An old favorite plus a new favorite = solution

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.

Using circles and arrows

Many “semantic” practices and applications — including “brainstorming” and construction of computer ontologies — involve the use of (a) circles or other symbols (“nodes”) to represent concepts or ideas and (b) arrows (connecting arcs or “edges”) to represent the relationships among the concepts or ideas.

(Tim Berners-Lee uses the phrase “circles and arrows” in at least one of his papers: “The Semantic Web starts as a simple circles-and-arrows diagram relating things, which slowly expands and coalesces to become global and vast.” in “The Semantic Web lifts off” by Tim Berners-Lee and Eric Miller. ERCIM News, No. 51, October 2002. http://www.ercim.org/publication/Ercim_News/enw51/berners-lee.html. His original vision is for metadata for documents.)

The graphic representation is not the tool itself in some cases, but a method of helping users visualize and/or manipulate complex, abstract data that is difficult for the average human to understand quickly — for example, RDF expressed in XML.

Mapping arguments on a whiteboard in support of decision making is a common practice in many meetings. (But integrating those representations into subsequent discussions is almost always a challenge.)

We need a much better and more widely usable set of tools for such purposes, but just applying current, limited tools is useful in its own right. One thing you definitely begin to understand as you try to deconstruct your arguments into meaning — especially when using graphical tools for that purpose — is that the process itself is useful in getting to meaning.

The process is useful in exposing what is tangential, peripheral or simply irrelevant. You tend to create and refine elemental, focussed, unambiguous assertions that can be verified as true or debunked.

You certainly expose conditions and constraints that apply to those assertions. Sweeping generalizations quickly become far less general … but often more useful. And you find that most of what you have written is not part of the core meaning that you want to represent and transfer.

That process, however, is still not easy. And you need to have a set of guidelines to keep yourself on track.

I will explore some of the tools and issues in this area in future posts.

The problem of situating ideas

I have papers scattered across my office. Some are printed documents filled with marginalia. Some started blank and are now filled with isolated observations. Some of those observations are in the form of sentences written in small blocks at different angles on the page or enclosed in circles or rectangles linked to to other blocks by curved and straight lines. Some are post-it notes inserted into books I’m reading.

I have a stack of spiral-bound notebooks I use for taking notes at meetings. My notes and comments are liberally interspersed among those notes. Some pages are filled with notes and comments by themselves.

My computer files contain notes in at least 10 different formats (right now — a system for building help files, five (maybe six) different PIMs, outlines made with TreePad, files in Open Office Writer, HTML files created with Sea Monkey, emails and HTML files exported from email, and text files created in Notepad++). Some of the products I’m reviewing contain notes and ideas locked in those particular tools.

Other ideas are scattered across the Web in wikis, blogs, and several web sites.

Let’s face it. I have a problem. Those ideas are not “situated.” They have little or no context. I don’t know — or at least I cannot demonstrate — how they are connected and where they overlap or duplicate each other. And that’s a problem, because I certainly don’t remember most of them.

I have no way around one of the roadblocks to improving this situation: Sometimes I can’t easily record those ideas on a computer. It’s just inconvenient.

At other times, using the computer just seems inappropriate. (Yesterday, I wrote six pages of notes on paper about using Ron C. de Weijze’s Personal Memory Manager (PMM) — a tool for capturing and integrating ideas on your computer! –while sitting at my computer … with PMM open.)

I do go back periodically and try to capture some of the stuff on paper, putting large X’s through notes that I have transcribed. That helps a bit, but it doesn’t connect them. It does little to make their meaning explicit or trace their impact on other ideas. It does nothing to aid in finding the other contexts in which this idea may have occurred.

I have long railed against trapping ideas in formats that makes them effectively not re-usable. That’s a problem with most concept-mapping tools and PIMs — even those that support export to Web formats. I really thought I could solve my problem with David Karger’s Haystack or the NEPOMUK semantic desktop, now being commercialized by (or as) Gnowsis, but I found them clumsy, incomplete, or lacking support.

