Information retrieval is the process of finding relevant information from a large collection of documents. It involves searching for documents that contain specific keywords or phrases, and then ranking the results based on their relevance to the user's query.

Keyword-based search matches exact words in a document, while semantic search understands the meaning behind the words. This allows semantic search to find relevant documents even if they do not contain the exact keywords that the user is searching for.

Semantic search faces a number of challenges, including the difficulty of understanding the meaning of words and phrases, the difficulty of representing the meaning of words and phrases in a computer-understandable way, and the difficulty of finding relevant documents that are semantically similar to a user's query.

Semantic search has a wide range of applications, including web search, e-commerce, enterprise search, healthcare, and many others.

The future of semantic search is bright. Semantic search will become more accurate and efficient, it will become more widely used, and it will be used to solve new and challenging problems.

A traditional search engine uses keywords to find relevant documents, while a semantic search engine uses the meaning of words to find relevant documents. This allows a semantic search engine to find relevant documents even if they do not contain the exact keywords that the user is searching for.

Building a semantic search engine is a challenging task. Some of the challenges include the difficulty of understanding the meaning of words and phrases, the difficulty of representing the meaning of words and phrases in a computer-understandable way, and the difficulty of finding relevant documents that are semantically similar to a user's query.

Semantic search has a wide range of applications, including web search, e-commerce, enterprise search, healthcare, and many others.

The future of semantic search is bright. Semantic search will become more accurate and efficient, it will become more widely used, and it will be used to solve new and challenging problems.

Semantic search uses a variety of techniques, including natural language processing, machine learning, and knowledge graphs, to understand the meaning of words and phrases and to find relevant documents that are semantically similar to a user's query.

Evaluating semantic search systems is a challenging task. Some of the challenges include the difficulty of defining what makes a semantic search system good, the difficulty of finding a large and diverse set of test queries, and the difficulty of measuring the relevance of search results.

There are a number of open research problems in semantic search, including how to better understand the meaning of words and phrases, how to better represent the meaning of words and phrases in a computer-understandable way, and how to better find relevant documents that are semantically similar to a user's query.

There are a number of trends in semantic search, including the use of deep learning techniques, the use of knowledge graphs, and the use of natural language processing techniques.

Semantic search is a subfield of information retrieval that focuses on understanding the meaning of words and phrases and finding relevant documents that are semantically similar to a user's query.

Semantic search has a wide range of applications in the real world, including web search, e-commerce, enterprise search, healthcare, and many others.