Ontology Development for Classification: Spirals - A Case Study in Space Object Classification

Bin Liu, Li Yao, Junfeng Wu, Zheyuan Ding

2017

Abstract

Ontology-based classification (OBC) has been used extensively. The classification ontologies are the grounds of the OBC systems. It is an urgent call for a method to guide the development of classification ontology, to get better performances for OBC. A method for developing classification ontology named Spirals is proposed, taking the development of the ontology for space object classification named OntoStar as an example. Firstly, soft sensing data and hard sensing data are collected. Then, various kinds of human knowledge and knowledge obtained by machine learning are combined to build the ontology. Finally, data-driven evaluation and promotion is deployed to assess and promote the ontology. Experiments of the OBC system built upon OntoStar show that the data-driven evaluation and promotion in Spirals increases the accuracy of space object classification by 4.1%. OBC is more robust than baseline classifiers with respect to a missing feature in the test data. When classifying space objects with the feature “size” missing in the test data, OBC keeps its FP rate, while that of baseline classifiers increases between 3.9% and 35.5%; the losing accuracy of OBC is 0.2%, while that of baseline classifiers ranges from 1.1% to 69.5%.

References

  1. Belgiu, M., Tomljenovic, I., Lampoltshammer, T. J., Blaschke, T. & Höfle, B. 2014. Ontology-based classification of building types detected from airborne laser scanning data. Remote Sensing, 6, 1347-1366.
  2. Brank, J., Grobelnik, M. & Mladenic, D. A survey of ontology evaluation techniques. Proceedings of the conference on data mining and data warehouses (SigKDD 2005), 2005. 166-170.
  3. Breiman, L. 2001. Random forests. Machine learning, 45, 5-32.
  4. Brewster, C., Alani, H., Dasmahapatra, S. & Wilks, Y. Data driven ontology evaluation. Proceedings of Int. Conf. on Language Resources and Evaluation, 2004 Lisbon.
  5. Casellas, N. 2011. Methodologies, Tools and Languages for Ontology Design, Springer Netherlands.
  6. Cohen, W. W. Fast effective rule induction. Proceedings of the twelfth international conference on machine learning, 1995. 115-123.
  7. Cox, A. P., Nebelecky, C. K., Rudnicki, R., Tagliaferri, W. A., Crassidis, J. L. & Smith, B. 2016. The Space Object Ontology. Fusion 2016. Heidelberg, Germany: IEEExplore.
  8. Dellschaft, K. & Staab, S. Strategies for the Evaluation of Ontology Learning. Conference on Ontology Learning and Population: Bridging the Gap Between Text and Knowledge, 2010. S256.
  9. Di Beneditto, M. E. M. & De Barros, L. N. 2004. Using concept hierarchies in knowledge discovery. Advances in Artificial Intelligence-SBIA 2004. Springer.
  10. Erb, D. R. J. 1995. The Backpropagation Neural Network - A Bayesian Classifier. Clinical Pharmacokinetics, 29, 69-79.
  11. Fürnkranz, J. & Kliegr, T. A Brief Overview of Rule Learning. RuleML 2015: Rule Technologies: Foundations, Tools, and Applications, 2015. 54-69.
  12. Friedman, N., Dan, G. & Goldszmidt, M. 1997. Bayesian Network Classifiers. Machine Learning, 29, 131-163.
  13. Fruh, C., Jah, M., Valdez, E., Kervin, P. & Kelecy, T. 2013. Taxonomy and classification scheme for artificial space objects. Kirtland AFB,NM,87117: Air Force Research Laboratory (AFRL),Space Vehicles Directorate.
  14. Gómez-Romero, J., Serrano, M. A., García, J., Molina, J. M. & Rogova, G. 2015. Context-based multi-level information fusion for harbor surveillance. Information Fusion, 21, 173-186.
  15. Godfray, H. 2007. Linnaeus in the information age. Nature, 446, 259-260.
  16. Haghighi, P. D., Burstein, F., Zaslavsky, A. & Arbon, P. 2013. Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decision Support Systems, 54, 1192-1204.
  17. Han, J., Kamber, M. & Pei, J. 2011. Data mining: concepts and techniques: concepts and techniques, Elsevier.
  18. Han, Y., Sun, H., Feng, J. & Li, L. 2014. Analysis of the optical scattering characteristics of different types of space targets. Measurement Science and Technology, 25, 075203.
  19. Hastings, J., Magka, D., Batchelor, C. R., Duan, L., Stevens, R., Ennis, M. & Steinbeck, C. 2012. Structure-based classification and ontology in chemistry. J. Cheminformatics, 4, 8.
  20. Henderson, L. S. 2014. Modeling, estimation, and analysis of unresolved space object tracking and identification. Doctor Doctoral, The University of Texas at Arlington.
