What is a Semantic Search?
Definition
Semantic search is an advanced information retrieval method that comprehends and considers the intent and contextual meaning behind user queries to deliver precise and relevant search results. Unlike traditional keyword-based search engines, semantic search interprets the relationship between words, utilizing techniques from Natural Language Processing (NLP) and artificial intelligence (AI) to ascertain the user's intent and understand the context. This approach enhances the accuracy of the search results by providing users with content that is semantically related to their queries, even if it doesn't contain the exact words used in the search.
Description
Real Life Usage of Semantic Search
Semantic search is predominantly used in applications like voice search, virtual assistants (e.g., Google Assistant, Siri), and smart search functions on platforms like Google and Bing, where the user's intent is inferred from their natural conversation style inputs.
Current Developments of Semantic Search
Major search engines and Artificial Intelligence (AI) companies are investing in AI-driven language models and vector databases to enhance the semantic capabilities of their search platforms. These advancements aim to improve the understanding of context and nuance in user queries.
Current Challenges of Semantic Search
One significant challenge is maintaining privacy while collecting data used for training Natural Language Processing (NLP) models. Ensuring the avoidance of bias in AI models, which might lead to skewed or incorrect results, is another challenge. Furthermore, the high computational resources required pose scalability issues.
FAQ Around Semantic Search
- How does semantic search improve user experience?
- What technologies are involved in semantic search?
- What industries benefit the most from semantic search?
- How does semantic search handle ambiguous queries?