Thinking Clearly .

So we talked about 5 or 6 SemTech 2008 proposals based on our products, services, and technology bits. The guys convinced me that of those 6 ideas, there were 4 actual, strong proposals:

1. A Pellet tutorial
2. A talk about Pronto, our probabilistic reasoner integrated with Pellet
3. A talk about XACML-DL, our XACML policy analyzer
4. A talk about JSpace, our Linked Data browser

I thought we had a very small chance for (1)-(3), but a better than 50% chance for (4); my reasons were based on what typically shows up at SemTech, the interests of the organizers (in my view), and past talks that have been accepted.

Hence, I was a bit surprised when we got notifications today that (1) and (3) were accepted; (4) was not accepted; and we haven’t heard back on (2), though I’m assuming that it won’t be accepted.

(Update: Actually, the talk on Pronto was accepted, they just sent notification quite late. This is interesting because while we think, long term, there is commercial utility here, it really is quite a complex subject. One thing I realized in watching Pavel do this work over the summer is how surprising valid probabilistic reasoning can be.)

The Pellet talk is called “What to do with an OWL Reasoner”, and we’re hopeful there will be people who attend SemTech for whom that’s an interesting question. I was very surprised that the talk on XACML-DL got in, not because it’s not an interesting bit of tech, since it is, but more because we haven’t said much, if anything publicly about it yet. It has no buzz whatever.

We think policy management may be the big win for OWL in the enterprise space; but it’s still very much a dark horse.

Just as a précis: our XACML-DL analyzer, based on Pellet, for a near arbitrary set of XACML policies, can:

• perform formal policy verification and deep testing (think HTTP unit testing, only way sexier);


• perform policy change analysis;


• detect policy redundancy;


• perform policy repair, debugging, and explanation;


• support policy federation (disjointness checking, etc);


• perform policy set optimization.

I’ve recently started saying, to explain the commercial appeal of policy management generally, that every IT solution creates, eventually, a new round of IT problems, and XACML is a perfect example of that. So you’ve moved from procedural and imperative ACL code all over yr enterprise to declarative, orthogonal ACL decision points using XACML. That’s potentially a huge win in programmer productivity, in security quality, and in compliance. But now that you’ve got a few thousand XML files describing yr ACL policies, how the hell are you going to manage them? Are they game-able? Are they coherent? Are there redundancies and, thus, inefficiences?

Who knows and how does one go about finding out?

Those are precisely the kinds of management services for domain-specific policies that OWL is well-suited for, and there lots and lots of such policy languages out there in the world.

Our plan to offer commercial support for Pellet, and to gradually transition it from an academic to an industrial-strength tool, is proceeding nicely. OWL-DL is an interesting technology; but I keep hearing and seeing signs that the killer app still for Description Logics in Web technologies is still around the corner.

One of the things I spend my time these days doing is, well, looking for that killer app. I think at some point it will be found in the area of policy management; there is some very promising research being done in this space at UMD (and many other places), and we’re excited about our chances to roll out products in this area in the next 12 to 18 months.

But I’m also on the lookout for areas that will work now, and I’m starting to think that “collective intelligence” could turn into one of those near-term wins. What I mean in particular is creating an argument or decision-support tool, using OWL-DL and Pellet, within a public policy web app, like a prediction market, policy-support tool, social-network analysis, etc.

I recently happened upon an interesting paper in this area, “Collective Intelligence for Decision Support in Very Large Stakeholder Networks: The Future US Energy System”, which describes a web app to be used in stakeholder networks, with the example domain being the energy market in the US. Now, the web app part isn’t novel: it’s roughly equal parts wiki and polling system. The novel bits are what we might call the social informatics, that is, the structure of the interaction process and that these kinds of systems are being seriously considered and, in some institutions, deployed.

Of particular note for Clark & Parsia as an R&D company is the fact that the web app is designed to allow the use of an automated tool for supporting human decision-making processes, including argumentation analysis. Now this makes me happy for at least four reasons:

1. we’re interested in what I call (I hope not idiosyncratically) “hard collective intelligence”, that is, computer-aided collective intelligence using description logics, machine learning, or other AI and KR approaches. Someone’s gotta do the soft CI work, but we’re not ready to start hiring sociologists just yet…;


2. in the US, anything that contributes to increased rationality in public policy deliberation can only be a good thing;


3. it’s precisely the kind of work we want to be doing; and


4. it’s the sort of work that will dovetail nicely with our policy management schemes, which include things like dedicated policy management appliances embedded in the enterprise fabric, and SDKs for policy-to-logic mappings, etc.

We are and will remain for some time, perhaps a long time, a description logic company; but we’re also very excited by all the great work happening in machine learning, social and network informatics, computational economics, etc. It’s a great time to be working in all of these areas, and I’m especially interested in all the overlaps and interstices. If you’re doing this kind of work towards a degree, you might want to drop us a line; we’re always looking for graduate students whose research we want to support, since they make great partners and even better employees.

This time on Logical Commonsense. Some good stuff from Davis as well as from the Common Sense Problesm page.

Now crack that egg!