Hybrid.AI

Abstract

Variety of Big data [17, 40, 44, 47, 52] is a significant impediment for anyone who wants to search inside a large-scale structured dataset. For example, there are millions of tables available on the Web, but the most relevant search result does not necessarily match the keyword-query exactly due to a variety of ways to represent the same information. Here we describe Hybrid.AI, a learning search engine for largescale structured data that uses automatically generated machine learning classifiers and Unified Famous Objects (UFOs) [33] to return the most relevant search results from a large-scale Web tables corpora. We evaluate it over this corpora, collecting 99 queries and their results from users, and observe significant relevance gain.