为其技术的理论基础，开发出文本挖掘产品。1998年，Autonomy公司看 中林奇的技术，以400万美元并购了林奇的公司，林奇也成为Autonomy公司的CEO。

　　Autonomy最核心的产品是Concept Agents。在经过训练以后，它能自动地从文本中抽取概念。

　　在林奇看来，按照香农的信息论，文档中除有效概念外，还有大量的冗余信息。而词或短语是否为冗余可根据它在文档中的随机度（概率）来判定。如果 能滤去冗余，就可从文档中自动抽取出表达文档主题的概念。在林奇的方案中，先要对系统进行训练，处理一些文档，由使用者对非冗余概念做出认定和识别。按照 贝叶斯概率理论，这一步实际上是让系统获得关于概念的先验概率。系统在随后的自动处理中根据这些概念在文档中出现的实际情况，按贝叶斯公式求出后验概率， 以此作为冗余过滤的依据。这一方法与语种无关，由于每个用户都要对系统进行个别训练，因而系统的文本挖掘天然就具有高度个性化的特点。到目前为止，包括报 业巨头默多克的新闻集团在内的一批知名公司已经成为Autonomy的客户，Compaq公司也已经将Autonomy的技术和产品纳入其知识管理解决方 案并在客户中推广。

　　TelTech:服务知识管理

　　TelTech的创始人Joe Shuster是一个化学工程师，他曾创建并出售了一个成功的低温工程专业公司。这一段工作使Shuster深切感受到从公司外获取专业知识的困难。基于此，Shuster于1984年创建了TelTech公司。

　　TelTech 提供三类服务:第一类服务由专家提供。TelTech拥有数千名签约专家，他们主要是有成就的学者、退休的资深专业人士和愿意提供资询服务的专业人士。 TelTech并不试图将这些人的知识存入计算机，再以专家系统的方式提供服务，而是维护专家档案,当客户需要用服务时，TelTech的知识工程师就帮 助客户分析问题，并向客户推荐数位专家。第二类服务是专业文献检索，用户可以自已通过TelTech的门户网站进行检索，也可以在知识工程师的帮助下进行 检索。第三类服务是产品与厂商检索，这种服务也是通过其门户网站提供。

　　TelTech成功的关键是建立了高性能的知识结构。它采用主题法，其主题词表分为不同专业，共有3万多个，由数位知识工程师维护，每周更新500～1200个词。

　　目前，计算机世界网也在致力于开发基于公共信息的知识管理系统，所采用的策略与TelTech基本相同。现在，计算机世界网“e海航标”频道提供的实际上就是基于主题法的IT知识管理服务。

作者： Eric Lesser and Kathryn Everest

摘要
       
        成功的商业必须反映市场的需求。为了实现这个目标，企业正在发掘他们组织内的知识资本--这些有价值的知识，以往被埋藏在电子媒体、印刷文件和书本，以及人们"脑中"的知识，并在需要它的地方和时间都共享这些知识。这些有价值的资源可以记录下来，例如将流程,实务手册储存在电子媒体；或储存在人们的大脑中，将有共同兴趣的群组连成网络社群。这种方法使得在"实践社群 (communities of practice；CoPs)"中共享的知识能在需要时发挥其作用。

2001-4-13　　来源：企业资源管理研究中心网


Notes的知识管理

    知识经济是以智力、技能资源为主要依托的经济,是用知识创造财富的经济。知识经济社会的基础是成千上万个具有知识经济特征的企业。这些知识型企业的成功发展、兴旺发达主要取决于这些企业的知识管理。

