140679 - An entropy-based class assignment detection approach for RDF data.pdf (643.4 kB)
An entropy-based class assignment detection approach for RDF data
The RDF-style Knowledge Bases usually contain a certain level of noises known as Semantic Web data quality issues. This paper has introduced a new Semantic Web data quality issue called Incorrect Class Assignment problem that shows the incorrect assignment between instances in the instance-level and corresponding classes in an ontology. We have proposed an approach called CAD (Class Assignment Detector) to find the correctness and incorrectness of relationships between instances and classes by analyzing features of classes in an ontology. Initial experiments conducted on a dataset demonstrate the effectiveness of CAD.
History
Publication title
Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence. Part II. Lecture Notes in Computer Science, volume 11013Volume
11013Pagination
412-420ISBN
9783319973098Department/School
School of Information and Communication TechnologyPublisher
SpringerPlace of publication
New York, United StatesEvent title
15th Pacific Rim International Conference on Artificial IntelligenceEvent Venue
Nanjing, ChinaDate of Event (Start Date)
2018-08-28Date of Event (End Date)
2018-08-31Rights statement
Copyright 2018 SpringerRepository Status
- Open