The Semantic Advantage

March 13, 2010

Putting meaning to work

Filed under: knowledge work,Uncategorized — Phil Murray @ 3:30 pm

KMWorld [finally] published my article, “Putting meaning to work.” See

It begins ….

Committing vast resources to the “fragmented and miscellaneous” aspect of our Internet-driven economy is a deer-in-the-headlights reaction to the superabundance of information. That reaction might be unavoidable, but it is also unfortunate, because information—and, in particular, unstructured content—is a surface characteristic of knowledge-based activities, not their essence. Focusing exclusively on new ways to handle or respond to the superabundance of information distracts us, ironically, from solving the most important problems of the Information Age.

Let me know what you think.


December 7, 2009

Resisting the hive mentality

Filed under: knowledge work — Phil Murray @ 6:53 pm

We certainly need better ways to find expertise in organizations, but we should carefully consider the implications of HiveMind and other technologies that look at the surface manifestations of behaviors rather than at the actors and activities themselves.

From the BinaryPlex website:

We’re building a product called HiveMind that helps you know what knowledge and expertise the people in your organization are demonstrating, without them needing to update a manual profiling system. Our philosophy is to manage information on behalf of people instead of adding to the flood. We call this “People Centric Software” [emphasis added].

NO, it’s not people-centric. It’s information-centric. It does what it does based on information, in the same way that you can learn things about bees and ants by converting their movements and interactions into information and interpreting that information.

But people aren’t bees or ants. The core problem, for both managers and individuals knowledge workers, is that the knowledge-based organization quite literally does not know what its members are doing. It does not know (or have a record of) who is engaged in what activities with what tools. It does not have an accounting of the inputs and outputs of those activities. It does not track — except in formally organized projects or processes — who or what processes are the beneficiaries of those activities. This is an astonishing reality accepted as par for the course, a level of ignorance that would be considered grounds for immediate dismissal in a manufacturing environment.

An information-driven tool is a poor solution for this problem. Consider the following:

  • A solution like HiveMind replaces analysis of work (what people actually do on a daily basis) with guessing games. That doesn’t seem like a great organizational policy, especially when it is possible to know what they actually do. A well-conceived analysis of work activities does not have to be intrusive, time-consuming, or static. It can be helpful to individuals themselves, to managers, and to the organization.
  • The HiveMind solution asserts a top-down association between language (vocabulary) and skills or job roles. I’m sorry, but pairing a language-based solution — even one supported by well-designed ontologies — with static and/or highly conventional descriptions of work activities is going to produce only a marginal advantage. What’s more, many (maybe most) productive work activities occur at a finer level of granularity or specificity than “skill/expertise” or “job description.”
  • Like other applications that skim large amounts of information for certain kinds of facts or for consumer sentiment, an algorithmic analysis of information about skills is a “derivative” instrument. As we have seen in the marketplace, derivatives are often accorded the highest value, even though the information on which they are based is farthest from reality. And we know what happens when derivatives drive mind share and management. This is not a stretched metaphor. Meaning connected to reality is the source of value in knowledge-based organizations.
  • The worst possible situation is one in which a solution seems to make sense, especially when it grabs the imagination but is actually deeply wrong-headed or distracting. I think this one falls into the latter category.
  • All practices and technologies that create, consume, or process information — especially the language we use to communicate meaning — ultimately have a deep impact on how we work. You have to work out the implications before you adopt those practices and technologies.

Do you really want to encourage a hive mentality? A hive perspective? Do you really believe we behave like bees? That our individual acts have value only when they are summed? [paranoia alert] I believe that just the opposite is true, but there are people who want us to believe that because they know how to steal some of that value from us.

November 15, 2009

An old favorite plus a new favorite = solution

Filed under: knowledge management,products for semantic approach — Phil Murray @ 3:03 pm

No rants about search engines this week. Instead, praise for a terrific desktop search engine — dtSearch — and ABC Amber SeaMonkey Converter, one of many converters offered by Yernar Shambayev’s Process Text Group.

