Semantic similarity refers to the measure of how closely related two pieces of text or concepts are in meaning. It evaluates the degree to which words, phrases, or sentences share similar meanings, regardless of their specific wording. This concept is widely used in fields like natural language processing, information retrieval, and machine learning. Techniques for assessing semantic similarity include using word embeddings, which represent words in a continuous vector space, and ontologies, which define relationships between concepts. Applications of semantic similarity can be found in search engines, chatbots, and recommendation systems, helping to improve understanding and relevance in communication.