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Automated class correction and enrichment in the Semantic Web

Citation

Barati, M and Bai, Q and Liu, Q, Automated class correction and enrichment in the Semantic Web, Journal of Web Semantics, 59 Article 100533. ISSN 1570-8268 (2019) [Refereed Article]


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Official URL: https://www.sciencedirect.com/science/article/pii/...

DOI: doi:10.1016/j.websem.2019.100533

Abstract

The Semantic Web is an effort to interchange unstructured data over the Web into a structured format that is processable not only by human beings but also computers. The key backbones of Semantic Web are ontologies and annotations that provide semantics for data. Ontologies are usually created before actual data is populated. Subsequently, they can be incomplete and they often do not provide all aspects that are required for specific domains of knowledge. Additionally, Semantic Web-based ontologies usually suffer from a considerable amount of faulty facts which are known as Semantic Web data quality issues. Due to the complexity of relationships, Semantic Web data quality issues are continuously growing. This paper follows two main objectives. Firstly, it concentrates on a specific Semantic Web data quality issue that indicates incorrect assignment between instances and classes in the ontology. Secondly, the paper shows how to discover new classes which are not defined in the ontology and how to place them in the hierarchical structure of the ontology. To make ends meet, an entropy-based approach called ACE (Automated Class Corrector and Enricher) is proposed that not only evaluates the correctness and incorrectness of relationships between instances and classes but also generates new classes to enrich ontologies. The contributions of ACE have been also explained throughout the paper. Initial experiments conducted on a Semantic Web dataset demonstrate the effectiveness of the ACE.

Item Details

Item Type:Refereed Article
Keywords:Semantic Web mining, knowledge discovery, ontology, information theory, incorrect class assignment, Semantic Web data quality issues, class enrichment, ontology enrichment
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Pattern Recognition and Data Mining
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:138228
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2020-03-27
Last Modified:2020-04-04
Downloads:0

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