But the problem of situating those ideas has become so great — and the value of connecting and superimposing stucture on those ideas has become so obvious — that I am giving up (for at least a while) my insistence on making everything (including relationships) convertible to RDF and XHTML. Or DITA.

So I’m going to try to live in the proprietary world of Personal Memory Manager for a while. “Try” is the operative word. And I will do so within a set of constraints, including continuing to create content in HTML — XHTML as much as possible — and referencing those files in PMM, rather than embedding them solely in PMM.

What is the Semantic Web all about?

Best quick read on the Semantic Web I’ve seen in a long time — by James Hendler, an expert and innovator in computer ontologies himself:

What is the Semantic Web all about?

Hendler was co-author of the Scientific American article that was primarily responsible for bringing Tim Berners-Lee’s ideas to a much broader audience, so he brings much more authority to his observations than, well, just about everyone else except Tim B-L himself.

Among the best information points and assessments in the brief post:

• A brief description of his own work on Simple HTML Ontology Extensions, which preceded formalization of the Semantic Web.
• A very brief history of the Semantic Web itself, including its DARPA funding.
• His distinctions among “linked data,” “Web 3.0,” and the “Semantic Web.”

Required reading if you want to talk sensibly about the Semantic Web and semantic technologies and practices in general.

This is also a good time for me to remind people of another item on my list of required readings: Barry Smith and Chris Welty, “Ontology: Towards a New Synthesis.” Available at http://www.cs.vassar.edu/~weltyc/papers/fois-intro.pdf

Also brief and clear.

An economy of meaning. Or, why “semantics” is ugly but important.

An economy of meaning? Yep. And I mean that explicitly in the sense of economic competitiveness and socio-economic solutions that pay direct attention to meaning. Not information.

Of course, we all rely on information. Always will. But we can’t rely on information the way we once did. We might be proud of our bookshelves or our long lists of browser bookmarks, but they’re decreasingly effective in helping us solve our problems. It’s not our fault. It’s the fault of information itself. There’s just too much to handle and apply.

That is, in part, the message of the Semantic Web. And my use of the phrase an economy of meaning is closest perhaps to Ilkka Tuomi’s use of the phrase Towards the New Economy of Meaning in his presentation, “Networks of Innovation”, which focuses on new socially-constructed forms of innovation.

But a “semantic perspective” is much more than that. It’s more than just innovation. It’s not just about controlling costs. It’s about survival. It is the reason, for example, that progress in providing healthcare has ground to a halt, even slipping backward at times, in spite of rapid advances in medical knowledge and treatment technology and in spite of massive financial resources.

Together with former associates at the Center for Semantic Excellence (CSE), I have been evaluating the causes of the problems in healthcare in the United States for many months — from multiple perspectives. But it got personal recently when my wife underwent hip-replacement surgery.

• In the pre-op phase, we experienced a surgical team that operated with both efficiency and humanity … while surrounded by dozens of different technologies. (I misplaced my wife’s cane because I tried to put it someplace in that maze of technologies where people wouldn’t trip over it.) Lots of direct personal contact.
• On the recovery floor, it was often quite different. The hallways were crowded with what my wife (a medical professional herself) refers to as “COWs” — Computers on Wheels — and other technologies. The staff spent a lot of time at those laptops. Caregivers are, of necessity these days, at the beck and call of what CSE member Tom Bigda-Peyton pointedly refers to as “people not in the room.”

  My personal impression was that the less-competent and less-caring members of that staff were far more concerned with what those screens demanded of them than what their patients (and their families) asked of them. It had little to do with inadequate staffing. There seemed to be an abundance of personnel. That can be deceiving, of course, but both minor requests (a blanket for the chilled elderly patient in the next bed) and important treatment concerns went unaddressed for  far longer than seemed reasonable. The staff ignored our surgeon’s standard practices for pain management.
  
