Semantic Web Technologies and NLP Techniques in a Practical Algorithm for Representing Concepts in Linked Data

Shan Gao; Ji Yan Wu

doi:10.4028/www.scientific.net/AMR.886.609

Paper Titles

New Tabu Search Algorithm for Multi-Vehicle and Multi-Cargo Loading Problem
p.589

Comparison of Heuristics for Resolving the Traveling Salesman Problem with Information Technology
p.593

Evaluation of Ground Thermal Infrared Camouflage Based on Dynamic DEA Model
p.598

Applied Technology for Usage Control Model in Pervasive Computing
p.605

Semantic Web Technologies and NLP Techniques in a Practical Algorithm for Representing Concepts in Linked Data
p.609

ODF: An Efficient OWL-Based Linked Course Data Generating Framework
p.613

Research on Information Applied Technology with Medical Search Engine for the General Public: A Survey of Demand
p.617

Applied Technology in an Interactive Design for a Web-Based Language Teaching System
p.621

Applied Technology in an Interactive System Design for ELT
p.625

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 886Semantic Web Technologies and NLP Techniques in a...

Semantic Web Technologies and NLP Techniques in a Practical Algorithm for Representing Concepts in Linked Data

Abstract:

The algorithms used to representing concepts in linked data are time consuming. In this paper we propose an algorithm, called PARC, to infer automatically the function of citations by means of Semantic Web technologies and NLP techniques. We also present some preliminary experiments and discuss some strengths and limitations of this approach, a novel method to analyze this phenomenon, based on a thorough theoretical analysis, as well as a novel graph-based method to resolve such issues to some extent. Our experiments on DBpedia show that our method can used for representing concepts in web of linked data.

You might also be interested in these eBooks

Advanced Research on Material Science, Environment Science and Computer Science III

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 886)

Pages:

609-612

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.886.609

Citation:

Cite this paper

Online since:

January 2014

Authors:

Shan Gao*, Ji Yan Wu

Keywords:

Concept, Linked Data, NLP, Practical Algorithm, Semantic Web

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Ming Xie. MULTI-GRANULARITY KNOWLEDGE MINING ON WEB. International Journal of Software Engineering and Knowledge Engineering (IJSEKE), 2012. 2.

Google Scholar

[2] Ming Xie, Chanle Wu. Open Rainbow Services-Oriented Testbed: Low Cost Cloud Computing Platform for Open Systems Research. In: Proceedings of the Intelligent Systems and Applications (ISA) 2010, 2010. 5.

DOI: 10.1109/iwisa.2010.5473267

Google Scholar

[3] Rajesh Chitnis, Fedor V. Fomin, Petr A. Golovach. Preventing Unraveling in Social Networks Gets Harder. Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013. 7.

DOI: 10.1609/aaai.v27i1.8462

Google Scholar

[4] Varish M. et al., 2012. A Domain Independent Framework for Extracting Linked Semantic Data from Tables, InCollection, Search Computing - Broadening Web Search.

Google Scholar

[5] Ming Xie. Semantic-Based Linked Data Mining and Services. Journal of Information and Compu- tational Science, 2011. 12.

Google Scholar

[6] Ming Xie. Knowledge Topic Aware-based Modeling, Optimzation and on Web of Collaborative Logistics System. In: Proceedings of the International Conference of China Communication Tech- nology2010, 2010. 11.

Google Scholar

[7] Ming Xie. Intelligent Knowledge-Point Auto-Extracting Model in Web Learning Resources. Journal of Computational Information Systems, 2010. 6.

Google Scholar

[8] Ming Xie. A New Intelligent Topic Extraction Model on Web. Journal of Computers, 2011. 3.

Google Scholar

[9] Ming Xie. Semantic Knowledge Mining on Web. Journal of Computational Information Systems, 2011. 11.

Google Scholar