Google takes data from a knowledge graph, FAQs, and organic links (documents) to provide an ideal user experience. Yext Answers takes the exact same approach. See how our results compare to a search on Google:
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Yext Answers
There isn’t a single perfect search algorithm—that’s why Answers has three. Rather than keyword-based search, Answers uses a multi-algorithm approach to surface the best results, similar to how the top consumer search engines work.
Answers uses Named Entity Recognition—based on Google's open source machine learning framework BERT—to detect potential filters and show structured results from a Knowledge Graph. This works great for structured entities like products, events, and jobs.
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FAQ data is more loosely structured than location or product data, but it contains rich information. Answers uses Semantic Text Search for FAQs. Instead of relying on keywords, we embed both search queries and FAQs in vector space and use an algorithm to determine the most relevant FAQs—no synonyms required!
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Yext Answers is able to search unstructured data to identify the most relevant documents and answers. With Extractive QA, you can search through unstructured content like blog posts, help articles, and product manuals and extract relevant snippets that answer the query posed.
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Answers is more than just a set of search algorithms—it's a full search-as-a-service platform. Learn more about the individual features that make up Answers.
