Difference between revisions of "Entity-Relationship Queries over Wikipedia"
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− | '''Entity-Relationship Queries over Wikipedia''' - scientific work related to Wikipedia quality published in 2012, written by Xiaonan Li, Chengkai Li and Cong Yu. | + | '''Entity-Relationship Queries over Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Xiaonan Li]], [[Chengkai Li]] and [[Cong Yu]]. |
== Overview == | == Overview == | ||
− | Wikipedia is the largest user-generated knowledge base. Authors 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. Authors present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model (BCM) for accurate ranking of query answers. Authors also explore various weighting schemes for further improving the accuracy of BCM. Authors test ideas on a 2008 version of Wikipedia using a collection of 45 queries pooled from INEX entity ranking track and own crafted queries. Experiments show that the ranking and weighting schemes are both effective, particularly on multipredicate queries. | + | Wikipedia is the largest user-generated knowledge base. Authors 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. Authors present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model (BCM) for accurate ranking of query answers. Authors also explore various weighting schemes for further improving the accuracy of BCM. Authors test ideas on a 2008 version of Wikipedia using a collection of 45 queries pooled from INEX entity ranking track and own crafted queries. Experiments show that the ranking and weighting schemes are both effective, particularly on multipredicate queries. |
Revision as of 07:43, 22 June 2019
Entity-Relationship Queries over Wikipedia - scientific work related to Wikipedia quality published in 2012, written by Xiaonan Li, Chengkai Li and Cong Yu.
Overview
Wikipedia is the largest user-generated knowledge base. Authors 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. Authors present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model (BCM) for accurate ranking of query answers. Authors also explore various weighting schemes for further improving the accuracy of BCM. Authors test ideas on a 2008 version of Wikipedia using a collection of 45 queries pooled from INEX entity ranking track and own crafted queries. Experiments show that the ranking and weighting schemes are both effective, particularly on multipredicate queries.