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Google VP Mayer describes the perfect search engine

Juan Carlos Perez | Nov. 11, 2009
She also talks about the key to Google's success

We have two, three, five changes every week that are visible to the end-user in the user interface. We don't [publicize] the ranking changes. We are making changes to our ranking algorithm at the rate of two per day. Interestingly, some of our competitors haven't made any changes to their ranking function for quite some time. Search needs to evolve: the user interface, the ranking function. It's a process of making lots of small changes all the time and to constantly make things better.

IDGNS: What's the status of semantic search at Google? You have said in the past that through "brute force" -- analyzing massive amounts of queries and Web content -- Google's engine can deliver results that make it seem as if it understood things semantically, when it really functions using other algorithmic approaches. Is that still the preferred approach?

Mayer: We believe in building intelligent systems that learn off of data in an automated way, [and then] tuning and refining them. When people talk about semantic search and the semantic Web, they usually mean something that is very manual, with maps of various associations between words and things like that. We think you can get to a much better level of understanding through pattern-matching data, building large-scale systems. That's how the brain works. That's why you have all these fuzzy connections, because the brain is constantly processing lots and lots of data all the time.

IDGNS: A couple of years ago or so, some experts were predicting that semantic technology would revolutionize search and blindside Google, but that hasn't happened. It seems that semantic search efforts have hit a wall, especially because semantic engines are hard to scale.

Mayer: The problem is that language changes. Web pages change. How people express themselves changes. And all those things matter in terms of how well semantic search applies. That's why it's better to have an approach that's based on machine learning and that changes, iterates and responds to the data. That's a more robust approach. That's not to say that semantic search has no part in search. It's just that for us, we really prefer to focus on things that can scale. If we could come up with a semantic search solution that could scale, we would be very excited about that. For now, what we're seeing is that a lot of our methods approximate the intelligence of semantic search but do it through other means.

IDGNS: Universal Search was announced in May 2007. Is it considered finished now? Is it something that will always be a work in progress?

Mayer: It's still a very living, breathing thing. Now we have multiple teams: We have a local [search] universal team, an image [search] universal team, the product [search] universal team. They're all looking at how can we do an even better job ranking and triggering this content. When we launched it, it was showing in about one in 25 queries. Today, it shows in about 25 percent of queries. And we think there are probably times when those auxiliary [file] formats could actually help, and we aren't triggering them on our results page. That's something we need to continue to strive to do.


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