With the increasing of data on the internet, data analysis has become
inescapable to gain time and efficiency, especially in bibliographic
information retrieval systems. We can estimate the number of actual scientific
journals points to around 40,000 with about four million articles published each year. Machine
learning and deep learning applied to recommender systems had become
unavoidable whether in industry or in research. In this current, we propose an
optimized interface for bibliographic information retrieval as a running example, which allows different kind of
researchers to find their needs following some relevant criteria through
natural language understanding. Papers indexed in Web of Science and Scopus are
in high demand. Natural language including text and linguistic-based
techniques, such as tokenization, named entity recognition, syntactic and
semantic analysis, are used to express natural language queries. Our Interface
uses association rules to find more related papers for recommendation. Spanning
trees are challenged to optimize the search process of the system.
References
[1]
Lal, N., Qamar, S. and Shiwani, S. (2016) Information Retrieval System and Challenges with Dataspace. International Journal of Computer Applications, 147, 23-28.
https://doi.org/10.5120/ijca2016911128
[2]
Osadchiy, T., Poliakov, I., Olivier, P., Rowland, M. and Foster, E. (2018) Recommender System Based on Pairwise Association Rules. Expert Systems with Applications, 115, 535-542. https://doi.org/10.1016/j.eswa.2018.07.077
[3]
Chakraoui, M. and El Kalay, A. (2016) Efficiency of Indexing Database Systems and Optimising Its Implementation in NAND Flash Memory. International Journal of Systems, Control and Communications, 7, 221-239.
https://doi.org/10.1504/IJSCC.2016.077406
[4]
Chakraoui, M. and El Kalay, A. (2016) Optimization of Local Parallel Index (LPI) in Parallel/Distributed Database Systems. International Journal, 11, 2755-2762.
https://doi.org/10.21660/2016.27.1322
[5]
Chakraoui, M., El Kalay, A. and Mouhni, N. (2016) Tuning Different Types of Complex Queries Using the Appropriate Indexes in Parallel/Distributed Database Systems. International Journal, 11, 2267-2274.
https://doi.org/10.21660/2016.24.1392
[6]
Lika, B., Kolomvatsos, K. and Hadjiefthymiades, S. (2014) Facing the Cold Start Problem in Recommender Systems. Expert Systems with Applications, 41, 2065-2073. https://doi.org/10.1016/j.eswa.2013.09.005
[7]
Brusilovsky, P., Kobsa, A. and Nejdl, W. (2007) Data Mining for Web Personalization. Springer-Verlag, Berlin, 90-135.
[8]
Tso-Sutter, K.H.L., Marinho, L.B. and Schmidt-Thieme, L. (2008) Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms. ACM SAC’08, Ceara, 16-20 March 2008, 1995-1999. https://doi.org/10.1145/1363686.1364171
[9]
Agrawal, R. and Sricant, R. (1994) Fast Algorithms for Mining Association Rules. VLDB’94, Proceedings of 20th International Conference on Very Large Data Bases, Santiago de Chile, 12-15 September 1994, 487-490.
[10]
Deshpande, D.S. (2014) A Novel Approach for Association Rule Mining using Pattern Generation. International Journal of Information Technology and Computer Science, 11, 59-65. https://doi.org/10.5815/ijitcs.2014.11.09
[11]
Amin, H., Devi, A. and Ul Amin, N. (2019) Predictive Analysis of Heart Disease Using K-Means and Apriori Algorithms. JASC: Journal of Applied Science and Computations, 6, 2183-2189.
[12]
Pop, P.C. (2020) The Generalized Minimum Spanning Tree Problem: An Overview of Formulations, Solution Procedures and Latest Advances. European Journal of Operational Research, 283, 1-15. https://doi.org/10.1016/j.ejor.2019.05.017
[13]
Kruskal, J.B. (1956) On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Proceedings of the American Mathematical Society, 7, 48-50.
https://doi.org/10.1090/S0002-9939-1956-0078686-7
[14]
Biswas, P., Goel, M., Negi, H. and Datta, M. (2016) An Efficient Greedy Minimum Spanning Tree Algorithm Based on Vertex Associative Cycle Detection Method. Procedia Computer Science, 92, 513-519.
https://doi.org/10.1016/j.procs.2016.07.376
[15]
Zhu, Y.J., Yan, E. and Song, I.-Y. (2017) A Natural Language Interface to a Graph-Based Bibliographic Information Retrieval System. Data & Knowledge Engineering, 111, 73.
[16]
Li, F. and Jagadish, H.V. (2014) Constructing an Interactive Natural Language Interface for Relational Databases. Proceedings of the VLDB Endowment 8, 73-84.
https://doi.org/10.14778/2735461.2735468
[17]
Carrer-Neto, W., Hernández-Alcaraz, M.L., Valencia-García, R. and García-Sánchez, F. (2012) Social Knowledge-Based Recommender System. Application to the Movies Domain. Expert Systems with Applications, 39, 10990-11000.
https://doi.org/10.1016/j.eswa.2012.03.025
[18]
Tuzhilin, A. and Adomavicius, G. (2015) Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17, 734-749.
https://doi.org/10.1109/TKDE.2005.99
[19]
Sujatha, B. and Raju, D.S.V. (2016) Ontology Based Natural Language Interface for Relational Databases. Procedia Computer Science, 92, 487-492.
https://doi.org/10.1016/j.procs.2016.07.372
[20]
Wachtel, A., Weigelt, S. and Tichy, W.F. (2015) Initial Implementation of Natural Language Turn-Based Dialog System. Procedia Computer Science, 84, 49-56.
https://doi.org/10.1016/j.procs.2016.04.065
