Abstract
In this paper, for the low similarity computation accuracy of concept in the field of domain ontology mapping, formal concept analysis theory and rough set theory are introduced to similarity computation. Jointly considering attribute hierarchies in concept lattice, the semantic hierarchy of the concepts are weighted differently, and the theory and methods of semantic similarity based on concept hierarchy is given. Finally, similarity computing model is prospected. Experimental results show the model has a high computational accuracy.
Fund projects: Key Project of AnHui Education Department (KJ2015B023by); Key Project of Bengbu Medical College (BYKY1409ZD).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wu, Q., Liu, Z.: The rough concept in FCA. J. Chin. Comput. Syst. 26, 1563–1565 (2014). Beijing
Shao, M.: Set approximations in fuzzy formal concept analysis. Fuzzy Sets Syst., 2627–2640 (2013). Beijing
Xie, Z., Liu, Z.: Concept analysis knowledge acquisition in missing-context. Comput. Sci., 36–39 (2010). Beijing
Mao, H.: An algorithm of concept lattice based on matrix column rank with attribute priority. J. Hebei Univ., 130–133 (2009). Baoding
Kesorn, K., Poslad, S.: An enhanced bag of visual words vector space model to represent visual content in athletics images. IEEE Trans. Multimedia 14(1), 211–222 (2012)
Tang, Y., Xu, D., et al.: A novel image scene classification method based on category topic simplex. J. Graph. 15(7), 1067–1073 (2010)
Fernando, B., Fremont, E.: Supervised learning of Gaussian mixture models for visual vocabulary. Pattern Recognit. 45, 897–907 (2012)
Wang, Y., Li, Y., Gao, W.: Detecting visual words. J. Beijing Inst. Technol. 28(5), 410–413 (2008). Beijing
Maree, R., Denis, P., et al.: Incremental Indexing and Distributed Image Search Using Shared Randomized Vocabularies, pp. 91–100. ACM Press, New York (2010)
Sanchez, J., Perronnin, F., et al.: Image classification with the fisher vector: theory and practice. Int. J. Comput. Vision 105(3), 222–245 (2013)
Yuan, W., Tao, J., et al.: Information retrieval and data mining based on open network knowledge. J. Comput. Res. Dev. 52(2), 456–474 (2014)
Lazabnik, S., Schmid, C., Ponce, J.: Beyond bag of features: pyramid matching for recognizing natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition, New York, pp. 2169–2178 (2006)
Li, F., Perona, P.: Abayesian hierarchical model for learning natural scene categories. In: IEEE International Conference on Computer and Pattern Recognition, San Diego, pp. 524–531 (2005)
Emrah, E., Nadiz, A.: Scene classification using spatial pyramid of latent topics. In: 20th International Conference on Pattern Recognition, Istanbul, pp. 3603–3606 (2010)
Liu, S.Y., Xu, D., Feng, S.H., Liu, D.: A novel visual words definition algorithm of image patch based on semantic information. Acta Electronica Sinica 38(5), 1156–1161 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, K. (2016). Research on the Calculation Method of Semantic Similarity Based on Concept Hierarchy. In: Yang, M., Liu, S. (eds) Machine Translation. CWMT 2016. Communications in Computer and Information Science, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-3635-4_10
Download citation
DOI: https://doi.org/10.1007/978-981-10-3635-4_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3634-7
Online ISBN: 978-981-10-3635-4
eBook Packages: Computer ScienceComputer Science (R0)