Editorial: 鈥�Do internet companies have all the answers?鈥�
SEARCH engines have barely changed since Google was founded in 1998. Sure, they run on blazingly fast servers and are powered by sophisticated algorithms, but the experience itself is basically the same: users enter a word or two and the engine spits out links to the most relevant pages.
That is about to change.Last month, Google its 鈥渒nowledge graph鈥�, which serves up facts and services in response to search terms 鈥� not just links to websites. It is the latest step in a process in which search engines are morphing into something quite new: vast brains that respond directly to questions posed in everyday language.
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鈥淪earch does a good job of returning pages,鈥� says Shashidhar Thakur of Google. 鈥淏ut we can go beyond that and return knowledge.鈥�
Links are not necessarily the best way to answer a query. When I search for 鈥渓ocation of Arsenal Football Club鈥�, for example, I would prefer to get a direct answer telling me the address of the club鈥檚 ground in London, rather than a link to a document containing the information. Google and Microsoft鈥檚 Bing can already provide direct answers to a small number of queries, but the range and depth of those answers is about to expand dramatically.
Over the past few years, Google and Microsoft have been quietly compiling vast knowledge databases to help them do this. Their stores have been built from publicly available information, such as Wikipedia pages, as well as prices from retail websites and user reviews. 鈥淎ll the things that describe an object are scattered across the web,鈥� says , director of Bing Search. 鈥淭he challenge is to bring them together.鈥�
鈥淭he things that describe an object are scattered across the web. The challenge is to bring them together鈥�
This open-source data is combined with internally acquired data, such as location information from the firms鈥� respective mapping products. It is all put together to create a 鈥済raph鈥�: a network of things, such as people and places, and the relationships between them. Google鈥檚 graph contains 500 million entities linked by tens of thousands of different types of relationships, Thakur says. Microsoft鈥檚 knowledge graph, which it calls the Satori database, contains 350 million entities, says Weitz.
At Google, algorithms now trawl the graph for information as well as searching the web. When I enter 鈥渂eagle鈥�, for example, I am shown a panel of data on the dog breed as well as links to relevant websites. The selection of data is based on previous searches on this topic, so Google knows in this case that users are interested in things like average height and lifespan.
Microsoft is taking a different tack. Its Snapshot service, due to be added to Bing this month, will use its knowledge graph to serve up links to services associated with the search term. A search for a restaurant, for example, will still return the restaurant鈥檚 website, but when a user moves the mouse over the link, Bing will give them the option of reading reviews, seeing a picture of the venue or booking a table there. Weitz says that the aim is to guess the real-world action that a user is interested in when they search and to return links that allow them to carry out those actions.
Both Google and Microsoft are also expanding the ability of their search engines to understand queries phrased in everyday language. Combined with the knowledge graph, new kinds of search will become possible. Thakur is working to make Google鈥檚 search engine answer detailed questions about the world, such as listing all books that have been made into movies, or all volcanoes that erupted between specific dates.
Microsoft鈥檚 service-oriented approach will be particularly suited to mobile devices, says Weitz. People on the move often want a direct answer to a question, like 鈥渉ow do I get to the nearest subway station?鈥�, rather than a link to a page with a relevant map.
The two giants face competition from a recent arrival that has formed a powerful partnership with one of their rivals. Wolfram Alpha, the creation of mathematician and entrepreneur Stephen Wolfram, also relies on a vast knowledge store. But unlike at Google and Microsoft, where data is often pulled from websites, the 200-strong Alpha team assembles its knowledge graph from traditional, fact-checked sources and eschews use of Wikipedia. Alpha can also perform data mash-ups, such as comparing stock prices. By drawing on geolocation and air travel data it can even identify the planes flying overhead.
Alpha got a break last year when Apple that it was using it as the brains behind Siri, an automated assistant that comes with the latest iPhone. It is the kind of application that Google and Microsoft, which have their own voice-recognition systems, are also looking at.
And Siri may be just the beginning. Voice-activated devices could soon tap into knowledge graphs to do all sorts of things. Want to listen to that 1980s pop song that was on the soundtrack of that action flick you have forgotten the name of? Or identify the digital camera that costs less than $150 and has the best user reviews? You have to trawl a list of links to get an answer, for now, but it will not be long before the graph delivers one in an instant.
When only a human will do
Algorithms aren鈥檛 much good at answering the nuanced questions that we face in everyday life, such as weighing the pros and cons of products or holiday destinations. For those queries, users might want to try Quora, a question-and-answer site that has taken off since its 2010 launch. The site鈥檚 answers, which are often detailed and submitted by experts in the subject, have expanded in range since its launch and now cover health, politics and entertainment, as well as business and technology. Although the firm has yet to unveil its business model, investors seem to think it has one. The site recently raised $50 million from high-profile people in Silicon Valley, valuing it at $400 million.