    一、知识管理的概念和内涵

    知识管理(knowledge management)是一个新的产业行话,是Lotus公司多年客户的实践经验的总结。这就是,当一个企业或公司把知识管理工作定位在商业战略的关键部位上时,它就能取得成功,并得到最大的回报。在Lotus于1998年1月发表的"Lotus、IBM和知识管理"战略白皮书中,把创新、反应能力、生产率和技能素质作为特定商业目标和知识管理的基本内涵,以帮助公司自身适应知识管理的活动要求。
    1. 创新(Innovation)
    知识经济的生命力在于创新。在以技术迅速变化和产品周期不断缩短为特征的商业竞争中,创新往往是保持长久竞争优势的主要源泉。为此,要经常鼓励和培育新思想、新主张,最大限度地把企业员工聚集到献计献策和通力合作的论坛中来,共同创建新的产品和服务,把创新作为协作技术的一个关键目标。
    2. 反应能力(Responsiveness)
     今天的商业环境中经常出现无法预测的事件,如某个洲金融危机的发生,某项惊人技术的发明,某个非传统竞争者的出现等。为了把突发事件对自身的影响减小到最低限度和更快地解决客户提出的问题,以便帮助客户作出最好的决断,就要求企业能对市场的变化作出快速的反应,具有较强的应变能力。
    3. 生产率(Productivity)
     获取和共享最好的经验以及可重复使用的知识资产,以便缩短循环时间,并最大限度地减少重复劳动。生产率取决于对个人和群体创造的知识加以收集、综合并提供给其他人再利用的程度。知识管理技术必须向个人提供借以发现、挖掘和优化已创造的共同知识的工具,并把它们应用于新过程,解决新问题。
    4. 技能素质(Competency)
    一个企业要保持竞争力,就必须提高现有员工和新雇用员工的技能素质和行业规范。为此,要通过在职、在线培训、远程学习和企业网资源浏览,学习新知识。一个企业或公司如果全力支持和促进这种提高员工技能素质的学习,并使之制度化,就是成功的知识管理。

    二、Lotus的知识管理框架

    不难看出,Lotus的四个商业目标要受两个因素制约:一个是企业的规模大小,另一个是企业的协作程度。如下图所示。
    组织规模小,对企业员工的技能素质和创新要求就低;一个企业的协作程度低,对企业的生产率和员工的技能素质要求就低。另一方面,一个企业的组织规模大,则对企业的生产率和反应能力要求就高;一个企业的协作程度高,对企业的创新和反应能力要求就高。这样,企业的组织规模和企业的协作程度就形成一个二维函数的坐标系,而四个商业目标即知识管理的内容分别是该直角坐标系平面中的四个部分。整个坐标系构成了Lotus知识管理解决方案的框架。
    1. 协作程度(Degree of collaboration)
     创造、共享和使用知识的过程包含着各种程度的协作。四个商业目标中的每一个目标,在实现过程中也包含着不同程度的协作。一般来说,凡与技能素质和生产率有关的知识活动,其总体协作程度均较低,如个人学习。相反,与创新和反应能力有关的知识活动具有较高的协作性,如献计献策会。
    2. 组织规模(Organization scale)
     知识管理活动及其产出的多少可以通过整个组织来调整和控制。例如,典型的技术素质培训(如员工参加讲习班和培训班等)和创新总是在小规模的个人或工作组一级进行,他们所学的并非是能很容易地为全组织所用,只有当个人学业的产出或一项创新经过包装后再次使用时,其成果才能为整个组织所掌握和使用。在一定组织规模中重复使用个人和群体所创造的知识,才能在生产率和反应能力上产生积极的效果。
    框架的四个部分之间存在着相互依存的关系。没有技能素质的个人,就难以实现创新、生产率和反应能力。同样,创新、生产率和技能素质又是反应能力的前提,但四个部分之间又不是完全相互依存的关系。一个公司并不需要在技能素质方面实现完善的知识管理后才能从事创新和提高生产率,或者在其它三个方面都达到最佳状态之后才能具备上佳的反应能力。一个公司可以对任何一个方面进行少量的投资,以便在短期内收到明显的回报,但这不意味着在其它几个方面知识管理已完美无缺。

