Core Technologies in Semantic Search Engines

Semantic search engines have revolutionized the way we retrieve information from the web by focusing on user intent and contextual meaning, rather than relying solely on keyword matching. This is enabled by core technologies like NLP, Knowledge Graphs, AI, ML. NLP helps search engines make sense of human language, allowing them to understand how words and phrases relate to each other. This utilizes Knowledge Graphs to improve search results, as this builds the data into relations, supplying the search engine with the ability to return more precise and contextual results. AI and ML algorithms work within search engines to improve the quality of outputs, learning based on interactions and helping to continuously improve ranking models. Further factors such as ontologies and entity recognition are involved in contextual awareness, allowing for more accurate responses to complex queries as well. Vector search with encoders moves us away from naive keyword search to allow much more semantically related and deeper search that fulfills a deeper connection of the user to the data. Semantic search engines are becoming more sophisticated as the digital landscape evolves, enabling such innovations as voice search; conversational AI; and recommendation systems. This review article describes these key pillars, their interdependencies, and their implications for the future of information retrieval, conveying that semantic search is transforming the next-generation intelligent search systems.