Online searches lead mostly to … more online searches instead of to reusable value. But we can’t live without them, and I have to admit that Google and Yahoo! are steadily improving the effectiveness of their products. However, sometimes our needs are more narrowly defined than locating something in all the world’s information.

When I’m building a network of knowledge using the approach I have designed, I need to know whether the idea or concept I want to add to the network is the same as — or similar to — other ideas or concepts already in the network. Let me stop for a minute and define idea as an observation about reality — the equivalent in meaning to a simple sentence in natural language. Contrast that with concept — the essential name of a thing, whether material or imagined. Concept appears to be the preferred terminology for practitioners who construct taxonomies (or facets), thesauri, and ontologies that organize such entities into larger structures. I won’t go into the fine points here.

I have not built a rich ontology of the concepts in the ad hoc spaces I discuss, and I haven’t found any affordable tools that allow me to look for similarities among ideas. So I resort to a very simple practice: I maintain a directory in which each idea and each concept occupies a separate file. The file contains the name of the concept or idea, explanations of those items, and text examples that contain instances of those items. A full text search of that directory using the new concept or idea as the query retrieves the search engine’s best guess at files that contain similarities with concepts and ideas already in the network of knowledge.

Or not. Because most search engines are primarily string-matching tools, and the files retrieved may not be what I want.

dtSearch is better than that. In addition to the features you might expect in a good or desktop enterprise search engine — including stemming, wild cards, fuzzy search, proximity search, and Boolean operators — you have the option of looking for files that contain synonyms based on Princeton’s WordNet — a kind of semantic network that anyone can use. So even if you can’t keep track of synonyms, the dtSearch tools will. You can add your own synonyms, too, within dtSearch.

Great stuff. Some consider the dtSearch interface dated, but I think it’s highly functional. Real easy to set up separate named indexes for different sets of directories, too. (Excuse me. I’m dating myself. We call them “folders” now, don’t we?)

I also use dtSearch for a variety of other search tasks — including finding emails from the thousands I have captured in SeaMonkey. Making those emails accessible in a reasonable (and, ideally, consistent) way has been virtually impossible. The native SeaMonkey search features — like those in other email clients I have encountered — are simply inadequate.

And even if those email search features were superb, they wouldn’t solve the problem, because SeaMonkey stores each mailbox as one big file. I do mean big for some of my mailboxes. So finding a huge file is almost meaningless. Big files will satisfy many queries unless you use proximity searches and other tricks, and even if one mailbox does contain the information you want, it may take a long time to find the right spot within that file. And you have to go through the same process if you want to execute that query again.

ABC Amber SeaMonkey Converter solves that problem by allowing me to split SeaMonkey mailboxes into separate HTML files. (I could use ABC Amber options to convert them to text or a couple dozen other output formats, but I prefer HTML for a variety of reasons.) When I use a dtSearch query against the directories containing those exported HTML emails, I get a highly relevant selection of small files — exactly what I want.

Very easy, too. When I ran ABC Amber the first time, it found the SeaMonkey mailboxes automatically. The emails in each folder were displayed in a list, and you can easily select as many or as few as you wish. Oh, and I should mention that ABC Amber promotional pages stress the ability of the converter to output a single, integrated HTML file from a mailbox. That’s a plus for many people, but not what I want.

I also tested the mailbox-to-TreePad converter. (You just click a different output option in ABC Amber.) The results were flawless and the TreePad outliner let me explore view the email content by date. Cool.

One caution: As of this writing, it appears that SeaMonkey has changed where it places email folders. So folders I created with SeaMonkey 2.0 — and any new email since the changeover — did not show up in the ABC Amber converter, but I was able to redirect the program to the new location using an ABC Amber option. I have advised the Process Text people about this.

UPDATE (16-nov-2009): The ProcessText people have already updated the converter. It now finds the SeaMonkey 2.0 mailboxes automatically. That was quick!

I’ve been using dtSearch for nearly a decade now. It’s still worth the money — about $200 for an individual license. Adding ABC Amber SeaMonkey Converter (about $20) to my set of tools will really make a difference.

