Difference between revisions of "Entity-Relationship Queries over Wikipedia"
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− | '''Entity-Relationship Queries over Wikipedia''' - scientific work related to | + | '''Entity-Relationship Queries over Wikipedia''' - scientific work related to Wikipedia quality published in 2010, 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 | + | Wikipedia is the largest user-generated knowledge base. Authors propose a structured query mechanism, entity-relationship query , for searching entities in Wikipedia corpus by their properties and inter-relationships. An entity-relationship query consists of arbitrary number of predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text rather than pre-extracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It avoids information loss that happens during extraction. Authors present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model for accurate ranking of query answers. Experiments on INEX benchmark queries and own crafted queries show the effectiveness and accuracy of ranking method. |
Revision as of 07:04, 1 July 2019
Entity-Relationship Queries over Wikipedia - scientific work related to Wikipedia quality published in 2010, 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 Wikipedia corpus by their properties and inter-relationships. An entity-relationship query consists of arbitrary number of predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text rather than pre-extracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It avoids information loss that happens during extraction. Authors present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model for accurate ranking of query answers. Experiments on INEX benchmark queries and own crafted queries show the effectiveness and accuracy of ranking method.