  

• At home, the first 45-minute visit by a physical therapist was occupied almost exclusively by notetaking … on yet another laptop. Not much therapy. A technician arrived to draw blood that would be used to check levels of Coumadin, a blood-thinning agent vital to safe recovery in such invasive surgical procedures. The results were available in a few hours to the doctor’s staff, but they seemed in no hurry to report those results — and deliver any changes in dosage – even with a long weekend coming up. In fact, the staff seemed rather clueless about the importance of correct and timely adjustments of the medication.

Nothing went wrong. My wife is recovering much more rapidly than expected. The surgeon did his job very well. And neither my wife nor I would want to go back to pre-technology medicine.

What’s troubling here is less the current state of care than the warning signs of problems to come: the demands of capturing information, demands that distract from properly interpreting that information and delivering the service itself; the growing role of intermediaries who have no stake in how well that service is provided (in particular, those “people not in the room”); and the continuing growth in costs, even as technology is applied successfully to specific requirements.

Those troubles are hardly limited to healthcare. And the only way to solve them is to understand how we have created, transferred, and applied meaning in these situations in the past … and how we must do so in a world now dominated by information.

New publications

I’m finally adding some of the things I’ve written over the past year to my web site. The following two papers are available at http://www.semanticadvantage.com/id22.html

• The Idiot Savant of Search
• “Sarkozy bites Obama child”: A commentary on the benefits and distractions of the Semantic Web

Enjoy. Comments are very welcome.

Mills Davis’ “Web 3.0 Manifesto”

A few weeks ago, Mills Davis offered me an evaluation copy of his “Web 3.0 Manifesto: How Semantic Technologies in Products and Services Will Drive Breakthroughs in Capability, User Experience, Performance, and Life Cycle Value.”

I jumped at the opportunity, because Davis is one of those rare intelligences who can get his arms around complex market and technology trends, providing substantive new information and helpful perspective at the same time. A friend accused him of being “too far ahead of the curve,” but I’d love being insulted like that from time to time.

In this dense 32-page report, Davis

• Differentiates semantic (“Web 3.0″) technologies from “Web 1.0″ (connecting information) and “Web 2.0″ (social computing) phases.
• Describes the link between semantic technologies and generation of value.
• Provides a graphic representation of semantic technology product and service opportunities broken down into 70 discrete “elements of value.” Each opportunity is described in the text. Some random examples: visual language & semantics, semantic cloud computing, and collective knowledge systems.
• Assesses general market readiness for semantic technologies.
• Lists over 300 “suppliers” (“research organizations, specialist firms, and major players”) in the semantic technologies space.

What does “Web 3.0″ represent?

According to Davis, Web 3.0 is starting now. “It is about representing meanings, connecting knowledge, and putting these to work in ways that make our experience of internet more relevant, useful, and enjoyable.”

What do “semantic solutions” include, according to Davis? Well, pretty much everything that isn’t structured data in the traditional sense. That’s not unreasonable, if you accept — as I do — that if you are dealing with meaning and you believe that everything is connected and meaningful, then it’s really hard to avoid semantics. And I will, once more, quote the simple but extraordinarily astute observation of Aw Kong Koy: “You can’t manage what you can’t describe.”

You may think you’re new to “semantic technologies” but you’re not. If you’re reading this, you probably use and understand relational databases. You may actually design them. And if you do, you have engaged in a form of semantic modeling for business requirements. In fact, as fellow CSE member Samir Batla (See Batla’s Semanticity blog.) observes, the idea of relational databases and the Semantic Web’s Resource Description Format (RDF) both have roots in first-order logic.

This “semantic” thing is simple, really: It’s the necessary solution to having too much information and too little time to consume it. Engineers get it. Just hand me the schematic! You can talk all you want about principles of product or building design — or even about a specific product — but I want to see how, exactly, Tab A fits into Slot B. I want the realities expressed explicitly … and in a consistent way. Tab A doesn’t fit into Slot B until that happens.