    三、Lotus知识管理的成功范例

    Lotus的知识管理解决方案广泛应用于各类客户的应用中。这些范例的共同点都是在 Lotus Notes知识平台上实行知识管理的。这些企业客户把Notes技术具体运用到知识管理上。
    1. 巴克曼实验室(Buckman Labs):技术素质
    巴克曼依靠高技能素质的员工在特殊化工产品市场中保持领先地位。它的竞争力来源于所拥有的高技能素质的员工,这是该公司针对工业的最新发展和客户的营业情况不断对员工进行有针对性的培训和教育,借以激励员工不断寻求机会的结果。
    巴克曼实验室应用Lotus开发的远程学习解决方案的软件包Learning Space,在不影响工作和个人生活的情况下,把培训安排得恰到好处,使员工能真正学到最多最实用的东西。
    Lotus的体会是:在提高员工技能素质中要方便学生,要有一位教师,能充分地进行相互交流,要给个人一定程度的自由活动空间,营造一种舒适活泼、开朗坦诚的气氛和环境。
    2. 蒙桑多生命科学研究所 (Monsanto Life Sciences):创新
     该公司的成功归于有创新、可增值又能获得专利的产品。这是该公司对研究和开发进行大量投资的结果。该研究所所聘用的员工都是受过高等教育,一般是一名与世界各地的科学家和研究人员经常有接触的博士。该研究所以发挥高技能专业技术人员的才干为手段,开展以创新为中心的知识管理活动。它通过举办电子技术论坛, 协助员工交流思想 ,为创新提供了有利条件。
    该公司应用了Lotus开发的Solution Space,它是一个以Notes为基础的正在研究与开发的用于支援团队、部门不断创新的项目。
    Lotus的体会是:凡是能不断成功创新的公司都有一定的做法。团队协作成员要尽可能地多出好主意,多提好办法,从中选定最有可能取得成功的办法。如果有人主持或负责献计献策会,而且与会人员相互熟悉,讨论就会更加坦率,并会有更多的相互交流。
    3. 安德森咨询公司(Andersen Consulting):生产率
    管理咨询公司是知识管理的广告人。在这个领域的竞争是直接在知识基础上的竞争 :他们知道些什么?如何与客户共享这些知识?
     要使其知识库中的知识有效并保持更新,安德森咨询公司制定了专业知识人员的岗位要求,这些员工是各主题的专家,由他们对文档进行筛选、分类和整理,剔除重复和过时的内容,以确保其质量、适应性和推广价值,并使有价值的珍贵资料容易为人们所查询。这样,使人们不仅能得到而且能重复利用公司最新和最有用的知识。安德森咨询公司把这种作用称之为知识服务员(knowledge steward)。
    该公司应用了TeamRoom,它是Lotus已经开发的一个用于建立协作过程(从制作、行销到完成当前任务)的软件包,也是用于讨论的Notes数据库的下一代产品。
    Lotus的体会是:知识库只有在使人们易于获取有用的知识时才是好的。为此,就要对资料不断进行分类,经常显示有价值的内容,还要对图书馆不断进行整理。
    4. 英国石油公司(British Petroleum):反应能力
    英国石油公司在北海建设石油生产平台,集中了自己的专家和大批建设分包商集体的智慧和知识。为了应付不测,该公司使用了一种以Notes为基础的工程项目管理系统,它允许各方提出进度报告,判定瓶颈所在,对各种热点问题(如因天气造成的误工、紧急接送员工到达和撤离建筑工地等)。但是,项目管理并不等于知识管理。英国石油公司发现,以N otes为基础的项目管理数据库起到了对专门知识进行详细分类编目的作用。在发生不测事件时,公司就使用员工和承包人花名册,一张"专门技术人才查询图 ",就可以迅速确定该谁(如经理、地质专家、财务人员、领班或渡轮人员等)去应付这一不测事件。还可以通过电视会议迅速地在恰当的时间召集恰当的人员,并通过媒体提供尽可能详细的信息,妥善处理好紧急情况,而不影响紧张的工作安排。
    "专门技术人才查询图"的概念是以Notes为基础的专家网络(Expert Network)软件包的核心。
    Lotus的体会是:第一,效益在于不仅要得到文档资料,而且要知道在作出决定时,哪些是可用的恰当人选。一张"专门技术人才查询图"是一笔无价的知识财富。
    第二,真正的合作将大大增加项目参与人员共享知识的价值。它可以创造信任,更加充分地交流某些信息,并加快实现协商一致的过程。英国石油公司的经验对于不把知识管理固定于某一组织,而是通过协调多家公司的力量使自己特别获益的一个成功范例。
    Team Network是Lotus正在研究的一个项目,用于支援多个团队的工作,也会对管理多个团队的经理给予支援。

    四、知识管理平台——Notes

    1. 优势
    几乎所有Lotus的客户都把Notes作为其知识管理的最佳平台,这是因为:
    · Notes有知识管理的良好要素;
    · Notes文档便于管理和查询;
    · Notes所获得的知识易于销售和保持更新;
    · Notes提供各种层次的保密;
    · Notes知识管理方案与其它解决方案相结合是其结构的关键;
    · Notes应用软件使知识流动成为行动的依据;
    · Notes是一个单一体基础结构,无需装配。
    2. 扩大Notes的知识管理结构
    · 新的Notes性能使知识管理更易成功;
    · Notes可在某一时刻对打开的所有文档进行检索;
    · 可以给用户一张"道路图",引导用户进入不同的主题领域;
    · Notes对新来的信息实行优选;
    · 新的应用格式使公司明确哪些知识有用,哪些知识已经过时。

