Schema2QA: Answering Complex Queries on the Structured Web with a Neural Model
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Virtual assistants today require every website to submit skills individually into their proprietary repositories. The skill consists of a fixed set of supported commands and the formal representation of each command. The assistants use the contributed data to create a proprietary linguistic interface, typically using an intent classifier. This paper proposes an open-source toolkit, called Schema2QA, that leverages the Schema.org markup found in many websites to automatically build skills. Schema2QA has several advantages: (1) Schema2QA handles compositional queries involving multiple fields automatically, such as "find the Italian restaurant around here with the most reviews", or "what W3C employees on LinkedIn went to Oxford"; (2) Schema2QA translates natural language into executable queries on the up-to-date data from the website; (3) natural language training can be applied to one domain at a time to handle multiple websites using the same Schema.org representations. We apply Schema2QA to two different domains, showing that the skills we built can answer useful queries with little manual effort. Our skills achieve an overall accuracy between 74 span three or more properties with 65 can be supported by transferring knowledge. The open-source Schema2QA lets each website create and own its linguistic interface.
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