NERank＋： a graph-based approach for entity ranking in document collections  

作　　者：Chengyu WANG Guomin ZHOU Xiaofeng HE Aoying ZHOU 

机构地区：[1]Shanghai Key Laboratory of Trustworthy Computing, School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China [2]Department of Computer and Information Technology, Zhejiang Police College, Hangzhou 310053, China [3]School of Data Science and Engineering, East China Normal University, Shanghai 200062, China

出　　处：《Frontiers of Computer Science》2018年第3期504-517,共14页中国计算机科学前沿（英文版）

摘　　要：Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user query. The rank order of entities is determined by the relevance between the query and contexts of entities. However, entities can be ranked directly based on their relative importance in a document collection, independent of any queries. In this paper, we introduce an entity ranking algorithm named NERank＋. Given a document collection, NERank＋ first constructs a graph model called Topical Tripartite Graph, consisting of document, topic and entity nodes. We design separate ranking functions to compute the prior ranks of entities and topics, respectively. A meta-path constrained random walk algorithm is proposed to propagate prior entity and topic ranks based on the graph model. We evaluate NERank＋ over real-life datasets and compare it with baselines. Experimental results illustrate the effectiveness of our approach.Most entity ranking research aims to retrieve a ranked list of entities from a Web corpus given a user query. The rank order of entities is determined by the relevance between the query and contexts of entities. However, entities can be ranked directly based on their relative importance in a document collection, independent of any queries. In this paper, we introduce an entity ranking algorithm named NERank＋. Given a document collection, NERank＋ first constructs a graph model called Topical Tripartite Graph, consisting of document, topic and entity nodes. We design separate ranking functions to compute the prior ranks of entities and topics, respectively. A meta-path constrained random walk algorithm is proposed to propagate prior entity and topic ranks based on the graph model. We evaluate NERank＋ over real-life datasets and compare it with baselines. Experimental results illustrate the effectiveness of our approach.

关 键 词：entity ranking Topical Tripartite Graph priorrank estimation meta-path constrained random walk 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP316.7[自动化与计算机技术—控制科学与工程]