A Survey of the Data Quality Problems in Knowledge Bases

PhD Qualifying Examination


Title: "A Survey of the Data Quality Problems in Knowledge Bases"

by

Mr. Linnan JIANG


Abstract:

Nowadays, knowledge bases have been widely applied in data storage and 
analysis. KG, known as knowledge graphs is the main form of data organization 
in knowledge bases. However, statistics have shown that the knowledge bases are 
still be far from high completeness and accuracy due to the low data quality in 
knowledge bases. Methods have been proposed respectively to enhance the 
knowledge base by increasing the completeness and accuracy. In general, there 
are internal methods and external methods. The former one will depend on the 
knowledge graphs themselves and use machine learning methods to make the 
refinement, while the latter one will refer the external sources such as other 
knowledge bases or human knowledge. These methods primarily aim to confirm a 
missing or faulty entity type, entity relation, knowledge graph interlinks or 
literal text. In this survey, we mainly discuss the methods that contribute to 
solve the data quality issues.


Date:			Tuesday, 14 August 2018

Time:                  	5:00pm - 7:00pm

Venue:                  Room 3494
                         Lifts 25/26

Committee Members:	Prof. Lei Chen (Supervisor)
 			Dr. Qiong Luo (Chairperson)
 			Dr. Xiaojuan Ma
 			Dr. Ming Liu (ECE)


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