Bing it on! Comparing Bing Snapshot with Google Knowledge Graph

When we check the search engine market share, Google clearly dominates the market. At a global level, Google is expected to enjoy around 58%, while the second player is China’s Baidu with around 29% market share. Bing takes the third spot with close to 8% share. Considering Baidu is predominantly focused on Chinese market; Bing could be considered the second most preferred search engine at a worldwide level. If we consider the US market, Google enjoys around 68% share and Bing enjoys close to 19% share.

We have been discussing about Google algorithm updates in the last few blog posts. A natural question that will arise is does Bing  or other search engines also follow a similar algorithm update exercise and  technology ? In this post, let’s try to understand briefly about these questions from Bing’s point of view!

Bing was launched in 2009 as a successor of Microsoft Live Search. While still not a great contender to Google, Bing offers most of the features Google offer if not more. Bing has
The search engine – Bing (like Google search engine)
  • Bing Maps (like Google Maps)
  • Bing Local (like Google + places)
  • Bing Satori (like Google Hummingbird)
  • Bing Snapshot (like Google Knowledge Graph)
  • Bing Cortana (like Google Now)

The biggest difference (read USP) of Bing when compared to Google is the integration of social element. I consider the social component to be having two parts – an active one in which enables you to login to your Facebook account and a social sidebar is activated while you do the search in Bing. You can use this feature to search for friends near your locality while traveling or ask for suggestions and so on. The passive component of social element provides Facebook results, twitter, quora, and other social platform based options in Bing search results (for example Pin It option in image search! Or showing klout or other community Q&A results in Bing search results). Unfortunately, some of these feature are not available worldwide (for example Bing tags are available only for US users).

Since we have been discussing about entity search in last few blog posts; let’s try to understand how Bing compares with Google’s Knowledge Graph arsenal. Like Hummingbird, Bing powers its entity search engine with the help of a technology called Satori. As explained in the blog post on Knowledge Graph, Bing Satori or Google Knowledge Graph tries to understand the search queries (whether person, place, thing and so on) as an ‘entity’ that has several different possible connections with other ‘entities’ in the web world. Thus ‘Mahatma Gandhi’ as an entity can be considered to be related to ‘India’ entity as the relationship – ‘Father of Nation'; related to ‘Kasturba Gandhi’  entity as ‘wife'; related to ‘Jawaharlal Nehru’ entity as a ‘Indian freedom fighter’ and so on. The beauty of Bing Satori is that it brings in the social angle. Thus you need not be a celebrity to get into Bing Snapshot as opposed to Google Knowledge Graph.


If are to look straight on between Snapshot and Knowledge Graph, both looks pretty much the same. However one cool feature I liked in Bing was, as soon as you search for something and if Bing recognizes the entity relationships, it shows a preview in the search suggest itself! Moreover, we can click on the links in preview to go directly onto an another information if that interest you more. For example, consider the search query – ‘actors in bangalore days’ below. Without looking into the actual search results, Bing offers an option to directly goto the search result for ‘Fahadh Faasil’!



We can do a head to head comparison of search results in Bing viz. Google. I will leave it out to you…isn’t it fun?  :)

Here I will try to provide some examples where Bing provides a better result compared to Google. Let’s start with ‘Search Suggest’ option. Provided below is an example of a search query – ‘Java’. I am logged in to both Google and Microsoft accounts. I believe relation to Java software could be in association to my search history (instead of Java as a place!). However the interesting piece is Bing provides internal web page links in the suggest area if it understand the entity relationship! Isn’t that cool…?


The Bing Snapshot is more feature rich compared to Knowledge Graph in certain situations. For example if the search query is related to audio (Eg: ‘Indian national anthem’), a link to listen is provided; similarly if the celebrity have spoken at TED, the Snapshot provide link to the talks and so on. Bing Snapshot also aims to be a true knowledge provider. For example try searching ‘dolphin’. The amount of information provided by Bing Snapshot is way superior to Google Knowledge Graph!. Having said that, it fails in lot of search queries (for example , try ‘fermentation’).


Overall, I believe Bing provides some interesting ‘information’ when it comes to entity search and it could very well be a good competitor to Google in future. However with the strong position Google enjoys and the inertia of moving away from ‘Google it’ psyche, Bing has a long way to go…

What do you think…?

Google Hummingbird update and the opening up of entity search

Unlike Penguin and Panda updates that we discussed in the previous posts, Google hummingbird is an update to the search platform itself. It was released in September 2013. Hummingbird is considered to be first of its type update since 2000’s. In essence, hummingbird tries to add intelligence to the whole search phrase by considering the meaning of the phrase. If PageRank was the buzzword in 2000’s hummingbird and entity search is the buzzword today. In my opinion, this was not an overnight change, but an experiment and improve approach starting with the introduction of Knowledge Graph.



