Social Networking Through Search: Hakia Helps You Meet Others
Hakia, a natural language search engine, has just added a new spin to search: social networking. Their new Meet Others feature lets you connect with others who are searching for the same things you are. Since Hakia processes queries differently than old school search engines such as Google, you aren’t just matched up with people […]
Hakia, a natural language search engine, has just added a new spin to search: social networking. Their new Meet Others feature lets you connect with others who are searching for the same things you are.
Since Hakia processes queries differently than old school search engines such as Google, you aren’t just matched up with people who typed in the exact query you did — you’re matched with a larger set of searchers that Hakia thinks are looking for the same things you are based on natural language processing. For instance, if you’re searching for “what drug treats a headache,” Hakia processes the semantic relationships between words and may deduce that someone searching for “what medicine relieves migraines” is a match. And that type of processing is the crux of how Hakia wants to differentiate itself.
I recently sat down with President and COO Melek Pulatkonak and CEO Dr. Riza Berkan to talk about what they’re doing in the search space and where they see things heading. More on how they’re tackling natural language processing below. First, a run down of what was launched today.
In addition to working on a completely new way of indexing and ranking the web behind the scenes, Hakia is also working on providing a unique search experience. In July, they launched the Hakia ScoopBar, which highlights the sections of the pages that are relevant for your query when you click through to them from the search results.
Today, with Meet Others, they hope to add a social networking component to search. The feature is entirely opt in. Once you do a search on Hakia, you’ll see a Meet Others icon above the search results. Click that to access a room designed for those doing similar searches. You can post a message and then provide details about how you want to be contacted (masked email or instant messaging via MSN or Skype). You can also contact others who have posted messages to the room. The freshest and most highly rated posts stay in the room longest. Older and less popular posts fall off as searchers make new posts. Hakia says they monitor abuse and have safeguards in place for spam (for instance, your post is authenticated through email).
What about privacy? Since the feature is opt-in, no one will see what you are searching for unless you decide to click the Meet Others icon and post a message. Even then, no registration is involved so your post isn’t associated with a username. And you decide what contact information you want to make available to others. If you choose email, Hakia masks it so anyone contacting you doesn’t see the address. (However, your IM details, if you choose to post them, are public.) You can also remove your post at any time, which removes any contact information you’ve made available.
Hakia likens this system a bit to craigslist.org. They want to bring people together. They use the example of someone looking for concert tickets. Someone else may post a message about tickets available for sale. Hakia can bring the buyer and seller together.
Here’s social networking search in action. Doing a search for “Looking for collectible pokemon cards” brings up results that include a Meet Others icon to the right of the search button.
Click that to see who has posted about that query.
Then post a message of your own or choose a contact option for someone who’s already posted.
As noted above, there’s no set time limit for how long a message remains in the room. It varies depending on how many messages get posted and how they get rated.
Is this an innovation in search or a recipe for disaster? How many people will find this valuable and how many will find it just plain creepy? In today’s climate of extreme social networking, Hakia just might be onto something, but the proof will be in the adoption.
Getting searchers comfortable with the notion of chatting with others about their searches isn’t Hakia’s only adoption obstacle. As I said in my review of one of Hakia’s CDs (they’re talented musicians in addition to computer scientists, who sing about zebras and finding your childhood on eBay for twenty five cents), regardless of how revolutionary their technology may be, the big challenge will be getting people to change the way they search because their technology isn’t at its best for the 2.8 word queries that Google has taught the world to type in. With those types of queries, Hakia performs just like any other search engine. Their differentiation comes with natural language processing, best used for longer queries that are typed more like the way people talk or write.
And what of this differentiation? Hakia provides interesting results now, but the jury’s still out on just how different and valuable what they are working on really is. They aren’t launching the search experience that’s powered by the core technology they are working on until sometime next year. They say what’s currently launched leverages the technologies they’re developing, so you can get a sense of what the final product will be like.
They say they are working on an entirely new infrastructure (different than what traditional search engines employ) called QDEXing (query detection and extraction). They search the web for concepts, rather than words, when satisfying a search. They point out that while the traditional search engines bring back good results most of the time, it’s impossible to know if pages that weren’t returned (because they have too few links to them, for instance) would have been more relevant for the query. By understanding the concepts on web pages rather than relying on things like external links and anchor text, they feel they can have a better sense of what page across the entire web is most useful to a searcher.
Traditional search engines use inverse indexing to catalog the words on a page. At the simplest level, when someone does a search, the engine looks through the index and finds the pages listed for those query words. Hakia, instead, uses QDEXing to determine what questions each page can answer. When someone does a search, Hakia finds that question and then returns the pages that answer it.
They use a number of scoring factors (such as linguistic and referential methods) to determine page quality. For instance, they say they can detect if a page was written by a person or was autogenerated. They only store pages that they feel meet their quality bar.
So, how will they get searchers to try it out? They may start with vertical databases that don’t do as well with traditional search technology. And they’ve cataloged the 700,000 most popular search results into galleries, which are algorithmically generated and have human review. Groups of hand-edited results for popular queries? Haven’t I heard this story before somewhere? Hakia says they’re different from sites like Mahalo and Wikipedia in that the results are algorithmically generated, so there’s less of an editorial component. They provide a well-balanced representation of query results — not just the most popular. And most importantly, the content is search results, not reference material. Everything about what Hakia does is about improving the search. What does a gallery look like? Well, I randomly chose a query — “Buffy the Vampire Slayer”. No reason. Hakia returns a gallery page with things like headlines, television profile, the channel it’s on, pictures, and fan sites. Hakia algorithmically generates the categories based on overall semantic processing of pages about the topic.
Of course, there are other players on the natural language processing bandwagon, with Powerset one of the most hyped of the bunch. Will Hakia’s approach of providing a unique user experience set them apart? Well, the Buffy page is kind of cool. Whether or not searchers can get comfortable with a new search experience and different way of querying remains to be seen.
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