  21. Hloman, H. & Stacey, D. A. 2014. Multiple Dimensions to Data-Driven Ontology Evaluation. Knowledge Discovery, Knowledge Engineering and Knowledge Management. Springer.
  22. Horrocks, Ian, Patel-Schneider, Peter, F., Boley, Harold, Tabet, Said, Grossof & Benjamin 2004. SWRL: A Semantic Web Rule Language Combining OWL and RuleML. World Wide Web Consortium.
  23. Howard, M., Klem, B. & Gorman, J. RSO Characterization with Photometric Data Using Machine Learning. Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, held in Wailea, Maui, Hawaii, September 15-18, 2014, Ed.: S. Ryan, The Maui Economic Development Board, id. 70, 2015. 70.
  24. Kang, S. K., Chung, K. Y. & Lee, J. H. 2015. Ontologybased inference system for adaptive object recognition. Multimedia Tools & Applications, 74, 8893-8905.
  25. Kassahun, Y., Perrone, R., De Momi, E., Berghöfer, E., Tassi, L., Canevini, M. P., Spreafico, R., Ferrigno, G. & Kirchner, F. 2014. Automatic classification of epilepsy types using ontology-based and geneticsbased machine learning. Artificial intelligence in medicine, 61, 79-88.
  26. Keerthi, S., Shevade, S., Bhattacharyya, C. & Murthy, K. 2006. Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation, 13, 637- 649.
  27. Landwehr, N., Hall, M. & Frank, E. 2005. Logistic Model Trees. Machine Learning, 59, 161-205.
  28. Maedche, A. D. 2002. Ontology Learning for the Semantic Web, Kluwer Academic Publishers.
  29. Magka, D. Ontology-based classification of molecules: A logic programming approach. Proceedings of the SWAT4LS conference, 2012.
  30. Maillot, N. E. & Thonnat, M. 2008. Ontology based complex object recognition. Image and Vision Computing, 26, 102-113.
  31. Mansinghka, V., Tibbetts, R., Baxter, J., Shafto, P. & Eaves, B. 2015. BayesDB: A probabilistic programming system for querying the probable implications of data. Computer Science.
  32. Moran, N., Nieland, S., Suntrup, G. T. G. & Kleinschmit, B. 2017. Combining machine learning and ontological data handling for multi-source classification of nature conservation areas. International Journal of Applied Earth Observation & Geoinformation, 54, 124-133.
  33. Pulvermacher, M. K., Brandsma, D. L. & Wilson, J. R. 2000. A space surveillance ontology. MITRE Corporation, Bedford, Massachusetts.
  34. Quinlan, J. R. 2014. C4. 5: programs for machine learning, Elsevier.
  35. Raad, J. & Cruz, C. A Survey on Ontology Evaluation Methods. International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015.
  36. Ruttenberg, B. E., Wilkins, M. P. & Pfeffer, A. Reasoning on resident space object hierarchies using probabilistic programming. Information Fusion (Fusion), 2015 18th International Conference on, 2015. IEEE, 1315- 1321.
  37. Sánchez, D., Batet, M., Martínez, S. & Domingo-Ferrer, J. 2015. Semantic variance: an intuitive measure for ontology accuracy evaluation. Engineering Applications of Artificial Intelligence, 39, 89-99.
  38. Savioli, L. 2015. Analysis of innovative scenarios and key technologies to perform active debris removal with satellite modules.
  39. Suárezfigueroa, M. C., Gómezpérez, A. & Fernándezlópez, M. 2015. The NeOn Methodology framework: A scenario-based methodology for ontology development. Applied Ontology, 10, 107-145.
  40. Zhang, X., Hu, B., Chen, J. & Moore, P. 2013. Ontologybased context modeling for emotion recognition in an intelligent web. World Wide Web, 16, 497-513.
  41. Zhichkin, P., Athey, B., Avigan, M. & Abernethy, D. 2012. Needs for an expanded ontology-based classification of adverse drug reactions and related mechanisms. Clinical Pharmacology & Therapeutics, 91, 963-965.
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Paper Citation


in Harvard Style

Liu B., Yao L., Wu J. and Ding Z. (2017). Ontology Development for Classification: Spirals - A Case Study in Space Object Classification . In Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-246-2, pages 225-234. DOI: 10.5220/0006240002250234


in Bibtex Style

@conference{webist17,
author={Bin Liu and Li Yao and Junfeng Wu and Zheyuan Ding},
title={Ontology Development for Classification: Spirals - A Case Study in Space Object Classification},
booktitle={Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2017},
pages={225-234},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006240002250234},
isbn={978-989-758-246-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Ontology Development for Classification: Spirals - A Case Study in Space Object Classification
SN - 978-989-758-246-2
AU - Liu B.
AU - Yao L.
AU - Wu J.
AU - Ding Z.
PY - 2017
SP - 225
EP - 234
DO - 10.5220/0006240002250234