Thinking Clearly .

We’re welcoming 2009 by making a new release candidate for Pellet 2.0 available for download. Pellet 2.0 RC4 resolves several issues present in previous release candidates, which are documented more fully in a Pellet Trac report.

The resolved issue that was most likely to have frustrated users was broken import behavior when using the the Pellet command line with the Jena loader. In addition, our effort to support the built-ins listed in the SWRL submission is closer to complete now that the team has added implementation of the built-ins for URIs.

Special thanks to the users who reported issues since most of the changes in this release were made in response to user identified problems. Keep up the good work by sending your bug reports to the Pellet users mailing list.

For some people, the point of the Semantic Web is distributed, web-friendly knowledge management and knowledge representation. Generally we’re in that camp. But that camp breaks down into several factions, and it’s useful to be clear about which faction we’re in.

There is a spectrum that runs from Maximum Fidelity to Maximum Scalability. Given our roots in Description Logic, we lie somewhere in-between these two poles. Notice that I have intentionally avoided calling these “extremes”; they are endpoints, and perfectly respectable, useful ones, depending on who you are and what you’re trying to achieve.

The Max Fidelity folks want to model as closely as possible some world-chunk in as fine-grained and faithful manner as is possible. This often means that they are at least first order logic fans, and sometimes higher-order logic users. They debate edge cases, corner cases, alternate and competing semantics and logics in an attempt to ever more faithfully mirror reality. The price they pay is, generally, computability. For some use cases, that price is perfectly acceptable. For other use cases, that price is entirely too high, since the most perfect representation of the world is useless if you can’t practically compute with it—at least, that’s how Max Fidelity often looks to us.

At the far end of the spectrum we have Max Scalability folks, for whom the point of the Semantic Web is rather more the “Web” than the “Semantic” part—we might playfully call them the “semantic WEB” crowd, in order to reflect their ideal ratio. Here the point isn’t to model perfectly; but, rather, to do something with lots and lots of data, ideally Webfuls of data. This means, in the argot of current tech choices, that they tend to be RDF and Linked Data fans and users, since that’s just about the only approach to doing anything at all interesting with Webfuls of data. The price they pay, of course, is expressivity. For some use cases, that’s just fine, since you don’t always need a lot or even much semantic fidelity to get the job done. Sometimes we build applications for customer that take this approach. But, as above, for other use cases, this is simply a killer, because without enough or the right semantics, you don’t get the right kind of help from the machine in figuring out complex stuff.

So what do we have so far? First, we have a notional (and idealized) spectrum that runs from Webfuls of data to, roughly, at least first order logic. Second, we have obviously tons of interesting use cases at (probably) every point along this spectrum. And, third, we have the suggestion that we aim for some kind of sweet spot in the middle—where “sweet spot” and “in the middle” are not absolute notions, but are interest-relative and goal-specific, and where the interests and goals we care about are, surprise-surprise, ours.

(In other words, I’ve setup a little fantasy where we are the Heroes—where we naturally occupy the “sweet spot”—but then, since I’m not a complete jerk, I’ve ironized or called into question that very fantasy in an effort to suggest that we, just like everyone else, try to spin things to make ourselves look smart, cool, and useful.)

And—will miracles never cease?—that’s just about where Description Logic fits along such an idealized spectrum. Technically, it’s the decidable subset of first order logic, which means that we try to balance Fidelity and Scalability in a way where we can get some of both.

The Max Fidelity folks are forever poking us with sticks to the effect that we can’t model world-chunks nearly as faithfully as they can. Well, no crap, of course we can’t! Then the Max Scalability folks poke us with different sticks to the effect that we can’t scale to Webfuls of data—again, no duh!

And then we poke back at both camps—hey, they started it!—to the effect that we can model far better than Max Scalers and we can scale far further than Max Fideliters (yes, I just made that word up…Rock!)...

Finally, a word about how this positioning issue plays out in our approach to modeling. In short, we model such that we get the right inferences, since getting the inferences is typically what our kind of applications (analysis, decision support kinds of apps, in short) are all about. So that means some edge or corner cases, even if they fit into DL, get ignored or dropped out or even distorted when there’s no point—given requirements analysis—to fidelity for its own sake. And it means, on the flip side, that we don’t worry too much that that inference over Webfuls of data is not realistically achievable anytime soon. Fast enough for the customer’s data is sufficient scalability in most cases for us.

Related Articles

• Thinking ‘Inside the Box’ with Description Logics
• The Tribes of the Semantic Web

The perceived utility of automated reasoning for a wide range of applications matters to us greatly, which makes sense, given that our biz proposition is “semantic infrastructure OEM”. In other words, we’re trying to make money by licensing reasoning infrastructure, and related pieces, for semantic applications to other developers to use in their apps. With the right APIs and tool maturity, as well as supporting materials, our customers should be able to treat automated reasoning as a black box—not a black art.

A problem with demonstrating automated reasoning’s utility is that automated reasoning is complex, with non-trivial logical background and framework, including oodles of domain-specific vocabulary. Another problem is that automated reasoning is, in the end, just a kind of mechanical term rewriting often according to, considered individually, quite trivial rules. (Pellet isn’t really a rules engine, but we’ll talk about that another time.)

That means that for toy cases, which is what most people new to the subject are ready for, it seems dull and unimpressive. And for the hard cases? Well, most people aren’t ready for hard cases, so they simply tune out. And who can blame them, really? It’s like my example about Emma and Jack. I mean, that example really sucked, but what’s the alternative?

This is not an easy problem to solve.

My approach, rather than showing more toy or real examples, is just to talk about the utility of automated reasoning in plain language, in an attempt to communicate not so much specific details as the general mindset or approach to solving particular sorts of problems using automated reasoning. This approach to marketing mirrors our approach to technology development: both are iterative and experimental, but not just for us. As the man said, even a blind pig occasionally finds an acorn.

Related Articles

• Why Reasoning Matters: It’s Not What You Know… (2)
• Why Reasoning Matters: Explanations (3)
• Upcoming on This Blog…

I don’t have slides for my time at the APQC in Houston, I was not slated to present, so no cool slide widget with my presentation in this post.  I was merely there to observe and learn, and maybe answer some questions about POPS.

As Kendall mentioned, POPS was nominated as a best practice as part of NASA JPL’s overall efforts in Knowledge Management. The meeting at APQC was for all the nominees to give a short talk and to hear the overall findings of the study conducted by APQC, which in this case was on Expertise Location and Social Networking.

I got to see some great presentations by folks from IBM, Sun, Pratt-Whitney, Rockwell Collins, and Mitre and get a lot of insight into what they’re doing with Expertise Location and Social Networking; challenges they faced in the past, lessons learned, and what they’re doing now, and in the future, to continue their efforts in these areas.

It was a great experience, the people from APQC were fantastic, very friendly and put on a great event, and all the nominees and study partners, a group which included L3, Marathon Oil, ExxonMobil, and Wyeth, were all great and added a lot to the discussions.

Hopefully I get the chance to participate or work with APQC in the future.

Yesterday I gave a presentation at the XML in Practice conference about automated policy analysis using OWL. Here are the slides from my presentation.