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:
Google Search
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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Soon, Yext Answers will be able to search unstructured data to identify the most relevant documents. With Extractive QA, you can crawl, index, and search through blog posts, help articles, and product manuals and extract relevant snippets that answer the query posed.
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At its core, search is about understanding language. That’s why we’ve leveraged the latest Natural Language Processing (NLP) technology, including Google’s breakthrough machine learning framework BERT. The Answers Algorithms are constantly evolving to provide better results over time, reducing the need to hire data scientists and developers.
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.
