Ontology-based approach for identifying the credibility domain in social Big Data

ABSTRACT

The challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academics and industry. To address this challenge, semantic analysis of textual data is focused on in this paper. We propose an ontology-based approach to extract semantics of textual data and define the domain of data. In other words, we semantically analyze the social data at two levels: the entity level and the domain level. We have chosen Twitter as a social channel for the purpose of concept proof. Ontologies are used to capture domain knowledge and to enrich the semantics of tweets, by providing specific conceptual representation of entities that appear in the tweets. Case studies are used to demonstrate this approach. We experiment and evaluate our proposed approach with a public dataset collected from Twitter and from the politics domain. The ontology-based approach leverages entity extraction and concept mappings in terms of quantity and accuracy of concept identification.

KEYWORDS:

• Semantic data extraction
• ontology
• social Big Data
• social media
• data analytics
• Twitter
• alchemyapi
• Watson

Acknowledgement

The paper has benefited from comments and suggestions by Professor Richard Baskerville, Department of Computer Information Systems, Georgia State University, and Dr. Nik Thompson, Curtin Business School, Curtin University, the two reviewers, and the editor-in-chief, Professor Clyde Holsapple.

Notes

^1. https://about.twitter.com/company.

^2. https://twitter.com/.

^3. https://www.ibm.com/watson/alchemy-api.html .

^4. dublincore.org/.

^5. http://www.w3.org/2004/02/skos/.

^6. http://sioc-project.org/.

^7. http://umbel.org/.

^8. http://www.freebase.com/.

^9. http://www.foaf-project.org/.

^10. http://www.foaf-project.org/.

^11. dublincore.org/.

^12. http://www.w3.org/2004/02/skos/.

^13. http://sioc-project.org/.

^14. wiki.dbpedia.org/.

^15. http://www.bbc.com/.

Additional information

Notes on contributors

Pornpit Wongthongtham

Pornpit Wongthongtham received the B.Sc. degree in Mathematics, the M.Sc. degree in Computer Science, and the Ph.D degree in Information Systems. She is currently an independent researcher. She was an academic staff member at the Curtin Institute for Computation (CIC) and also at the school of Information Systems, Curtin University, Perth, Australia. Her research interests include Semantic Analytics, Big Social Data Analytics, Ontology Engineering, Semantic Web Technology, and the like.

Bilal Abu Salih

Bilal Abu Salih received the B.Sc. degree in Computer Science from Qatar University, Qatar, and the M.Sc degree in Computer Science from Al-Balqa Applied University, Jordan. He is now pursuing the Ph.D degree in the School of Information Systems, Curtin University, Australia. His current research interests include Big Social Data Analytics, Trust, Semantic Analytics and Distributed Computing.