Abstract
Motivated by the assumption that Semantic Web technologies, especially those underlying the Linked Data paradigm, are not sufficiently exploited in the field of financial information management towards the automatic discovery and synthesis of knowledge, an architecture for a knowledge base for the financial domain in the Linked Open Data (LOD) cloud is presented in this paper. Furthermore, from the assumption that recommendation systems can be used to make consumption of the huge amounts of financial data in the LOD cloud more efficient and effective, we propose a deep learning-based hybrid recommendation system to enable end user access to the knowledge base. We implemented a prototype of a knowledge base for financial news as a proof of concept. Results from an Information Systems-oriented validation confirm our assumptions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Berners-Lee, T.: The semantic web. Sci. Am. 284, 34–43 (2001)
Zhang, S., Yao, L., Sun, A.: Deep Learning based Recommender System: A Survey and New Perspectives (2017). ArXiv170707435 Cs
Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5, 199–220 (1993)
Lee, V., Goto, M., Hu, B., Naseer, A., Vandenbussche, P.-Y., Shakair, G., Rodrigues, E.M.: Exploiting linked data in financial engineering. In: Liu, K., Gulliver, S.R., Li, W., Yu, C. (eds.) ICISO 2014. IFIP AICT, vol. 426, pp. 116–125. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-55355-4_12
Ashraf, J., Hussain, O.K.: Integrating financial data using semantic web for improved visibility. In: 2012 Eighth International Conference on Semantics, Knowledge and Grids, pp. 265–268 (2012)
Wong, W., Liu, W., Bennamoun, M.: Ontology learning from text: a look back and into the future. ACM Comput. Surv. 44, 20:1–20:36 (2012)
Petasis, G., Karkaletsis, V., Paliouras, G., Krithara, A., Zavitsanos, E.: Ontology population and enrichment: state of the art. In: Paliouras, G., Spyropoulos, C.D., Tsatsaronis, G. (eds.) Knowledge-Driven Multimedia Information Extraction and Ontology Evolution. LNCS (LNAI), vol. 6050, pp. 134–166. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20795-2_6
Liu, K., Hogan, W.R., Crowley, R.S.: Natural language processing methods and systems for biomedical ontology learning. J. Biomed. Inform. 44, 163–179 (2011)
Sugibuchi, T., Tanaka, Y.: Interactive web-wrapper construction for extracting relational information from web documents. In: Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, pp. 968–969. ACM, New York (2005)
Park, S.-B., Kim, S.-S., Oh, S., Zeong, Z., Lee, H., Park, S.R.: Target concept selection by property overlap in ontology population. Int. J. Comput. Electr. Autom. Control Inf. Eng. 2, 50–54 (2008)
Kaufmann, E., Bernstein, A.: Evaluating the usability of natural language query languages and interfaces to semantic web knowledge bases. Web Semant. Sci. Serv. Agents World Wide Web 8, 377–393 (2010)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information Tapestry. Commun. ACM 35, 61–70 (1992)
Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., Anil, R., Haque, Z., Hong, L., Jain, V., Liu, X., Shah, H.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10. ACM, New York (2016)
Covington, P., Adams, J., Sargin, E.: Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191–198. ACM, New York (2016)
Okura, S., Tagami, Y., Ono, S., Tajima, A.: Embedding-based news recommendation for millions of users. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1933–1942. ACM, New York (2017)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_1
Palumbo, E., Rizzo, G., Troncy, R.: Entity2Rec: learning user-item relatedness from knowledge graphs for Top-N item recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 32–36. ACM, New York (2017)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM, New York (2014)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.-Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 353–362. ACM, New York (2016)
Lupiani-Ruiz, E., García-Manotas, I., Valencia-García, R., García-Sánchez, F., Castellanos-Nieves, D., Fernández-Breis, J.T., Camón-Herrero, J.B.: Financial news semantic search engine. Expert Syst. Appl. 38, 15565–15572 (2011)
García-Manotas, I., Lupiani, E., García-Sánchez, F., Valencia-García, R.: Populating knowledge based decision support systems. Int. J. Decis. Support Syst. Technol. 2, 1–20 (2010)
Rodríguez-García, M.Á., Valencia-García, R., García-Sánchez, F., Samper-Zapater, J.J.: Ontology-based annotation and retrieval of services in the cloud. Knowl. Based Syst. 56, 15–25 (2014)
Grover, A., Leskovec, J.: Node2Vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM, New York (2016)
Burges, C.J.C.: From RankNet to LambdaRank to LambdaMART: An Overview. Microsoft Research (2010)
Acknowledgements
This work has been supported by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER/ERDF) through project (TIN2016-76323-R) and by the Fundación Séneca through grant 19371/PI/14.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Colombo-Mendoza, L.O., García-Díaz, J.A., Gómez-Berbís, J.M., Valencia-García, R. (2018). A Deep Learning-Based Recommendation System to Enable End User Access to Financial Linked Knowledge. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-92639-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92638-4
Online ISBN: 978-3-319-92639-1
eBook Packages: Computer ScienceComputer Science (R0)