Abstract

The long-standing problem with long tail queries in information retrieval systems is the mismatch between the vocabulary presented by the user and the vocabulary presented by the documents: the architectural design of traditional keyword search systems does not deal with this problem in an adequate manner. The next revolutionary approach is the use of vector databases, which encode the semantic associations in high-dimensional spaces to permit systems to match queries with pertinent documents in terms of meaning as opposed to the use of lexical similarity. This semantic solution is especially useful in specialized areas like regulatory compliance, where users often use informal and non-formal language that does not follow the documentation language. With the introduction of neural embedding models and approximate nearest neighbor algorithms, it is now possible to perform retrieval systems based on vectors using millions of documents with an interactive response time. The dense passage retrieval techniques have shown significant advances over the conventional ways of retrieving information by retrieving context and pragmatic aspects of language that cannot be exploited by keyword matching. The combination of the similarity of the vectors with those of the traditional keywords is the best type of architecture, which provides maximum performance in that it balances the ability to understand semantically the neural architecture with the accuracy needs of queries having special identifiers or proper nouns. Attention mechanisms and fine-tuning application increase retrieval accuracy further in situations when long-document retrieval is needed, and the answer must be extracted with accuracy. Since embedding models are still undergoing change with additional developments in transformer architecture and training processes, the amenity of semantic search is becoming more and more a necessary infrastructure to support the provision of service to diverse user groups with different expertise levels and with variable information demands across the knowledge domain of specific expertise.