In a June 2010 Semantic Web Meetup in San Diego, Peter Mika of Yahoo!’s research division gave a presentation entitled, “The future face of Search is Semantic for Facebook, Google and Yahoo!” As the title suggests, the presentation focused on the ever-growing use of semantic markup as a means for helping computers parse and understand content.
The talk focused on what was then the current state of the Semantic Web, as well as upcoming formats/technologies in development and the research being done in the field of semantic search.
The idea that the Semantic Web would be central to search within just a few years was met with some skepticism at the time — back then, all most folks were tracking was the adoption of Semantic Web technologies and semantic search using primarily RDFa, embedded metadata, or semantic markup.
Search Becomes Semantic & Graph-Based
The prediction that search would become increasingly semantic and graph-based has certainly proven to be more than true. Not only have the search engines since adopted schema.org as a standard along with microdata as a syntax (Facebook RDFa and Open Graph are examples), but things are now elevated to the next level in this process of adoption.
Graph Search on consumed/verified/validated information, which is a core component of the Semantic Web, is now considered key for the future of search in both search and social engines.
Google Knowledge Graph
Google originally started their rich snippets program in 2009 and finally announced their Knowledge Graph just a couple of days before Facebook’s IPO in 2012. For their “rich snippets,” Google initially utilized their own ontology/vocabulary, then switched to schema.org standards along with the three other major search engines (now four, as Yandex recently joined the mix).
Google’s Knowledge Graph is effectively using their version of graph search; however, it is largely focused on information and facts rather than searcher interests and social interactions. While there are some social signals provided via Google+, Knowledge Graph search is primarily based on Wikipedia-type information and other related/verified sources.
Excitingly, this is constantly being extended. (Those of you interested in further details and additional information may want to check out wikidata.)
Search Is Fast Moving
As an example of the incredible pace at which the industry moves, look at image recognition. Image recognition was a research problem in the 1990s. Several years ago, facial recognition made its way into everyday applications like i-photo, Facebook, Picasa and even your camera. Computer vision techniques are now being applied all over the Web — especially in retail to find “visually similar” items (e.g., Google Shopping and Amazon).
Google Play also now leverages facial recognition to enhance its Knowledge Graph capabilities. It’s pretty remarkable, but we are only seeing the beginning.
The reality is that intelligent systems are driving e-commerce and sales. We now live in an era where forward deployed inventory calendars can leverage predictive stocking, making same-day delivery of online purchases possible. Technologies like graph search, graph analytics, predictive analytics and big data are par for the course, being leveraged under the hood every time a user makes a purchase online. Google itself is a master at these technologies.
Clearly, the other search engines have adopted similar data tracking techniques and are working to leverage them as well. Bing “tiles” are their version of rich snippets; their version of the knowledge graph, or graph search, is probably Bing snapshots. (Feel free to read more on Microsoft’s graph-based repository, Satori.)
From what I can garner looking at this in the context of Bing Tags, they are taking a more social approach to Semantic and graph-based search (presumably in line with their relationships with social media sites like Facebook, LinkedIn, etc), even going so far as to allow users to control their appearance in SERPs to some extent. Bing clearly needs this strongly differentiated angle to help increase its share of the search market.
Facebook Graph Search
Finally, enter Facebook Graph Search, which utilizes Facebook’s wealth of easily filterable user data to provide searchers with results that are tailored specifically for them. As my colleague, Gerald Burnand, writes in his piece about Facebook’s Graph Search, there are numerous possibilities for how this filtered data might be used.
Recruiters might look to Facebook to narrow down potential candidates by industry (although it has a long way to go if it wants to compete with LinkedIn). Similarly, Facebook marketers could use Graph Search to learn more about common Likes/interests among their fans, making it easy to create highly-targeted micro campaigns aimed at relevant demographics.
Understanding & Using Search Data
Today, even the end consumer has access to these resources in the form of their mobile phones, tablets and computers. Remember, every time a consumer picks up a mobile device, uses an app, or performs a search, those devices are essentially acting as sensors, tracking search queries, geolocations, and many other consumer behaviors — all of which ultimately turn into information to be used to increase conversions.
Google’s status as a market leader in search grants them access to copious amounts of invaluable user data and product information. This information — obtaining it, understanding it and applying it — is the key to their success.
There is clearly a race among these companies to better aggregate and understand huge amounts of data. The Semantic Web has come far since 2010, but improvements and new technologies are continuously being developed. What new developments do you see on the horizon?
Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.