Google introduced a new feature, which represents a
substantial extension to how their search engine
presents information and marks a significant
departure from some of the principles that have
underpinned their conceptual and technological
approach since 1998.
Knowledge Graph display was added to Google's search engine in
2012, starting in the United States, having been announced on
May 16, 2012.
The information in the Knowledge Graph is derived from many sources,
including the CIA World Factbook, Freebase, and Wikipedia. The feature
is similar in intent to answer engines such as Ask Jeeves and Wolfram
As of 2012, its semantic network contained over 570 million objects and
more than 18 billion facts about and relationships between different
objects that are used to understand the meaning of
the keywords entered for the search.
The “knowledge graph” basically adds a
layer to the search engine that is based on
formal knowledge modeling rather than
word statistics (relevance measures) and
link analysis (authority measures).
“The Knowledge Graph enables you to search for things,
people or places that Google knows about—landmarks,
celebrities, cities, sports teams, buildings, geographical
features, movies, celestial objects, works of art and more—
and instantly get information that’s relevant to your query.
This is a critical first step towards building the next
generation of search, which taps into the collective
intelligence of the web and understands the world a bit more
like people do.”
The Knowledge Graph is a knowledge base used
by Google to enhance its search engine's search results
with semantic-search information gathered from a wide
variety of sources.
It provides structured and detailed information about the
topic in addition to a list of links to other sites.
This is useful for a lot of reasons, but by far the most
important reason is because it facilitates human trends. You
can’t go to a website and easily find spider webs of
information that all, in some way, relate to each other for
the sole reason that only a product as big as Google’s search
could possibly hope to organize that information!
The goal is that users would be able to
use this information to resolve their
query without having to navigate to
other sites and assemble the
Johanna Wright calls the move “from
an information engine to a knowledge
The “knowledge graph” basically adds a layer to the search
engine that is based on formal knowledge modelling rather
than word statistics (relevance measures) and link analysis
Google is "enhancing" informational queries by serving up
some of the information you might find on reference sites
right in the SERP, as they have done in the past with weather
reports, movie times, flight information, and other data,
essentially removing the need for a Google user to click
through to another site.
For example, if I were looking for information on Matt Cutts, I
would almost certainly have to click one of the results to
get rich information with any real value. All the Knowledge
Graph here tells me is a) what he looks like b) his job c)
where he went to school and d) some other people who
are sort of associated with him. The Wikipedia page or his
own website would have much more richness of detail.
Knowledge Graph is providing me with some level of detail
(beyond the snippets, which already reveal some
information) before I click on any results. It's also making the
SERP more visual and more attractive.
The knowledge Graph does run on the right hand side
where the ads also run. Prospect there could be some
short-term impacts on that, but I think the primary thing is
getting people better answers is really good for our
Shopping about product listing ads I think that for us in the
early stages of that. We just rolled out Google Shopping
It seems crystal clear that he just acknowledged the fact
that Knowledge Graph goes where the ads go. They are very
aware of the placement and implications
The Knowledge Graph is beneficial to on-site content
strategies, especially post-Google’s Panda update
This specificity is great for users. They are able to find what
they are looking for much more efficiently without the noise
caused by results for words with different meanings.
There is also an inherent benefit to advertisers. No longer do
they have to compete with irrelevant search results for
valuable search engine real estate. With more refined results,
there are fewer pages that can potentially outrank an
Before Panda, it was typical for content strategies to include
developing unique pages around all targeted keywords. In
order to rank for two different phrases that have the same
meaning two pages would be created, each optimized for
one of the synonymous phrases.
The problem is that since the two content pieces are about
the exact same topic, the content would be very similar and
not have any added value to the user.
The fact that the Panda update made this practice essentially
obsolete by flagging the similar pages as “thin content” and
devaluing them posed a great threat to SEO.
The Google Knowledge Graph is a theory involving the
semantic Web. Instead of a search engine keying into certain
words, it actually interprets what each words means, based
upon the other words entered into the search engine.
Many words have two or more meanings, and since a search
engine is just a computer program it cannot tell the
difference between one and the other.
The Google Knowledge Graph theory is that the search
engine will be able to interpret the meaning of words from
other words in the search bar, so that the search
engine results are not as distorted.
Google suggestions gives webmasters an idea of what people
are going to run a search for. This makes it good for SEO,
because all they need to do is run a few Google searches, and
they will be able to what people are going to click for
The suggestions may be used as inspiration for the
If the Google Knowledge Graph is actually doing its job, then your
traffic numbers should drop a little, but your more targeted traffic should
go up. This is because people are not typing your keywords into the
search engine and getting unrelated results.
Long tailed keywords are surely going to become more powerful as the
Google Knowledge Graph becomes more sophisticated.
The semantic feature of the Google Knowledge Graph means that the
words surrounding a keyword are going to affect the search results.
Long tailed keywords have words surrounding other words, and so are
surely going to become more powerful than keywords that stand alone.