Difference between revisions of "Open Domain Question Answering Using Wikipedia-Based Knowledge Model"
(New study: Open Domain Question Answering Using Wikipedia-Based Knowledge Model)
Latest revision as of 22:46, 12 August 2019
Open Domain Question Answering Using Wikipedia-Based Knowledge Model - scientific work related to Wikipedia quality published in 2014, written by Pum-Mo Ryu, Myung-Gil Jang and Hyunki Kim.
This paper describes the use of Wikipedia as a rich knowledge source for a question answering (QA) system. Authors suggest multiple answer matching modules based on different types of semi-structured knowledge sources of Wikipedia, including article content, infoboxes, article structure, category structure, and definitions. These semi-structured knowledge sources each have their unique strengths in finding answers for specific question types, such as infoboxes for factoid questions, category structure for list questions, and definitions for descriptive questions. The answers extracted from multiple modules are merged using an answer merging strategy that reflects the specialized nature of the answer matching modules. Through an experiment, system showed promising results, with a precision of 87.1%, a recall of 52.7%, and an F-measure of 65.6%, all of which are much higher than the results of a simple text analysis based system.