Entity-Relationship Queries over Wikipedia

Author:

Li Xiaonan1,Li Chengkai1,Yu Cong2

Affiliation:

1. University of Texas at Arlington

2. Google Research

Abstract

Wikipedia is the largest user-generated knowledge base. We propose a structured query mechanism, entity-relationship query , for searching entities in the Wikipedia corpus by their properties and interrelationships. An entity-relationship query consists of multiple predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text instead of preextracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It only requires rudimentary entity annotation, which is simpler than explicitly extracting and reasoning about complex semantic information before query-time. We present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model (BCM) for accurate ranking of query answers. We also explore various weighting schemes for further improving the accuracy of BCM. We test our ideas on a 2008 version of Wikipedia using a collection of 45 queries pooled from INEX entity ranking track and our own crafted queries. Experiments show that the ranking and weighting schemes are both effective, particularly on multipredicate queries.

Funder

Division of Information and Intelligent Systems

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Theoretical Computer Science

Reference26 articles.

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. RELink;Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval;2017-08-07

2. A SVR-based ensemble approach for drifting data streams with recurring patterns;Applied Soft Computing;2016-10

3. Exploratory querying of extended knowledge graphs;Proceedings of the VLDB Endowment;2016-09

4. Relationship Queries on Extended Knowledge Graphs;Proceedings of the Ninth ACM International Conference on Web Search and Data Mining;2016-02-08

5. Separating Wheat from the Chaff – A Relationship Ranking Algorithm;The Semantic Web;2016

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