Semantic Search : Driven By AI
Role of search engines as a channel to provide information has grown exponentially over the years . In pursuit of catering to all kinds of users with most ambiguous search terms in order to provide results based on the trends and their interests, search engines have come a long way . Decoding the entity relationships, the context and intent of users have becoming a challenging task for search engines as the way people carry out the searches is many a times much different to the content or information they are looking for .
Some years back, search engines could only analyze the exact phrase of a search term while matching results with a search query for eg : content which exactly matches with the keywords / search terms
If you now type “how old is the president of united states” in Google , you get an instant response along with the name of the incumbent president . How is this possible ?. The results are possible owing to ‘Semantic Search’ . Algorithms have evolved and are fine tuned to provide the closest or the best possible results satisfying searcher’s objectives .
Semantic search focuses on the meaning behind search queries instead of the traditional keyword matching. It refers to the ability of search engines to consider the intent and contextual meaning of search phrases while serving content to users on the web.
Google : Algorithms/Functionalities powering Semantic Search
- BERT : Improves understanding of long and complex sentences/queries. Works bidirectionally to understand the context of words and the relationship between them in a better manner
- RankBrain : AI based algorithm looks at queries and the content of web pages in Google’s index to better understand the meaning of the words
- Hummingbird : Focuses on the meaning of search queries over individual keywords. Topical relevance is given more importance
- Knowledge Graph : Knowledgebase of entities, relationships between them depends on structured data and entity extraction from text for gathering information including correlation of data among different dimensions especially on a particular theme /topic
Basic Optimization tips to navigate the world of Semantic Search
- Link building with a focus on relevancy
- Adoption and usage of structured data / schema markup
- Semantic HTML to follow a standard structure and layout of pages
- Brand building with an aim to become a knowledge graph entity
- Importance to topics /themes rather than keywords
- Search intent and user context should be given highest priority while developing content
Are you ready to take the plunge and ride the wave of AI driven experiences ?
The era of NLP algorithms has already begun !!!
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