Narrowing search results just to what you mean courtesy the Knowledge Graph

The search engine major is keen to produce sharper and smarter, quicker and more relevant results. The company has just introduced a dynamic new feature known as ‘Knowledge Graph’. a ‘graph’ that will understand real-world entities and their relationships to one another: things, not strings. The idea is to hone the user searches and offer them more focus, breadth, depth and detail. These are several different ways in which the Graph will enhance Google Search:

Tapping into collective wisdom of the web

The Knowledge Graph will let you search for things, people as well as places Google knows about, including celebrities, cities, landmarks, sports teams, geographical features, buildings, movies, works of art, celestial objects and more, to instantly fetch information relevant to your search query.

This is a vital first step to build the next-generation search algorithm that will dig deeper into the collective wisdom of the web and understand the online world around us a bit more like real people do. According to the technical lead on this project, Shashi Thakur, the graph is more about collecting data about objects than anything else, in the real world. The near-omniscient engine is transcending the simple and basic text string for mapping the information there at the tips of its crawlers.

More relevant and accurate search results

Language can get ambiguous. For example, what does one mean while keying in the term ‘Taj Mahal’ – the monument, or the musician? Now, Google will understand the difference, and narrow the search results just to the one you mean. You simply click on any of the links to check that particular slice of search results. Your results are better – more relevant and accurate – since the engine now ‘understands’ these entities, and also the nuances in their hidden meaning, the way people do.

Google claims it can better grasp your query with the Knowledge Graph, so it can ably summarize relevant stuff around that particular topic, comprising key facts you are likely to require for that thing. For instance, if you are looking for information on a famous scientist or musician, you will view when he or she was born and died, apart from details on that person’s education and achievements.

Google elaborates in an explanatory note:

“How do we know which facts are most likely to be needed for each item? For that, we go back to our users and study in aggregate what they’ve been asking Google about each item. For example, people are interested in knowing what books Charles Dickens wrote, whereas they’re less interested in what books Frank Lloyd Wright wrote, and more in what buildings he designed.”

Understanding relationships between different interlinked things  

The graph will also help the engine to grasp the latent relationships between different things all of which will be linked in it. In essence, the graph is not simply a catalog of objects. Instead, it will also model the inter-relationships between them, spelling out the intelligence that binds them.