February 5, 2009

Reducing dependence on tacit knowledge

Filed under: knowledge management,semantic technology — Phil Murray @ 7:33 pm

Much is made of the importance of tacit knowledge — which might be loosely understood as “things you do on autopilot” or highly internalized experience that can be applied in work situations. Examples of the value of tacit knowledge might include the stock trader with 10 clients on the line or a nurse practitioner making rapid decisions about the status and treatment of a distressed infant.

You’ll see references to the importance of tacit knowledge everywhere you turn. (In my experience, nearly everyone without a background in “knowledge management” who becomes interested in KM latches on to this idea uncritically.) One rationale for this mindset is the generalization that you can’t really capture knowledge in an explicit or formal way. That is usually combined with the assertion that these skills are the most important skills in an organization — not all that trivial explicit knowledge (articulated knowledge) stuff (which anyone get his or her hands on).

(BTW, everyone seems to claim that everyone else is misinterpreting Michael Polanyi’s tacit vs. explicit distinction. [See, for example, the Wikipedia entry on Tacit knowledge] I simply don’t care. Argue among yourselves and don’t send me any nasty pedantic emails on the topic. I use the distinction in the way described above. And please don’t send me your favorite definition of knowledge.)

Sure, we depend on tacit knowledge in many cases where we are applying knowledge to work. But overemphasis on tacit knowledge as a business strategy or vital business practice is fundamentally wrongheaded and counterproductive.

  • What is tacit for one person may be very explicit for another. Part of the problem today is that, as individuals, we are forced to deal with a much wider range of situations and conditions than in the past. There are so many more things that touch our jobs and so much more information about those things is readily available to us. But someone, somewhere has in fact explicitly represented much of what we as individuals deem “tacit.”
  • A corollary: Examined closely, any particular skill that depends on highly internalized information may turn out, in fact, to be easily represented not only explicitly, but also very formally. Knowledge engineers — in the traditional sense, creators of expert systems — have demonstrated this to be true in many cases.
  • The dividing line between internalized “knowledge” and information is very fuzzy. These days, nearly every application of knowledge to work is deeply dependent on explicit knowledge and information.
  • The emphasis on tacit knowledge is fundamentally elitist … and shortsighted. The working assumption is that those who already demonstrate or are capable of demonstrating superior skills in an activity deserve more attention. This attention and investment in time and money may actually be counterproductive, because when the expert walks out the door, so does his knowledge. People who are deeply committed to improving their knowledge and skills will do so anyway, assuming you let them. Those who do not have that drive for excellence and improvement aren’t going to be prodded like cattle into improved learning and better behaviors.
  • The “tacit agenda” heavily emphasizes the role of learning in an organization. But I agree with my friend Jim Giombetti that focusing on learning — enhancing the knowledge of individuals — is fundamentally a bad investment for enterprises, especially if it comes at the expense of more thoughtful approaches to making knowledge work more effective. In general, you simply don’t get a good, predicatable return on that investment.
  • Tacit knowledge simply doesn’t apply in some situations. Lately I’ve been listening in on the NASA/Ontolog conversation about Ontology in Knowledge Management & Decision Support (OKMDS). The diverse and distributed community in this discussion can’t depend in any significant way on tacit knowledge. That is probably true of many enterprises and communities of practices as well. (The large pool of experts in IBM comes to mind.)

Don’t get me wrong. The last thing I want organizations to do is to chain experts to desks and make them write down their “knowledge” in formal ways. By the time they finish doing so, the world has changed. And it’s simply impossible to treat this kind of knowledge capture as a manageable top-down enterprise activity.

But it is vital, IMHO, to pursue ways of converting what we know as individuals into what is useful for others in the organization to know. Technology and new thinking about knowledge work will help us do so.

December 12, 2007

Fun with technology haters

Filed under: knowledge management,semantic technology — Phil Murray @ 8:47 pm
Tags: ,

Recently I posted an extended opinion to the OKMDS (Ontology for Knowledge Management and Decision Support) forum (okmds-convene) about paying more attention to structured support of discussion as the source of good decisions in enterprises … and less attention to computer ontologies. (The full post follows.) I made the unpardonable mistake of asserting that such discussion can and should be supported by technology.

