Abstract
Contemporary financial institutions are relying on varied and voluminous data and so they need advanced technologies to provide their customers with the best possible services. Capturing the meaning, or semantics, of data and presenting these semantics in simplified yet relevant models are key challenges to achieving this. Formal Concept Analysis (FCA) automates the analysis of properties and instances of the data, generating a lattice which groups properties and instances into concepts. This lattice can be used as automatically generated semantic structure describing the domain, yet the complexity and size of the resultant lattice render this technique unusable in most practical cases involving financial data. To tackle this, our Ontology-informed Lattice Reduction approach can guide the reduction of the lattices generated from financial sampled data. We validate the adaptation of the approach to the financial domain through a real-world asset allocation case study, demonstrating that the approach achieves good overall performance and relevant results.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Exchange Traded Funds or ETFs are a basket of other assets that are designed to trace the performance of an index.
References
De Mauro, A., Greco, M., Grimaldi, M.: A formal definition of big data based on its essential features. Libr. Rev. 65(3), 122–135 (2016)
Singh, P.K., Kumar, C.A., Gani, A.: A comprehensive survey on formal concept analysis, its research trends and applications. Int. J. Appl. Math. Comput. Sci. 26(2), 495–516 (2016)
Rouane, M.H., Huchard, M., Napoli, A., Valtchev, P.: A proposal for combining formal concept analysis and description logics for mining relational data. In: Kuznetsov, S.O., Schmidt, S. (eds.) ICFCA 2007. LNCS (LNAI), vol. 4390, pp. 51–65. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-70901-5_4
Dias, S.M., Vieira, N.J.: Concept lattices reduction: definition, analysis and classification. Expert Syst. Appl. 42(20), 7084–7097 (2015)
Ignatov, D.I.: Introduction to formal concept analysis and its applications in information retrieval and related fields. In: Braslavski, P., Karpov, N., Worring, M., Volkovich, Y., Ignatov, D.I. (eds.) RuSSIR 2014. CCIS, vol. 505, pp. 42–141. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25485-2_3
Baader, F., Ganter, B., Sertkaya, B., Sattler, U.: Completing description logic knowledge bases using formal concept analysis. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, pp. 230–235 (2007)
Stumme, G.: Using ontologies and formal concept analysis for organizing business knowledge. In: Becker, J., Knackstedt, R. (eds.) Wissensmanagement mit Referenzmodellen, pp. 163–174. Physica, Heidelberg (2002)
Sarmah, A.K., Hazarika, S.M., Sinha, S.K.: Formal concept analysis: current trends and directions. Artif. Intell. Rev. 44(1), 47–86 (2015)
Quboa, Q., Behnaz, A., Mehandjiev, N., Rabhi, F.: Ontology-informed lattice reduction using the discrimination power index. In: Proceedings of the 24th International Conference on Conceptual Structures (ICCS), Marburg, Germany, July 2019
Behnaz, A., Natarajan, A., Rabhi, F.A., Peat, M.: A semantic-based analytics architecture and its application to commodity pricing. In: Feuerriegel, S., Neumann, D. (eds.) FinanceCom 2016. LNBIP, vol. 276, pp. 17–31. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52764-2_2
LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 21–32 (2011)
Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)
Financial Services Standards. http://www.omg.org/hot-topics/finance.htm. Accessed 19 Apr 2018
Belohlavek, R., Trnecka, M.: Basic level of concepts in formal concept analysis. In: Domenach, F., Ignatov, D.I., Poelmans, J. (eds.) ICFCA 2012. LNCS (LNAI), vol. 7278, pp. 28–44. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29892-9_9
Singh, P.K., Kumar, C.A.: Concept lattice reduction using different subset of attributes as information granules. Granul. Comput. 2(3), 159–173 (2017)
Belohlavek, R., Vychodil, V.: Formal concept analysis with background knowledge: attribute priorities. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 39(4), 399–409 (2009)
Zhang, S., Guo, P., Zhang, J., Wang, X., Pedrycz, W.: A completeness analysis of frequent weighted concept lattices and their algebraic properties. Data Knowl. Eng. 81, 104–117 (2012)
Bělohlávek, R., Sklenář, V., Zacpal, J.: Formal concept analysis with hierarchically ordered attributes. Int. J. Gen. Syst. 33(4), 383–394 (2004)
Domenach, F., Portides, G.: Similarity measures on concept lattices. In: Wilhelm, A.F.X., Kestler, H.A. (eds.) Analysis of Large and Complex Data. SCDAKO, pp. 159–169. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25226-1_14
Choi, S.S., Cha, S.H., Tappert, C.C.: A survey of binary similarity and distance measures. J. Syst. Cybern. Inf. 8(1), 43–48 (2010)
W3C, SPARQL 1.1 Query Language. https://www.w3.org/TR/sparql11-query. Accessed 23 Apr 2018
Sharpe, W.F.: Asset allocation: management style and performance measurement. J. Portfolio Manag. 18(2), 7–19 (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Quboa, Q., Mehandjiev, N., Behnaz, A. (2019). Applying Ontology-Informed Lattice Reduction Using the Discrimination Power Index to Financial Domain. In: Mehandjiev, N., Saadouni, B. (eds) Enterprise Applications, Markets and Services in the Finance Industry. FinanceCom 2018. Lecture Notes in Business Information Processing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-030-19037-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-19037-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19036-1
Online ISBN: 978-3-030-19037-8
eBook Packages: Computer ScienceComputer Science (R0)