Search engines, which process text and give you a menu of potential matches, make sense when you use an interface with a keyboard, a mouse, and a relatively large screen. Consider the below search for information about Columbia. Whether I mean Columbia University, Columbia Sportswear, or Columbia Records, I can relatively easily navigate to the official website of the place that I need.
Mobile devices require specificity as the cost of an incorrect result is magnified by the limits of the user interface. When using something like Siri, it is important to be able to give a precise answer to a question, rather than a menu of potential answers, as it is far harder to choose using these interfaces. As technology gets better, we will start to expect intelligent devices to be able to make the same inferences that we are able to make about what we mean when given limited information. For example, if I say “how do I get to Columbia?” to my phone while in New York, it should direct me to Columbia University, whereas in Chicago, it should direct me to Columbia College of Chicago. Leveraging contextual information is part of what makes Siri special, as it allows you to, for example, use pronouns. Some have said that Siri has resurrected the semantic web, as, in order to make the above choice of “Columbia” intelligently, it needs to know that Columbia University is located in New York while Columbia College is located in Chicago.
I have made the case before that people are increasingly seeking opinion data, not just factual data, online. It bears repeating that, as depicted in the below graph, searches for opinion words like “best” are increasing, relative to factual words like “car”, “computer”, and “software” which once were as prevalent as “best”, but now lag behind.
The implication of these two trends is clear. As more knowledge discovery is done via mobile devices that need semantic data to deliver precise contextual answers, and more knowledge discovery is about opinions, then mobile interfaces such as Siri, or Google’s answer to Siri, will increasingly require semantic opinion data sets to power them. Using such a dataset, you could ask your mobile device to “find a foreign movie” while travelling and it could cross-reference your preferences with those of others to find the best foreign movie that happens to be playing in your geographic area and conforms to your taste. You could ask your mobile device to play some Jazz music, and it could consider what music you might like or not like, in addition to the genre classifications of available albums. These are the kinds of intelligent operations that human beings do everyday, leveraging our knowledge both of the world’s facts and the world’s opinions and in order to do these tasks well, any intelligent agent attempting these tasks will require the same set of structured knowledge, in the form of a semantic opinions. Not coincidentally, Ranker’s unique competency is the development of a comprehensive semantic opinion dataset.
– Ravi Iyer