What sweet spot?
I wanted to leave a blog comment on the Clark and Parsia blog with respect to the entry Kendall wrote in the entry entitled “Our Approach to Modeling, Fidelity, and KR.” However, to leave such a comment I would have to log in, and I have way too many accounts right now, so I thought I’d write my response as a new entry (and by the time I finished, this was too long to be just a comment).
I don’t disagree with the overall “spectrum” that Kendall offers, but his point is that they have picked a point in the middle, and since they are in the middle they can model more than the scalers and scale more than the modelers. The problem is that the middle is very, very wide, and thus there are many places in this space that such a claim could be made. So, for example, a large triple store that can do a small amount of inferencing, say Garlik’s JXT as one example, would scale even better and could still be able to claim to do more modeling than a pure triple store.
On the other end, the idea that decidability is somehow a sweet spot (despite known exponential behaviors for DL) over a more highly modeled, but perhaps heuristic (or incomplete) logic. In this case the system could claim both to have more expressivity than a DL system, but also to be more scalable (just couldn’t gaurantee to have all the answers). In fact, right now the systems that probably have the highest score in modeling power vs. scalability would fall in this camp. The thing is their answer sets would be somewhat different.
I my opinion, the real problem with this blog entry is the idea that there is one sweet spot (Kendall called it the “sweet spot”) which implies that there is a general best answer. This is the point I cannot really live with, and have spent much of my recent career trying to debunk. Depending on what you are trying to do, there are many possible sweet spots. There are a set of problems for which what C&P are doing is exactly the right thing, but there are also many where they are not.
And that is the key thing, we in the field have to get much better at understanding where the tradeoffs are and what various kinds of applications require. Google taught us years ago that sometimes finding a good answer quickly can be an incredibly powerful thing. Expert systems taught us that for many application complex modeling is too expensive. Yet there are systems running in real applications that are using expert level modeling, because sometimes it is the thing you need despite the cost (and the ROI is high enough).
The other problem I have with the argument made actually has nothing to do with the issues of logic and such. The traditional database community for has for a long time made a similar claim, which is that there is a particular place in the expressivity/scalability place that is “the” correct place. They have spent years claiming that particular sweet spot is the only one that is interesting — it certainly has proven to be a very important one, making way more commercial success than the DL stuff. However, lately we’ve been learning that there exist problems where we need more expressivity, and thus other things have to be explored — the people in the DB community who’ve started looking at graph stores are, indeed, seeing that there are some applications, both in enterprises and especially on the Web, where the small amount of added expressivity makes a huge difference. (Anyone who has witnessed my debates with Ullmann have certainly heard this argued…)
Anyway, when I gave the first talk at the DARPA Agent Markup Language (DAML) program, lo these many years, I showed a slide with the word “THE” under a kill ring and stated that in the Web there is no the - and whether to the database community, the adherents of DL, the people who cite my work, or anyone else — remember you are exploring one sweet spot that can be important to some set of applications, but there are many others, and we all win when we remember that.
Cheers – Jim Hendler
p.s. Clearly this is not meant in any way to be an anti-C&P comment, I was just riffing off of what Kendall wrote.