Nowadays SEO experts are doing their best to draw Google’s attention to the well-designed websites. However, a lot of them miss the fact that Google implements a kind of intercourse aimed at developing an algorithm for search results building.
In spite of the algorithms being extremely helpful, search engines are still falling behind when compared to real human conversation. Mountain View specialists are currently doing a truly impressive job concerning the development and usage of artificial intelligence in computer technologies. As a matter of fact, they have already achieved unbelievable results including the AI Go champion, robotic employees in local stores, as well as the new personal assistant for smartphones.
The most up-to-date AI innovations are invariably used in in the sphere of search engines. Google aims at erasing the distinction between web search and natural conversation by means of semantic search and learning algorithms. So, how to keep up with the latest technologies and rank higher in Google search? In many aspects web search will turn into a natural communication.
There’s no wonder why the screenwriters of Ex Machina forebode AI being created by search engine developers.
What is Semantic Search?
Basically, "semantic" stands for the essence or meaning of a certain matter. Consequently, semantic search deals with the meaning and the logic of words. The aim of such search is to understand the contextual meaning of user’s query as accurately as possible. With the use of synonyms, concept matching and human language algorithms, semantic search engines convert all kinds of data into databases resulting into more interactive and accurate search response. Sematic search analyzes answers and forms individual results for each user. A good example of semantic search technology would be the Knowledge Graph used by Google.
Why do engines pursue semantic search?
The answer is quite obvious: Google semantic search opens a prospect for larger amounts of high-quality data, achieves better mutual understanding with users and makes the search language sound more “natural” to them. When combined, these factors definitely provide the best user experience.
As long as world’s data is doubling every 2 years, people have to find a way how to deal with this tremendous amounts of information in the most efficient way. This task is carried out by search engines that focus on data organization and structuring, as well as the creation of semantic connections in this system.
Defining and disqualifying poor quality content is one of the main Google semantic search functions. With the help of latent Dirichlet allocation (LDA), term frequency-inverse document frequency (TF-IDF) and latent semantic indexing (LSI) methods, Google distinguishes rewritten copies of original articles, as well as keyword spamming to estimate content quality. This way semantic web search “learns” the frequency of words combinations and their semantic relationships to increase content quality.
Entity-based semantics search technology allows engines to understand user’s requests better. The example bellow illustrates the contents of such search algorithm: basically, it consists of entities-nodes (beings, objects, places, abstract notions) and relationships-arrows connecting those nodes. Here is how a search engine interconnects different entities to make up more accurate search results:
An example of an entity-based semantic taxonomy.
Knowledge Graph response formed by the entity-based semantic search system.
Semantic search aims at showing the most relevant results. Take for instance “Jennifer Lawrence” search query that will be perceived by Google as the American actress famous for her role in the Hunger Games movies. Consequently, the search engine will show all the information about her – news, personal info, social media links, photos and movie list. Search engines combine given entities with the information acquired from search requests, and use it to guess what the next searcher will intent to ask about. Google’s Knowledge Graph has already advanced in the AI realm greatly.
“Jennifer Lawrence” search request will show only the information concerning the actress.
Thanks to the well-developed system of data connection, semantic search engines including Google have achieved great success in discerning various entities and making up appropriate answers. User’s request is “understood” by the search engine via special algorithms, and then the answer shows up. For instance, Google understands that the request “who is the dancer in the chandelier video?” concerns Maddie Ziegler, so a correspondent answer shows up. Google’s ability to join keywords with certain entities allows it to form relevant answers and provide better user experience.
Google’s Knowledge Graph answers properly, although the request didn’t contain entity name.
So why is search semantics so important for SEO specialists? The principles of semantic search tools are a crucial point to learn and understand for SEOs to keep up with search engines development. Another significant step in this sphere will be the rising technology of voice search that by no means should be left unconsidered.
Needless to say, the implementation of semantic search signals can’t but affect the way SEO experts design and implement their strategies. If it were possible to know all the keywords connected with each entity in every sphere, it would always be easy for SEOs to perfect their clients’ websites. However, such absolutes seem to be completely implausible, so the best results so far are more likely to be achieved with the help of SEO search strategies that have proved their efficiency through years of practice.
SEO semantic search strategies
The most recent achievements in the AI realm allowed Google to develop the Google Assistant – a new frontier of interactive web search. Sundar Pichai, Google CEO, expects the new assistant to keep up a natural conversation with a user.
In order to make this communication possible, Google requires a source to refer to, a specialist in a certain sphere who will help the search engine bring all the complex data to a highly organized level. And it is you who can become this Google’s trusted expert!
Tip: Decide in what realm you want to carve out a name for yourself. Just follow the useful questions below and design a perfect search strategy.
- What are the keywords topics you are willing to specialize in?
- Who are the leading experts in this sphere at the moment?
- What exactly characterizes them as top specialists?
- What can you do to become 10 times better?
- Who is the target audience of your content?
- Are they prospective customers or current ones?
- How would you characterize users’ activity within your content?
- What is your conversion rate?
- Are you aiming at introducing the content to users or converting them?
- Is your content relevant and does it exactly correspond with users’ requests?
- What can you do to ensure high-quality user experience on the website?
- How can you ensure more opportunities for users to find your content via organic search?
