Микрософт вдыхает в меня надежду:
Query execution proceeds as usual, with term parsing, analysis, and scans over the inverted indexes. The engine retrieves documents using token matching, and scores the results using the default similarity scoring algorithm. Scores are calculated based on the degree of linguistic similarity between query terms and matching terms in the index. If you defined them, scoring profiles are also applied at this stage. Results are then passed to the semantic search subsystem.
In the preparation step, the document corpus returned from the initial result set is analyzed at the sentence and paragraph level to find passages that summarize each document. In contrast with keyword search, this step uses machine reading and comprehension to evaluate the content. Through this stage of content processing, a semantic query returns captions and answers. To formulate them, semantic search uses language representation to extract and highlight key passages that best summarize a result. If the search query is a question - and answers are requested - the response will also include a text passage that best answers the question, as expressed by the search query.
For both captions and answers, existing text is used in the formulation. The semantic models do not compose new sentences or phrases from the available content, nor does it apply logic to arrive at new conclusions. In short, the system will never return content that doesn't already exist.
Results are then re-scored based on the conceptual similarity of query terms.
To use semantic capabilities in queries, you'll need to make small modifications to the search request, but no extra configuration or reindexing is required.
https://docs.microsoft.com/en-us/azure/search/semantic-search-overview
