Knowledge Graph Services

Cognitive & Semantic Search

Cognitives & Semantic search refers to a search technique that aims to understand the intent behind a user’s query rather than just matching keywords. It uses Natural Language Processing (NLP) and machine learning algorithms (for example- ChatGPT and LLM) to analyze the context, meaning, and relationships between words to deliver more relevant search results. Search can help businesses gain a competitive advantage in their decision-making process by providing insights and recommendations that are accurate, relevant, and actionable. Overall, the main benefit of semantic search is that it enables more natural and conversational interactions with search engines, which in turn leads to more accurate and useful search results. Here are some ways in which our team can help implementing semantic search capabilities:


We can improve the efficiency and accuracy of search results by understanding the context and meaning of the query. It is particularly useful for complex and ambiguous queries that may have multiple meanings or interpretations.This can help businesses find relevant information quickly and easily, allowing them to make informed decisions faster.


Search engines can interpret a user’s query in the context of the user’s search history, location, and other relevant data to provide more personalized and accurate search results. This technique goes beyond traditional keyword matching and takes into account the user’s intent, synonyms, related concepts, and other factors to provide the most relevant results.

Understanding of user intent:

Semantic search services use NLP to understand the intent behind a user’s query. This means that they can interpret the meaning of words in the context of the user’s search history, location, and other relevant data, in order to provide more relevant results.

Concept-based search:

Semantic search services go beyond traditional keyword matching and take into account related concepts and synonyms to provide a more comprehensive set of results. This means that even if a user’s query doesn’t include the exact words they are looking for, they can still get relevant results.


Semantic search services use natural language processing (NLP), including ChatGPT and other LLM models, to understand the meaning and context of words and phrases in user queries.

Some examples of semantic search services include Google’s Knowledge Graph, which provides a comprehensive set of results based on the context of a user’s query, IBM’s Watson Discovery, which is a platform for building and deploying AI-powered search applications in enterprises, and Microsoft AI from Bing is powering Azure Cognitive search, which is a cloud search service that gives developers APIs and tools to build rich search experiences over private, heterogeneous content in web, mobile, and enterprise applications.This means that users can interact with search engines in a more conversational and natural way, which can lead to more accurate results.