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 nd each page individually. Toward searching directly and holistically for nding information of ner granularity, we study the problem of entity search, a signicant 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.
Search
Reviews
[Write a review of this article]
Find related articles from these CiteULike users
