Semantic search engines employ various techniques from natural language processing (NLP), knowledge representation, and machine learning to understand the semantics of search queries and web content. Here's a breakdown of the process:

• Query analysis: The search engine analyzes the user's query to identify keywords, phrases, and entities. It also attempts to interpret the user's search intent by analyzing the relationships between these elements.
• Knowledge graph integration: Semantic search engines often leverage knowledge graphs, vast databases containing information about entities and their relationships. This information helps the engine understand the context of the search query.
• Content analysis: Similar to how a search engine analyzes queries, it also examines the content of web pages to determine their relevance to a particular search. This analysis goes beyond keyword matching and considers factors such as the overall topic, sentiment, and entities mentioned within the content.
• Result return and retrieval: Based on the analysis of the query and the content, the search engine could return  web pages according to their relevance and semantic similarity to the search query. It then retrieves and displays the most relevant results to the user.

Semantic search is important for several reasons:

• Improved relevance: By understanding the meaning behind a search query, especially complex or ambiguous ones, search engines can deliver more relevant results. This means users are more likely to find exactly what they're looking for on the first try.
• Enhanced user experience: When search results are highly relevant, users have a more satisfying experience. They can quickly find the information they need without wading through pages of irrelevant links.
• Increased engagement: Relevance is key to engagement. When users find what they are looking for, they are more likely to spend time interacting with the content since they more quickly find what they’re looking for.

Let's delve into how semantic search differs from other search methodologies.

While semantic search aims to understand the meaning and intent behind a search, keyword search focuses more so on finding exact matches between the keywords in a query and the keywords in a document.  Semantic search does a better job at capturing the user's true information needs, especially with complex queries involving synonyms, ambiguous terms, or implied relationships between concepts.

Lexical search, similar to keyword search, relies on matching words and phrases based on their literal form without considering their underlying meaning, whereas semantic search, again, aims to understand the meaning and relationships between words and phrases. 

Contextual search expands upon traditional search by taking into account the user's context, such as their location, and past interactions. Semantic search, while it can leverage contextual cues, primarily focuses on understanding the meaning of words and phrases within the search query itself. Think of contextual search as using external clues about the user, while semantic search focuses on deciphering the intrinsic meaning of the query.

Vector search relies on representing text as mathematical vectors in a high-dimensional space. It then calculates the distance between the query vector and document vectors to find the most similar content. While semantic search can use vector representations, it is a broader concept that encompasses various techniques to understand the meaning and relationships between words.

Semantic search can have a wide range of applications across various industries:

Semantic search may enhance e-commerce platforms by enabling more accurate and relevant product discovery. For example, a user searching for "warm winter gloves" could see results that include gloves made from wool, fleece, or other warm materials, even if the product descriptions don't explicitly mention "warm."

Within an enterprise setting, semantic search can help employees quickly and efficiently find relevant information within company databases, intranets, and knowledge repositories. This may improve productivity and decision-making by providing employees with the information they need when they need it.