Well, the technology-haters came out of the woodwork to complain about the pitfalls of technological approaches to knowledge management … making posts to the forum [perhaps] written in a word-processor, sent via email, organized and published via Web-supported forum technology, and viewed by more than a few people in their Web browsers and email clients (or via an RSS feed). I’ll bet some of them saved the thread on their desktops where it was indexed automatically with Google Desktop or another desktop search engine.

I was warned about the dangers of “throwing technology” at a problem, of confusing knowledge management with the tools used for it, and of being biased by my lurid obsession with technology (“If you’re a hammer, everything looks like a nail.”).

Umm, er, has everyone forgotten that people identify their business needs, then choose, deploy, use, and manage the technologies to address them? I’ve noticed that my Nissan Murano works fine most of the time, as long as I put gas in it and avoid large trees. I was aware that it didn’t get 40 mpg when I bought it, but it was big enough to haul the stuff and people I needed to transport. And it satisfied my vanity, too.

On the other hand, has everyone forgotten that designers and developers of KM technology were inspired by work problems. (Well, OK, there have been more than a few who see the world as a Matrix-like playground.) Did their resulting creations provide good solutions to those problems? Not always. Do they make bad assumptions about how their solutions will be adopted and applied? Not infrequently. But given a choice between listening to delirious rants about epistemology and listening to how a technologist addressed a real problem, I always tune in to the latter … when I’m looking for answers that work.

The irony is, perhaps, that I’m not a technologist. Never wrote or sold software. Sure, I’ve written a few small Perl scripts. Wrote SGML DTDs and XML Schemas. Served up tons of documentation on computer software. Used hundreds of different applications for creating, managing, organizing, and publishing content. Examined hundreds more software applications for KM requirements, but rejected most of them as inadequate for those purposes.

I’m not ashamed of my technology bent. Many aspects of “managing knowledge” are potentially improved, if not always solved, by technology:

  • Providing scalable solutions in general.
  • Capturing and integrating the enterprise’s “knowledge.” (I’ll admit that it’s not “knowledge” in the dictionary sense. Then we get into another endless argument about “tacit” and “explicit” knowledge. I don’t want to go there, either.)
  • Serving as a collective memory.
  • Enabling detection of patterns and relationships in communications that are effectively undetectable to individual human brains.
  • Observing and quantifying the flow of information among people in organizations. (Think Social Network Analysis.)

And that’s just the short list, of course. You can’t make judgments about the effectiveness of technologies you don’t know about … or which don’t exist yet.

Folks, ya gotta examine the needs first … from as many different perspectives as possible. Even, God forbid, a “people-oriented” perspective. But first of all from a business perspective.


As a relative newbie to formal ontologies, I apologize in advance for imprecise terminology. You did ask for input from the KM community, right?

The Ontolog forum recently featured a wonderful discussion between John Sowa, Leo Obrst and others on standards for ontologies and the utility of ontologies in enterprises. (Ontolog forum, 17-nov-2007) The two distinguished experts disagreed gently on the topic, but it appears that they agree ontologies are important for solving enterprise problems.

I have to object … also gently … and with qualifications. The distinguished members of the ontology community have demonstrated that ontologies and other technologies that have evolved from the study of logic and language can be applied successfully to data-mining, interpretation of natural language, and even situation awareness requirements in military scenarios, but the recent buzz about ontologies is — like the recent buzz about metadata in general — largely a reaction to the superabundance of unstructured information. From the perspective of what enterprises need in general, this emphasis seems to be an overreaction.

I do believe that ontologies in general and the Semantic Web in particular will play huge roles in “semantic” aspects of business activities. However, overemphasizing ontologies and metadata distracts us from the most common enterprise activities: the core processes of building an opportunity (or a domain) and successfully managing the enterprise. In the knowledge-based enterprise, those processes consist primarily of identifying and evaluating facts and conditions — statements about market and physical realities. An enterprise or domain — for example, the extended NASA community — is the sum of all the conditions and responses for that enterprise.

