*Other company or product names in the mix at various times include AI Corp and English Wizard. Not inappropriately, it seems that Larry has quite an affinity for synonyms …

EasyAsk is still a small business. The bulk is still in enterprise query, but new activity is concentrated on e-commerce applications. While Larry thinks that they’ve solved most of the other technical problems that have bedeviled him over the past three decades, the system still takes too long to implement. His rough rule of thumb is that implementation — i.e., building the thesaurus – takes 10% as much effort as overall database design did in the first place. That comment leads to what seems to me to be a pretty obvious suggestion: Focus on selling to sites that have already installed a “semantic layer” (Business Objects’ term) or the equivalent while setting up their BI system – whether or not EasyAsk can get actual partnerships with BOBJ, Cognos, et al. And I’ll stop right there, because I’m not sure whether Larry’s comments on what they have or haven’t done in that regard were meant as general briefing material, or were under NDA in our client relationship.

In the e-commerce area, EasyAsk is a direct competitor to Mercado, with a lot of analogous functionality. As previously noted, they claim their users enjoy particularly strong revenue benefits. After talking about it with Larry, I now feel it’s a sincere and credible claim. Neither he nor I would claim the case has been proven with shining statistical rigor (and my doubts as to its fundamental merits remain greater than his). But Larry is a smart and honest man, and when we discussed it I didn’t happen to catch him in any obvious and uncharacteristic thinking errors.

Takeaways from the conversation included:

• Customers are often software OEMs, especially in the health care and human resource areas.

• SQL generation times are down to a millisecond or so.

• MDX is a future direction for them, as an alternative to SQL. To the extent MDX syntax is ugly, that’s a plus for them, not a minus!

• Larry feels they’ve gotten a lot better at navigating people to canned reports and the like, rather than just re-executing queries for them. On the other hand, when I told him a vision I first pitched on his behalf (and to him) in 1984 about application command-and-control, I didn’t get a lot of recognition. Too bad. That kind of thing is central to natural language’s potential ability to solve some of the inherent problems of dashboards.

• The obvious voice recognition/natural language pairing now works pretty well for known users, but isn’t good yet for web applications.

• Since being acquired by Progress they’ve become much more multilingual. (I guess the name “English Wizard” gives a clue as to how multilingual they used to be.)

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Technorati Tags: Progress Software, EasyAsk, natural language, Mercado

I think it’s safe to say that e-commerce site navigation aids constitute a really important product category.

In broad outline, Mercado and InQuira each combine three basic search approaches:

• Generic text indexing.
• Augmentation via an ontology.
• A rules engine that helps the site owner determine which results and responses are shown under various circumstances.

Of the two, InQuira seems to have the more sophisticated ontology. Indeed, the not-wholly-absurd claim is that InQuira does natural-language processing (NLP). Both vendors incorporate user information in deciding which search results to show, in ways that may be harbingers of what generic search engines like Google and Yahoo will do down the road.

InQuira has all three standard levels of an ontology – generic, vertical, and customer-specific. They readily admit to being an instantiation of Monash’s Second Law of Commercial Semantics: Where there’s an ontology, there’s consulting. Indeed, professional services are almost 40% of InQuira’s revenue (which was almost $20 million last year). Beyond the ontology, they incorporate surfing and profile evidence to disambiguate users’ interests.

Let’s pause a moment to reflect on structured search and parts of speech. Obviously, when somebody’s shopping, it’s very important to interpret nouns. But adjectives are important too. If a customer expresses interest in a “gold” car, the website had better tell her about which “Metallic Champagne” vehicles are available. And on a retail site it’s rather important to know the difference between a “dress shirt” and a “shirt dress,” a test About.com’s ad-serving software currently fails.

The major differentiating feature of InQuira’s NLP/search technology is to take this further, and also think about verbs. More precisely, the focus is on “intents,” sometimes called “intent categories” instead – i.e., actions the customer is trying to undertake. These are defined in a kind of rules engine, which is separate from the semantic net used to represent the noun/adjective ontology.

Given that they’re defined by rulesets, there are a fair number of these intents. Back in June, 2005, InQuira told me they had packaged the linguistic knowledge for 100 “intents” for cell phone service companies, and were covering 72-72% of total inquiries that way. The most popular intents accounted for 10-12% of inquiries each.

Mercado doesn’t do “intents,” and I don’t think the ontology is as sophisticated either, but otherwise its search story is a lot like InQuira’s. Both companies, for example, offer rules-engine capabilities for displaying various page elements – i.e., portlets — alongside the actual search results (e.g., for upsell). Both also let web site owners tweak search results too, according to what they want to sell, what they think the customer is most likely to buy, or to provide some sort of near matches when the exact search isn’t a good enough match to actual inventory.

Mercado argues that its rules-based technology is particularly powerful, because of a capability they call RBT, for result(s)-based triggers. The idea is that rules can fire based on any characteristics of the search results themselves. Particularly important inputs seem to be the size and estimated precision of the raw result set.

I’ve hinted above that generic search can be beaten by more specialized technologies. I absolutely believe that, with one caveat. Whatever happens under the covers, word-based interaction with computers may well always have a generic interface – search boxes today, voice increasingly in the future. It’s what happens after the initial disambiguation that will be specialized according to – well, according to both the user’s and the server owner’s intent.