IntentsKB: A Knowledge Base of Entity-Oriented Search Intents
Table 1. An excerpt from our knowledge base, for IntentID:  .
['Predicate', 'Object', 'Conf.']
Examples of these terms, hereafter named refiners, are “movies” in the query “the rock movies” and “nyc” in “hilton nyc.” We propose to represent search intents at the level of entity types, where a type is a semantic class that groups multiple entities. We present a pipeline approach that consists of four main components. First, we acquire entity-bearing queries of given types and aggregate refiners to obtain type-level query patterns (e.g., “[hotel] booking” or “[airline] customer service”). Second, we classify each of these type-level refiners into four main intent categories, property, website, service, or other, based on how that information need can be fulfilled. Third, type-level refiners that express the same underlying intent are clustered together. A cluster of refiners corresponds to the various ways of expressing that intent in an actual query (like “[hotel] booking,” “[hotel] reservations,” “[hotel] book a room,” etc.). Fourth, structured representations of intents are created by assigning each intent a unique intentID. This structured representation then contains the type of the entity, the category of the intent (according to the four main intent categories), and the different ways that intent may be expressed. Additionally, each piece of information is assigned a confidence score.