The heart of semantic technologies: knowledge representation

It’s simple, really. But that doesn’t mean it’s easy, because we’re dealing with one of the most difficult challenges facing business and computing: representing knowledge. The domain of knowledge representation has been with us for a while, and in his Manifesto, Davis clearly asserts that it is the rock on which semantic technologies rest: “In Web 3.0, knowledge representation (KR) goes mainstream. This is what differentiates semantic technologies from previous waves of IT innovation.”

But we do have to distinguish between (a) KR in the broad sense of representing [common sense] reality — as targeted by the massive Cyc ontology, for example — and (b) the practical and quite limited representations of reality that are and will be used for most business applications in the next few years, in which the representation (typically, perhaps, an ontology or simply an RDF resource) enables applications to understand each other in better (but still limited) ways by referencing a common/shared “understanding” of a narrow domain.

Sometimes the product of a KR project is a life’s work, as Cyc has been for Doug Lenat. At other times, it is much more modest — little more than normalizing and organizing a small part of a domain’s vocabulary.

The core graphic

The core graphic of Davis’ Manifesto (“Web 3.0 Semantic Technology Product and Service Opportunities”) is a quadrant of functions that follows the AQAL model — interior vs. exterior and individual vs. collective axes. (See, for example, Completing the AQAL model: Quadrants, states and types.) This quadrant-based arrangement of semantic applications is actually quite useful in getting a handle on the possible dimensions of semantic solutions, but — in spite of Davis’ high-level descriptions of each area — it doesn’t eliminate the need for more structured explanations of the application areas … let alone validate their existence. (And I’m definitely not ready to commit to the Holon/AQAL perspective on the world.)

Quadrants aside, the core objection from some corners will be that Davis includes activities and solutions that are not drawn from the Semantic Web. Well, I have two responses to that: (1) Davis is absolutely right to talk about more than the Semantic Web and (2) some distinguished folks in the semantic community — which existed long before the Semantic Web — have expressed resentment that academic inquiry into semantic approaches is increasingly limited to the Semantic Web brand. I can’t verify that this is the case; I’m just reporting what has been written by a few experts.

If I have an objection, it is that applying such broad labels to the many real and possible areas of semantic activity in business may contribute to further “siloing” of applications, one of the business problems that semantic approaches should actually help solve. Everybody wants to be a specialist, but this is a time for semantic generalists. And a semantic infrastructure should enable (useful) deconstruction of conventional models for business processes, technology, and creation of value, especially in knowledge work. (Take that, Mills! I can speak high-level, too!)

Another surface criticism: Just putting the word semantic in front of current work practices and technologies does not mean they do or will exist, at least by those names. Let’s not get too far ahead of ourselves with this labelling thing. It’s reminiscent of early (mid 1990s) pontifications on knowledge management, in which one well-known KM “guru” opined a need for “knowledge reporters” and other gurus raced to assert the need for “knowledge engineers.” Well, it turns out that several existing, widely known professions (including, but not limited to, systems analysts and technical writers) were already filling that “knowledge reporter” gap. And “knowledge engineers” had been around for a long time building expert systems. The news of a sudden new need for their job title was a bit of a surprise to them.

Recommendation: Go get it

Mills Davis’ dense, sweeping, high-level look at the promise of “semantic solutions” will open your eyes, give you pause for thought, and make your brain hurt. Each sentence requires — and deserves — careful parsing. And it will at times make you go “Huh?”

Manifestos are like that, I guess. But better your brain should hurt every once in a while than simply be filled up with comfortable fluff.

Reducing dependence on tacit knowledge

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 http://en.wikipedia.org/wiki/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.

Normalizing ideas

The relational database model rests on the basic principle of normalization of data.

Semantic technology approaches need to apply this principle, too. Not just at the level of concepts but also — and perhaps just as importantly — at the level of ideas. By ideas I mean complex expressions or assertions about reality, like “Our opportunity in the marketplace is to apply IBM’s UIMA to unstructured information in the enterprise.”

The “truth” of that assertion is obviously critical to the success of a company in that business. However, even if such assertions can be specified in a theory of meaning (like an ontology language), it’s not clear that it can be asserted to be true by any means other than the consensus of experts.

Fun with technology haters

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