Abstract
Translation of a natural language query into a semantically sound and semantically meaningful structured query and mapping it onto a target database is a challenge for the interactive application designers. The realization of such a complex translation scenario requires a systematic approach with an end-to-end capability. The proposed deep learning-based ontological framework for semantic query in multilingual databases (DLOMLD) receives a natural language query from the user and maps that onto a semantically meaningful structured query using generative adversarial network and fires the query on an identified database. The DLOMLD possesses salient features such as the language translation capabilities, extraction of metadata from underlying relational databases and utilization of established ontological predicates. The functional merits of the DLOMLD are demonstrated while taking education domain as a case study, in a top-down manner elaborating the negotiations between two consecutive layers.
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References
Haralambous, Y., Lenca, P.: Text classification using association rules. In: Dependency Pruning and Hyperonymizationm Proceedings of DMNLP, Workshop at ECML/PKDD, Nancy, France (2014)
Salton, G.-J., Singhal, A., Buckley, C., Mitra, M.: Automatic Text decomposition using text segments and text themes. In: Proceedings of the seventh ACM Conference on hypertext, Washington D.C (1996)
Liu, T., Chen, Z., Zhang, B., Ma, W.-y. Wu, G.: Improving text classification using local latent semantic indexing. In: Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM’04), 0-7695-2142-8/04
Zhong Li., Z., Shang, W., Yan, M.: New text classification using Topic Model, ICIS (2016)
Chandra, G., Dwivedi, S.: A literature survey on various approaches of word sense disambiguation. In: 2nd International Symposium on Computational and Business Intelligence, pp. 106–109 (2014)
Zeng, Q.T., Redd, D., Rindflesch, T., Nebeker, J.: Synonym, topic model and predicate-based query expansion for retrieving clinical documents, AMIA Symposium, November 2012, pp. 1050–1059
Chen, Y.-H., Li, S.-F.: Using Latent Dirichlet allocation to improve text classification performance of support vector machine. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1280–1286 (2016)
Umadevi, M., Gandhi, M.: WordNet and Ontology based query expansion for semantic information retrieval in Sports domain. J. Comput. Sci. 11(2), 361–371 (2015)
Christodoulakis, C., Kandogan, E., Terrizzano, I.G.: VIQS- visual interactive exploration of query semantics, ESIDA, ACM, 4503-4903, March 2017
Ramada, M.S., da Silva, J.C., de Sa Leit˜ao-Junior, P.: Data Extraction from Structured Databases using Keyword-based Queries, SBBD Proceedings, ISSN 2316-5170 Oct 2014
Kolikipogu, R., Padmaja Rani, B., Swapna, N.: Pseudo relevance feedback by linking WordNet for expanding queries in information retrieval process. Int. J. Model. Optim. 3(5), 462–467, October 2013
Agarwal, N., Gupta, R.: Metadata based multi-Labeling for YouTube videos. In: 7th International Conference on Cloud Computing, Data Science & Engineering IEEE, pp. 586–590 (2017)
Zhang, Y., Gan, Z., Fan, K., Chen, Z. et.al.: Adversarial feature matching for text generation. In: International Conference on Machine Learning, Sydney, Australia (2017)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680 (2014)
Satyamurty, Ch.V.S., Murthy, J.V.R., Raghava, M: Developing higher education ontology using protégé: reasoning. In: 1st International Conference—SCI-2017-Springer-March (2017)
Satyamurty, Ch.V.S., Murthy, J.V.R., Raghava, M.: Metadata based Semantic query in Relational Databases. In: 4th International Conference -INDIA -2017 Springer, June 2017
Fell Baum, C.: Wordnet: An electronic lexical database (1998)
Satyamurty, ChVS, Dr, J.V.R., Murthy, M.R.: Metadata based semantic query in indian language databases. IJCER 08(06), 50–53 (2018)
Satyamurty, Ch.V.S., Murthy, J.V.R., Raghava, M.: Meta databased semantic query in Multilingual databases. In: International Conference FICTA-2017, Information and decision sciences, Advances in intelligent systems and Computing Springer 701, pp. 249–253
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Satyamurty, C.V.S., Murthy, J.V.R., Raghava, M. (2020). Metadata-Based Ontological Framework for Semantic Query in Multilingual Databases. In: Satapathy, S., Bhateja, V., Nguyen, B., Nguyen, N., Le, DN. (eds) Frontiers in Intelligent Computing: Theory and Applications. Advances in Intelligent Systems and Computing, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-32-9186-7_32
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