Mining, Ranking and Recommending Entity Aspects

Entity queries constitute a large fraction of web search queries and most of these queries are in the form of an entity mention plus some context terms that represent an intent in the context of that entity. We refer to these entity-oriented search intents as entity aspects. Recognizing entity aspects in a query can improve various search applications such as providing direct answers, diversifying search results, and recommending queries. In this paper we focus on the tasks of identifying, ranking, and recommending entity aspects, and propose an approach that mines, clusters, and ranks such aspects from query logs. We perform large-scale experiments based on users' search sessions from actual query logs to evaluate the aspect ranking and recommendation tasks. In the aspect ranking task, we aim to satisfy most users' entity queries, and evaluate this task in a query-independent fashion. We find that entropy-based methods achieve the best performance compared to maximum likelihood and language modeling approaches. In the aspect recommendation task, we recommend other aspects related to the aspect currently being queried. We propose two approaches based on semantic relatedness and aspect transitions within user sessions and find that a combined approach gives the best performance. As an additional experiment, we utilize entity aspects for actual query recommendation and find that our approach improves the effectiveness of query recommendations built on top of the query-flow graph.