Before we dive deep into Hummingbird, let’s focus very briefly on two associated concepts for this blog post.

Google PageRank
If you are in the search industry for at least some time, you know this the ABC of how Google works. PageRank was and may still be the holy grail of how Google search engine works. Having said that, various studies have indicated PageRank is just one of the over two hundred components or ‘signals’ as they call it in deciding the search results. At the very basic level, PageRank is an algorithm component in which links to a website/page is considered votes and in turn used to decide the relevancy and credibility of that web page. Though the caveat here is, the quality of links is also considered; thus the mere high number of inbound links won’t get you a better PageRank. Provided below are two articles that explain the topic exhaustively.


Google Caffeine Update
Caffeine was an infrastructure update to Google ecosystem in 2010 unlike a search algorithm change. Before Caffeine update, the crawling and indexing was based on a batch mode.  So irrespective of how large the batch is, all web documents were pushed to live after the complete indexing procedure. With the caffeine update, Google is able to crawl the page and looks into the index and push live all in a matter of microseconds! This enormously improves searcher’s experience. It is also expected that the storage capacity and index size was increased with this infrastructure update. Haven’t you seen the search results changing on the fly while we type in int Google? Thanks to caffeine update!

Google Knowledge Graph
We discussed about Knowledge Graph in the last blog post. In short, Knowledge Graph tries to provide information about search queries rather than mere links to web pages that talks about the search query. It tries to consider data as entities and defines relationships between these entities. 

Read the blog post to understand better what Knowledge Graph offers you today.
Today, we are moving away from the concept of search based on documents and links to search based on data and relationships. Knowledge Graph is the back bone. While introduction of Knowledge Graph was to make the search engine results more facts and information; Hummingbird opens up  the world of semantic and entity search. It is the beginning of conversational search. Conversational search tries to give direct answer to search queries like ‘what is’, ‘when is’ and ‘what for’ types. In short Google tries to understand the intent of your search. For example are you trying to understand about a newly launched mobile phone or are you trying to compare between models or are you looking to buy one now. The search results vary depending on how Google interprets the intent of your query. Some of the factors that Google could use to giving meaningful search results could be
  • Synonyms of keywords
  • Keyword location / substitution and analysis of co-occurring terms to gauge the meaning
  • Geo location
  • Search device

Last leg of hummingbird update is the incorporation of voice search, especially in mobile devices and how search results are shown in mobile devices (no surprise there since factors like geo location can be better identified and thus personalizing the search result).
We will look into entity search in detail in another blog post. However let’s look into one of the foundation component which drives this showcase of facts and information. It’s all about making and utilizing structured data; And schema is one way to achieve it. Schema is a markup which provides meaning to the web page components. For example if you are talking about reviews in your website – you can use the mark up to identify the section as Reviews so that a search engine can pick up the rating directly for an associated search query. Schema allows you to define entities. For example, a Product, its specifications, its reviews , price and so on. Another example being the the rich snippets that we discussed in the blog post about Knowledge Graph is powered by schema. Schema is not the only markup, we have others like RDFa, microdata and so on. Thus we could consider markups being the underlying requirement for semantic search.

Stay tuned for more on the world of semantic search…

Google Knowledge Graph – Gaining knowledge without visiting web pages

We have been discussing about various Google algorithm updates. As you might have guessed, next in the line is Google Hummingbird update. But before diving into Hummingbird, I thought of covering Knowledge Graph in this post since that’s the foundation stone. In a later post, we will dive deeper into Hummingbird and the whole new world of Semantic Search.

In the world of PageRank, we have been dealing with unstructured data; i.e search engines were looking for keywords independently and trying to gauge the relevancy of a web page compared to a keyword. We are presently moving to a world of structured data; and thus the world of semantic search. Semantic search tries to gauge the meaning of search queries with the help of microdataschemas, or RDFs (Resource Definition Frameworks) and more. Google Started to move in this direction with the introduction of Knowledge Graph in 2012. From a layman’s point of view, Google started showing facts in addition to the usual search results for keywords involving famous personalities. From a slightly technical point of view, this was achieved with the help of link graph model that Google incorporated in its algorithm.

Let’s fast forward to November 2014 and see where Knowledge Graph is today. Let’s start with an example search for ‘baba amte’ results in a knowledge carousel in the right and pulls information from Wikipedia (or other credible sources) along with the usual search results.

