SwiftTaxonomy is a novel system designed to expedite the induction of multiple taxonomies, thereby enhancing faceted text exploration. Leveraging state-of-the-art algorithms and machine learning methodologies, SwiftTaxonomy enables the rapid generation of tailored taxonomies for diverse datasets. This facilitates efficient organization and navigation of complex textual information, empowering users with advanced capabilities for knowledge discovery and exploration. In this paper, we outline the architecture and key features of SwiftTaxonomy, showcasing its effectiveness in accelerating multi-taxonomy induction and facilitating enhanced faceted text exploration.

SwiftTaxonomy, multi-taxonomy induction, faceted text exploration

Wong, W., Wei, L., & Bennamoun, M. : Ontology Learning from Text: A Look Back and Into the Future. ACM Computing Surveys, 44 (20).(2012).

Snow, R., Jurafsky, D., & Ng, A. (2006). Semantic Taxonomy Induction from Heterogenous Evidence. Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics

Yang, H., & Callan, J. (2009). A metric-based framework for automatic taxonomy induction. ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, Vol. 1-1, pp. 271-279.

Navigli, R., Velardi, P., & Faralli, S. (2011). A Graph-Based Algorithm for Inducing Lexical Taxonomies from Scratch. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (pp. 1872-1877). Barcelona, Spain: Toby Walsh.

Grefenstette, G. (2015). Simple Hypernym Extraction Methods. HAL-INRIASAC. Palaiseau, France.

Lefever, E. (2015). LT3: A Multi-modular Approach to Automatic Taxonomy Construction. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)-ACL, (pp. 943–947). Denver, USA.

Wong, W. (2009). Learning Lightweight Ontologies from Text across Different Domains using the Web as Background Knowledge. Doctoral Thesis, The University of Western Australia, Perth.

Bel, N., Papavasiliou, V., Prokopidis, P., Toral, A., Arranz, V.: Mining and exploiting domainspecific corpora in the PANACEA platform. In: The 5th Workshop on Building and Using Comparable Corpora (2012)

Wong, W., Liu, W., &Bennamoun, M. (2007, December). Determining termhood for learning domain ontologies using domain prevalence and tendency. In Proceedings of the sixth Australasian conference on Data mining and analytics-Volume 70 (pp. 47-54). Australian Computer Society, Inc.

deMelo, G. and Weikum, G., 2010, October. MENTA: Inducing multilingual taxonomies from Wikipedia. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 1099-1108). ACM.