Aiming at the existing problems in the traditional text classification methods and the current semantic classification methods, a new text classification model based on SUMO and WordNet Ontology integration is proposed. This model utilizes the mapping relations between WordNet synsets and SUMO Ontology concepts to map terms in document-words vector space into the corresponding concepts in Ontology, and forms document-concepts vector space to classify texts automatically. The experiment results show that the proposed method can greatly decrease the dimensionality of vector space and improve the text classification performance.
胡泽文, 王效岳, 白如江. 基于SUMO和WordNet本体集成的文本分类模型研究[J]. 现代图书情报技术, 2011, 27(1): 31-38.
Hu Zewen, Wang Xiaoyue, Bai Rujiang. Study on Text Classification Model Based on SUMO and WordNet Ontology Integration. New Technology of Library and Information Service, 2011, 27(1): 31-38.
[1] Bloehdorn S, Hotho A.Boosting for Text Classification with Semantic Features. In: Proceedings of the Workshop on the Mining for and from the Semantic Web at the 10th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA. 2004:70-87.
[2] Mitra V, Wang C J, Banerjee S. A Neuro-SVM Model for Text Classification Using Latent Semantic Indexing. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2005, Montreal, QC, Canada. 2005:564-569.
[3] Marina L, Mark L, Slava K. Classification of Web Documents Using Concept Extraction from Ontologies. In: Proceedings of the 2nd International Workshop Autonomous Intelligent Systems: Multi-Agents and Data Mining, AIS-ADM 2007. LNAI 4476. Heidelberg: Springer-Verlag, 2007:287–292.
[4] Carpineto C, Michini C, Nicolussi R. A Concept Lattice-Based Kernel for SVM Text Classification. In: Proceedings of the 7th International Conference on Formal Concept Analysis, ICFCA 2009. LNAI 5548. Heidelberg: Springer-Verlag, 2009:237-250.
[7] Ginte F, Pyysalo S, Boberg J, et al. Ontology-based Feature Transformations: A Data-driven Approach. In: Proceedings of the 4th International Conference, EsTAL 2004-Advances in Natural Language Processing. Berlin: Springer, 2004: 279-290.
[10] Lee Y H, Tsao W J, Chu T H. Use of Ontology to Support Concept-based Text Categorization. In: Proceedings of Designing E-Business Systems: Markets, Services, and Networks - 7th Workshop on E-Business, Web 2008. Heidelberg: Springer-Verlag, 2009: 201-213.
[12] Ahrens K, Chung S F,Huang C R. From Lexical Semantics to Conceptual Metaphors: Mapping Principle Verification with WordNet and SUMO. In: Proceedings of the 5th Chinese Lexical Semantics Worksho P(CLSW-5), Singapore. 2004:99-106.
[13] George A M. WordNet: A Lexical Database for English
[J]. Communications of the ACM, 1995, 38(11): 39-41.
[14] Pease A, Niles I, Li J. The Suggested Upper Merged Ontology: A Large Ontology for the Semantic Web and Its Applications. In: Working Notes of the AAAI-2002 Workshop on Ontologies and the Semantic Web, Edmonton, Canada. 2002:2002.