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2018 | OriginalPaper | Chapter

A Semantic Use Case Simulation Framework for Training Machine Learning Algorithms

Authors : Nicole Merkle, Stefan Zander, Viliam Simko

Published in: Knowledge Engineering and Knowledge Management

Publisher: Springer International Publishing

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Abstract

To train autonomous agents, large training data sets are required to provide the necessary support in solving real-world problems. In domains such as healthcare or ambient assisted living, such training sets are often incomplete or do not cover the unique requirements and constraints of specific use cases, leading to the cold-start problem. This work describes a semantic simulation framework that generates qualitative use case specific data for Machine-Learning (ML) driven agents, thus solving the cold-start problem. By combing simulated data with axiomatically formalized use case requirements, we are able to train ML algorithms without real-world data at hand. We integrate domain specific guidelines and their semantic representation by using SHACL/RDF(S) and SPARQL CONSTRUCT queries. The main benefits of this approach are (1) portability to other domains, (2) applicability to various ML algorithms, and (3) mitigation of the cold-start problem or sparse data.

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Mariappan, A.K., Sobhanam, H.: A hybrid approach to solve cold start problem in recommender systems using association rules and clustering technique. Int. J. Comput. Appl. 74(4), 17–23 (2013)

Carlson, A., et al.: Toward an architecture for never-ending language learning. In: AAAI. vol. 5, p. 3. Atlanta (2010)

Di Noia, T., Ostuni, V.C.: Recommender systems and linked open data. In: Faber, W., Paschke, A. (eds.) Reasoning Web 2015. LNCS, vol. 9203, pp. 88–113. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21768-0_4CrossRef

Godoy, D., Amandi, A.: An agent-based recommender system to support collaborative web search based on shared user interests. In: Haake, J.M., Ochoa, S.F., Cechich, A. (eds.) CRIWG 2007. LNCS, vol. 4715, pp. 303–318. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74812-0_24CrossRef

Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert. Syst. Appl. 41(4, Part 2), 2065–2073 (2014). https://doi.org/10.1016/j.eswa.2013.09.005CrossRef

Mantrach, A.: Cold start solutions for recommender systems. https://research.yahoo.com/_c/uploads/SeminarUCSD.pdf

Mehta, R., Rana, K.: A review on matrix factorization techniques in recommender systems. In: 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA), pp. 269–274, April 2017

Middleton, S.E., Alani, H., Roure, D.D.: Exploiting synergy between ontologies and recommender systems. CoRR arXiv:cs/0204012 (2002)

Mnih, V., et al.: Playing atari with deep reinforcement learning. CoRR arXiv:cs/0204012 (2013)

10.

Musto, C.: Introducing linked open data in graph-based recommender systems. Inf. Process. Manag. 53(2), 405–435 (2017). https://doi.org/10.1016/j.ipm.2016.12.003CrossRef

11.

Nouali, O., Belloui, A.: Using semantic web to reduce the cold-start problems in recommendation systems. In: 2009 Second International Conference on the Applications of Digital Information and Web Technologies, pp. 525–530, August 2009

12.

Palau, J., Montaner, M., López, B., de la Rosa, J.L.: Collaboration analysis in recommender systems using social networks. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds.) CIA 2004. LNCS (LNAI), vol. 3191, pp. 137–151. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30104-2_11CrossRef

13.

Peis, E., et al.: Semantic recommender systems. Analysis of the state of the topic. Hipertext.net 6 (2008). http://hipertext.net/english/pag1031.htm

14.

Rubens, N., Elahi, M., Sugiyama, M., Kaplan, D.: Active learning in recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 809–846. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_24CrossRef

15.

Schein, A.I., Popescul, A., Unger, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of SIGIR 2002, pp. 253–260. Tampere, Finland (2002). https://doi.org/10.1145/564376.564421

16.

Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison (2010). http://axon.cs.byu.edu/~martinez/classes/778/Papers/settles.activelearning.pdf

17.

Son, L.H.: Dealing with the new user cold-start problem in recommender systems: a comparative review. Inf. Syst. 58, 87–104 (2016). http://www.sciencedirect.com/science/article/pii/S0306437914001525CrossRef

18.

Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning, 1st edn. MIT Press, Cambridge, MA, USA (1998)

19.

Thanh-Tai, H., Nguyen, H.-H., Thai-Nghe, N.: A semantic approach in recommender systems. In: Dang, T.K., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds.) FDSE 2016. LNCS, vol. 10018, pp. 331–343. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48057-2_23CrossRef

20.

Tomeo, P., et al.: Exploiting linked open data in cold-start recommendations with positive-only feedback. In: Proceedings of the 4th Spanish Conference on Information Retrieval. pp. 11:1–11:8. CERI 2016. ACM, New York (2016). https://doi.org/10.1145/2934732.2934745

21.

Yang, R.: Using semantic technology to improve recommender systems based on slope one. In: Li, J., Qi, G., Zhao, D., Nejdl, W., Zheng, H.T. (eds.) Semantic Web and Web Science, pp. 11–23. Springer, New York (2012). https://doi.org/10.1007/978-1-4614-6880-6_2CrossRef

Title: A Semantic Use Case Simulation Framework for Training Machine Learning Algorithms
Authors: Nicole Merkle
Stefan Zander
Viliam Simko
Publisher: Springer International Publishing
Book: Knowledge Engineering and Knowledge Management
Print ISBN: 978-3-030-03666-9

Electronic ISBN: 978-3-030-03667-6

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-03667-6_16

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