Stated in a somewhat different way, decisions and and implementation particulars grow out of evaluation and acceptance (or rejection) of ad hoc assertions. By ad hoc assertions I mean statements that identify an opportunity or situation — for example, “The presence of Chinese and Japanese satellites around the Moon is a threat to our pre-eminence in space exploration.” Or “Nanotubes based on composites are the best choice for building a space elevator.”

By evaluation of those assertions, I mean such statements as, “This assertion is relevant (or valid).” Or, “This assertion must be expressed more precisely.” Other evaluations may consist of identifying (and quantifying) the impact of a set of assertions A on assertion B — an analog to applying If … andIf … Then logic to conditions in programming. But I’m not talking about precise programming statements. Evaluations are (or can be) collective, quantitative, “fuzzy,” qualitative, and/or arbitrary.

No matter what our particular role in a knowledge-driven organization may be, we communicate and evaluate assertions on a daily basis — in meetings, casual encounters, emails, personal note-taking, forums, and documentation of many types. These activities of the organization are frequent, pervasive, and vital to successful decision making and execution. In spite of that, there is no mandate to apply technology or business practices to making these activities more effective.

That’s unfortunate. Assertions and the evaluations of those assertions can be represented explicitly. They can be known — expressed as structured objects. They can be supported directly by technology, management practices, and education of workers in semantic principles. Decisions can be traced back to the conditions/assertions that influence those decisions.

Objects of this type and relationships among those objects can also be visualized easily. I am aware that software applications for specifying, visualizing, and evaluating assertions do exist. But those I have seen (like the Compendium Institute’s Compendium hypermedia tool) seem fundamentally disconnected from goals of precise representation of the meaning in natural language. They lack methods of formally representing assertions as objects that can be addressed with multiple tools, or they simply don’t scale well. Others simply don’t make the distinction between assertion and evaluation of assertions at all.

Supporting these core semantic activities of the organization should drive how knowledge-development, knowledge-representation, and information-management technologies are selected and implemented in enterprises — not vice versa. Similarly, the goal of enterprise strategies and personnel-management tactics should be, first and foremost, to make these core semantic activities as effective as possible.

Re-centering our focus on assertions and evaluation of assertions provides several important advantages:

  • Participants in the enterprise can deal with such assertions directly. Average Joes and Janes have a fundamental grasp of what constitutes an assertion — although they will need help in framing and evaluating assertions in a structured way. (It’s a lot like parsing a sentence. Not everyone’s favorite activity, but a lot easier than grasping subtleties of inheritance of semantic properties in ontologies.)
  • Participants can “see” the impact of their participation. Feedback is vital to participation. And a record of decision making can be kept and analyzed.
  • Evaluations can be weighted. An approximate sum of the evaluations of the quality of assertions — evaluations contributed by multiple stakeholders in the enterprise — should point to good decisions, especially as the number of relevant assertions and evaluations grows.
  • Participation in gathering and evaluation of assertions can be a source of objective information in performance evaluations.
  • Assertions become product. If you’re a software developer, you can see specifications emerge from assertions. (Currently, implementers have to leap that great chasm between unstructured descriptions of functionality and structured modeling of processes.) If you’re a tech writer, you can see that the rationale for features and functionality — and sometimes the behavior of features themselves — is captured in the assertion/evaluation process.

I want to stress that I’m not dismissing the importance of ontologies. Among other things, ontologies should support interpretation and management of assertions and evaluations. But we need to take a step back and re-center our pursuit of effective solutions for the challenges facing knowledge-based organizations. We have to ask,

  • What matters most to the participants in an organization?
  • What explicit information or “knowledge” most directly affects understanding and successful decision making?
  • What information is most directly relevant to a broad cross-section of people in the organization?
  • What information can people react to or evaluate with minimal education and effort?
  • How, in general, can participants most effectively contribute to improvement of the information that leads to success?

If this is yet another spin on the issues, I hope it is at least a more positive spin.

Phil Murray

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