　知识管理曾经被IT供应商和咨询师许以过高的承诺，其实却总是被他们弄得一团糟，以至于现在很少人仍然相信它。一些 实行知识管理的先锋，如可口可乐公司已经终止了他们的知识计划。我们并不能指责这些对知识管理失去信心的人或者公司，因为权威的调查证实了一个令人不安的 趋势:通过对108家公司的研究，他们发现那些主动进行知识管理的公司与其财务回报之间并没有任何联系。

　　然而说到底，知识管理不仅是竞争优势的来源，而且也决定了如何对价值链的重组。而那些保守的人，任何时候都会像在19世纪抵制新机器使用一样抵 制最佳做法和新软件的运用，即知识管理。但是，知识管理也有其自己的特殊要求，如何使知识管理产生切实的成果并不是一件简单的事。知识有各种形态，它可以 嵌入到设备、工具、流程以及聪明人的头脑中，并使他们都做得更好。知识的外在形态并不重要，从知识运用中所学到的经验是相同的，与知识的外在形态并没有联 系。

　　不管是什么知识形态，要获得成功的规则都是类似的，你必须做好两件事情:

　　第一，尽力共享最佳想法、促进同事之间的对话与交流以及给公司的每一位员工都提供他们工作所需的知识。这是人们常说的知识管理的主要内容，它很容易说到，但要真正做到却很难。

　　第二，要主动地去寻找和运用这样一些知识，它们能够极大地而且不断地增进公司为客户服务的能力。知识管理的范围可以大到像通用电气所使用的一些 方法和理论，如根据需求来生产、六西格玛质量管理，也可以小到像英国石油与Schlumberger合作开发新的水平孔钻探技术。

　　千万不要以为你能够通过上面两项工作的任何一项就能获得成功，如果你跳过其中任何一项工作，你就会坠入无法真正改善财务底线或者无法改变公司文化的境地。传统的知识管理可以帮助促进公司文化的变革，而你也更需要注重产生成果的驱动力，这种成果将确保最终的变革。

　　知识管理的理论基础其实很简单:如果你让每一位员工都能够得到另外的人所掌握的知识，并且得到他们完成其工作时所需要的核心内容和信息，那么每一个人的决策都会更优，公司运转将更为健康更有效率，而每一位员工也会更快乐。

　　当然，真正要做到并不简单。因为知识管理要求人们做他们并不熟悉的事情:首先，它要求人们将其最好的创意无私地与他人共享，放弃一定的个人竞争 优势，并且往往得不到任何奖赏;其次，知识管理也强迫人们运用其他人的知识，这意味着承认一些人比你知道得更多;最后，知识管理要求人们不断寻找改进的方 法和途径，即今天最好的工作方式永不会是明天最好的。知识管理要求人们每天都持续不断地向他人学习，同时也要求为他人无私地贡献知识。

　　知识管理要求有合适的流程和基础设施确保把正确的知识和信息在正确的时间输送到正确的地方，这包括人员也包括IT。知识管理还需要一个变革程序 以激励从业者之间互相合作。注重主动地去寻找能够极大地而且不断地增进公司为客户服务的能力这一点似乎与传统的知识管理大不相同，因为他们看起来是功能性 的，但是这种专注于成果的努力是而且总是与知识相关，如六西格玛方法不过就是关于改进质量的知识。

　　不要浪费时间企图去创造你所需要的持续改进的知识，你完全没有必要去重新发明轮子，你应当听取杰克?韦尔奇的建议"剽窃"。在商业领域，赢家总 是运用任何合适自己的方法或者理论，而不会也没有必要运用完全是自己发明方法或者理论。这种可能性非常大，即在某地的某个行业的某个企业已经发明了做某个 事情最佳的做法。通用电气的经理们总是在其客户、供应商以及合作伙伴那里寻找更好地做自己工作的方法。

　　最后，要使知识管理得以成功实施，公司的首席知识官必须对知识、对激励人以及对追求成果都充满热情。这三方面的热情缺一不可，首席知识官在知识管理逐渐失去它美丽的光环的时候，要仍然坚信它，他们应当成为一流的激励大师并且永远保持对切实成果的热望和追寻。