According to a comScore study done last year, booking travel over the Internet has become something of a nightmare for people. It’s not that using any of the booking engines is difficult, it’s just that there is so much information out there that planning a vacation is overwhelming. According to the comScore study, the average online vacation plan comes together through 12 travel-related searches and visits to 22 different web sites over the course of 29 days. Semantic search startup UpTake (formerly Kango) aims to make that process easier.
UpTake is a vertical search engine that has assembled what it says is the largest database of US hotels and activities — over 400,000 of them — from more than 1,000 different travel sites. Using a top-down approach, UpTake looks at its database of over 20 million reviews, opinions, and descriptions of hotels and activities in the US and semantically extracts information about those destinations. You can think of it as Metacritic for the travel vertical, but rather than just arriving at an aggregate rating (which it does), UpTake also attempts to figure out some basic concepts about a hotel or activity based on what it learns from the information it reads. Things such as, is the hotel family friendly, would it be good for a romantic getaway, is it eco friendly, etc.
“UpTake matches a traveler with the most useful reviews, photos, etc. for the most relevant hotels and activities through attribute and sentiment analysis of reviews and other text, the analysis is guided by our travel ontology to extract weighted meta-tags,” said President Yen Lee, who was co-founder of the CitySearch San Francisco office and a former GM of Travel at Yahoo!
What UpTake isn’t, is a booking engine like Expedia, a meta price search engine like Kayak, or a travel community. UpTake is strictly about aggregation of reviews and semantic analysis and doesn’t actually do any booking. According to the company only 14% of travel searches start at a booking engine, which indicates that people are generally more interested in doing research about a destination before trying to locate the best prices. Many listings on the site have a “Check Rates” button, however, which gets hotel rates from third party partner sites — that’s actually how UpTake plans to make money.
The way UpTake works is by applying its specially created travel ontology, which contains concepts, relationships between those concepts, and rules about how they fit together, to the 20 million reviews in its database. The ontology allows UpTake to extract meaning from structured or semi-structured data by telling their search engine things like “a pool is a type of hotel amenity and kids like pools.” That means hotels with pools score some points when evaluating if a hotel is “kid friendly.” The ontology also knows, though, that a nude pool might be inappropriate for kids, and thus that would take points away when evaluating for kid friendliness.
A simplified example ontology is depicted below.
In addition to figuring out where destinations fit into vacation themes — like romantic getaway, family vacation, girls getaway, or outdoor — the site also does sentiment matching to determine if users liked a particular hotel or activity. The search engine looks for sentiment words such as “like,” “love,” “hate,” “cramped,” or “good view,” and knows what they mean and how they relate to the theme of the hotel and how people felt about it. It figures that information into the score it assigns each destination.
Yesterday, we looked at semantic, natural language processing search engine Powerset and found in some quick early testing that the results weren’t that much different than Google. “If Google remains ‘good enough,’ Powerset will have a hard time convincing people to switch,” we wrote. But while semantic search may feel rather clunky for the broader global web, it makes a lot of sense in specific verticals. The ontology is a lot more focused and the site also isn’t trying to answer specific questions, but rather attempting to semantically determine general concepts, such as romanticness or overall quality. The upshot is that the results are tangible and useful.
I asked Yen Lee what UpTake thought about the top-down vs. the traditional bottom-up approach. Lee told me that he thinks the top-down approach is a great way to lead into the bottom-up Semantic Web. Lee thinks that top-down efforts to derive meaning from unstructured and semi-structured data, as well as efforts such as Yahoo!’s move to index semantic markup, will provide an incentive for content publishers to start using semantic markup on their data. Lee said that many of UpTake’s partners have already begun to ask how to make it easier for the site to read and understand their content.
Vertical search engines like UpTake might also provide the consumer face for the Semantic Web that can help sell it to consumers. Being able to search millions of reviews and opinions and have a computer understand how they relate to the type of vacation you want to take is the sort of palpable evidence needed to sell the Semantic Web idea. As these technologies get better, and data becomes more structured, then we might see NLP search engines like Powerset start to come up with better results than Google (though don’t think for a minute that Google would sit idly by and let that happen…).
What do you think of UpTake? Let us know int he comments below.