EntityRank: searching entities directly and holistically

TLDR: This work focuses on the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking.

Abstract: As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. While entities appear in many pages, current engines only find each page individually. Toward searching directly and holistically for finding information of finer granularity, we study the problem of entity search, a significant departure from traditional document retrieval. We focus on the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We evaluate our online prototype over a 2TB Web corpus, and show that EntityRank performs effectively.