InQuira and Mercado both have broadened their marketing pitches beyond their traditional specialties of structured search for e-commerce. Even so, it’s well worth talking about those search technologies, which offer features and precision that you just don’t get from generic search engines. There’s a lot going on in these rather cool products.
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.