Develop targeted content that answers your customer’s questions
Try creating some attractive informational content besides the one that brings your website profit. Add various media data to your information to make it more interesting and illustrative for users. It’s always a good idea to find a golden mean between your product pages and supplementary information about the entity of the products offered. Such well-thought-out strategy will be your next step towards becoming an expert semantic search engines would refer to.
- Consider using Q&A structure of your informational content.
Note: Using numbered and bulleted lists, as well as laconic step-by-step guides can rank your content higher in the Google search.
- Define the keywords related to your topic and find out corresponding “what is”, “how to” and “why” query formulations.
Note: The http://keywordtool.io/ website may come in handy. Contribute some of your time to make a thorough research on keywords, and diversify your content in the most efficient way.
Structure sentences clearly and answer-based
Semantic search obliges SEOs to write their content so that it will be intelligible and sound “naturally”. Such promising innovations as voice search, as well as Google’s Hummingbird and RankBrain improved relevant results matching and only enhanced the importance of this requirement.
Whenever you plan how to write your content, try to concentrate on nouns since they are usually bound to particular entities. Use simple sentences and focus on subjects to achieve the best results. A good idea would be to employ the subject-predicate-object pattern in your sentences. Such method will make your texts more readable for users. Furthermore, search engines will be able to analyze your content more easily.
- Make your texts sound natural.
Note: Reading your content aloud may help you estimate its “naturality”(A good recommendation by Gary Illyes).
- Formulate the sentences in a simple and understandable for users and search engines way.
This example from the KDD 2014 Constructing And Mining Webscale Knowledge Graphs illustrates the way different sentence structures are perceived by search engine bots. They analyzed the first 2 sentences and extracted their meaningful data successfully, yet the bots failed to grasp the last one.
Structure your data to help bots parse content
If you use structured markup, you will be able to highlight certain points of your site content in order to present your information for the search engine more clearly. These pieces of information can appear to be extremely useful when it comes to increasing your website’s click-through rates. If you use really relevant information, some extra data concerning your page content will definitely attract more users.
In accordance with Google policies, your structured markup data must be visible on your website. Check Google’s list of all existing data markups in use, illustrated with examples. And don’t miss regular updates!
Use only the structured data that is apt regarding your content. For instance:
- Use Product Schema to add information about your product. There you should also include reviews and ratings of the product.
- Consider adding your store addresses via LocalBusiness Schema.
- Article Schema markup will surely come in handy.
- In case you focus on Q&A pattern, try Question Schema markup.
- Having a lot of listed items? Employ ItemList Schema.
All kinds of websites:
- Use BreadcrumbList Schema for your breadcrumb trails.
- Indicate the name of your site with the help of WebSite Schema.
- Make your social media data markup via SameAs Schema.
Leverage internal linking
Internal linking is a great tool that can be really helpful for many visitors of your website. But don’t use internal links excessively – spamming won’t attract any users!
- Look for particular points in your content where it would be apt to use internal linking and lead the visitors to your landing pages.
- Make references to your important URLs in your sitemaps.
- Make sure the links to your website’s most important pages are included in your global or navigation footer.
- Include relevant internal links to your important pages within the content of other pages.
- Make sure there’re no links that lead to corrupted pages returning 3XX or 4XX status codes. Fix such errors if any.
- Do not make links to URLs with parameters; target end-state canonical URL instead.
Currently, search engines are getting on the new level of using semantic approach to search results analysis. It requires that SEOs put more effort in their content – adding some related information, using synonyms etc.
Modern search engines focus their attention on spam, semantic aspects of content, showing individual search results for each user, direct answers to queries and making a conversation with user as natural as possible.
Use advanced strategies: you should become the source of high-quality content, aim at giving answers to users’ queries, structure your sentences properly and use marked-up content.
How to implement semantic search?
Nowadays, Google employs semantic search in order to make the process of communication with users as efficient and “natural” as possible. For example, with the help of entity-based search system Google’s Knowledge Graph is able to give correct answers to searcher’s query, even if the query doesn’t contain the entity name proper. Since semantic search affects SEO’s job greatly, use the tips and strategies from the article above to achieve success with your content.
How does semantic search work?
Semantic search “understands” the contextual meaning of user’s query as accurately as possible. With the use of synonyms, concept matching and human language algorithms, semantic search engines convert all kinds of data into databases resulting into more interactive and accurate search response. Sematic search analyzes answers and forms individual results for each user.
What is semantic analytics?
Basically, sematic analytics is a web search technology that analyzes the semantics of website content and users’ queries. It deploys a special algorithm identifying entities and their relationships. Such entity-based search allows engines to understand user’s requests better and show more accurate results.
What are semantic keywords?
Semantic keywords are the words identified by search engines as reference points for “understanding” information. From the search engines’ viewpoint, semantic keywords are what users imply in their queries. If you as a SEO want to master a certain sphere of knowledge and create top-notch content, you should make a thorough research on keywords from this sphere.
What is LSI keyword in SEO?
Generally, LSI keywords are the words related to the topic of a certain web page. LSA stands for “Latent Semantic Indexing”, so LSI keywords are regarded by search engines as most semantically close to query wording. They may include synonyms and most frequently used words concerning the sought-for topic.
What is latent semantic indexing?
Latent semantic indexing (LSI) analyzes various documents on the web and defines different pieces of content that have many common words as semantically close, thus relating them to one realm of knowledge. This way LSI can show apt results even when there are no exactly matching keywords in the text. Even though LSI doesn’t understand the semantics of words, it has already proven its high efficiency.