If we dig a little more, and click ‘more’ in the Awards section; it takes you to another search result; this time for ‘baba amte awards’. It gives the details of awards, Baba Amte received in the search result itself as below. Essentially search engine are moving in the path of not making searchers surf various web pages from SERPs; instead provide all information in SERP itself (or at least the basic facts)! Google is able to provide the information at this level because of the crawl and grouping of information in the web into meaningful entities – like let’s say social workers, politicians, actors and so on; The interesting part is not only are the entities considered; but also relationships between them – For example, actor-award-films-personal. We will discuss about entities in detail in a future post.



Google + and Knowledge Graph
Though Google + is not relevant any more since they reduced the importance of G+, an additional entry in panel is the connection with Google +. Let’s assume the celebrity you searched for is also active in Google +. In that case, G+ posts by the person will also be shown in the knowledge panel (carousel). The panel continues to evolve with other options such as ‘Keep me updated’ options and more. Some other interesting information tidbits provided by the Knowledge Graph include
Social media profiles, in the news and more
  • Relationship information provided in the ‘People also search for’ section. If you search for an actor; the knowledge panel shows a hover assist with information on how the searched actor and other ‘also searched for’ persons are related.
  • Provision of social media profile links. If the person (entity) you searched for is active in social media platforms; a link to those are provided in the knowledge panel.
  • Nutrition and other information in food categories. For example – consider ‘potato’. It provides nutrition information and what not! The option varies depending on queries.
  • In the News section. For relevant search queries, results based on news articles are provided separately.


Now let’s focus our attention on top section of the SERPs. Consider the search query ‘Thailand tourism’. The search results provide a set of possible tourist places along with links and photos. If you click on a particular place, the search results will automatically be changed to the associated place! A similar carousel will be shown for queries like ‘things to do in thailand’ or ‘places to visit in Bangkok’ and so on.


Birth dates to current city temperature
Now consider another example of search query –  ‘when is mahatma gandhi born’. It gives the date of birth and in addition, provides the birth dates of related persons!. Now consider an example of flight number – EY283. Or ‘Abu Dhabi to Kochi’. Some other example include answers like time information, simple calculations, temperature and more. As you can see the search engine is trying to move in the direction of becoming an answer engine instead of just search engine!

Addition of such knowledge entities have become so diverse that it’s limited only be imagination. It’s reported that the knowledge graph has integrated options such as zip codes, hangout options, or comparison between entities or even step by step instructions! Latest in the Knowledge Graph world is the utilization of structured snippets to show facts in search results. With this inclusion, information is extracted from data tables in a web page and shown in the search results. An example in the Google blog post shows features of Nikkon camera being automatically picked up in the search result. But I couldn’t find a similar view yet 


Isn’t the world of semantic search exciting….?

A note on Google Pigeon algorithms

Now that we have understood a bit about local SEO, let’s continue our discussion on Google algorithm updates. Today we will look into Google Pigeon update. Google Pigeon  is the youngest component of the Google algorithm updates. The first Pigeon update was rolled out in July 2014. While Panda updates focus on quality of content, and Penguin updates on link building practices; Pigeon update specifically focused on local search.


In essence, the pigeon updates assign more weightage towards locality over authority. Also it took care of some concerns SEOs had in the case of local search like – Google + page given undue importance, removal of results from  local  search directories like  Yelp. Also it is expected to be having an affect only on Google US search queries. Since Pigeon update is comparatively new and only affect local search part, it’s still in the nascent stage to really understand the affect of this update. However it has been testified that, the pigeon update  affected few sectors like real estate and at the same time benefited sectors like education or hospitality.  Let’s try to dissect what Google Pigeon update affected.

Directories favored over local business listings

As mentioned above, after Pigeon update, search results from directories like Yelp or Tripadvisor made a comeback. This also mean that you should have a consistent citation across directories.

Change in local listing pack
The local listing pack or carousel had some changes post this update. It is reported that, the number of listings in the pack were reduced to three from earlier seven for some search queries. Also it is expected that the local listing pack itself was not shown for many search keywords. Another  change expected out of this update is reduction in duplicate results – i.e a website/business may not be shown in both organic search result and map pack at the same time.

Reduction in search radius
After the update, it is reported that Google has reduced the search radius while showing the local search results. This means the results for a search query will show listings much closer to the searcher’s location.

Brand v/s Local business
It is reported that in map listing and carousel, local business listings are shown comparatively on higher proportion when compared to brands for the same search queries. Also association between search keyword, brand name with domain have become positively correlated.

Long Tail keywords still the key
Optimizing for long tail keywords continue to show increased traffic. A combination of long tail keywords and targeted location specifics are now required. For example – 2BHK suite apartment in 13 Birds Road.