Abstract
Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “understanding”, and that more data does not entail more knowledge. We propose here a formulation of knowledge extraction in terms of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples. The aim is to let models emerge from data themselves, while inference is turned into a search problem in the space of consistent grammars, induced by samples, given proper generalization operators. We will finally present an application to the extraction of structural models representing user mobility behaviors, based on public datasets.
This work was partially supported by the Italian Ministry of Education, University and Research on the “StartUp” call funding the “BIGGER DATA” project, ref. PAC02L1_0086 – CUP: B78F13000700008.
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Notes
- 1.
A \(I_+\) sample set is said to be structurally complete with respect to an automaton A, if every transition of A is used by at least a string in \(I_+\), and every final state in A corresponds to at least one string in \(I_+\).
- 2.
Software available at: https://github.com/piecot/GI-learning.
References
De Paola, A., Gaglio, S., Lo Re, G., Ortolani, M.: An ambient intelligence architecture for extracting knowledge from distributed sensors. In: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, Seoul, Korea, pp. 104–109. ACM (2009)
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, New York (2011)
Carlsson, G.: Topology and data. Bull. Am. Math. Soc. 46(2), 255–308 (2009)
Higuera, C.: Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, New York (2010)
Cottone, P., Gaglio, S., Lo Re, G., Ortolani, M.: Gaining insight by structural knowledge extraction. In: Proceedings of the Twenty-Second European Conference on Artificial Intelligence, August 2016
Cottone, P., Ortolani, M., Pergola, G.: Detecting similarities in mobility patterns, in STAIRS 2016 - Proceedings of the 8th European Starting AI Researcher Symposium, The Hague, Holland, 26 August–2 September 2016
Walkinshaw, N., Bogdanov, K.: Automated comparison of state-based software models in terms of their language and structure. ACM Trans. Softw. Eng. Methodol. 22(2), 1–37 (2013)
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)
Wolpert, D.H., Macready, W.G.: Coevolutionary free lunches. IEEE Trans. Evol. Comput. 9(6), 721–735 (2005)
Whitley, D., Watson, J.P.: Search Methodologies: Introductory tutorials in optimization and decision support techniques, pp. 317–339. Springer, New York (2005)
Lo Re, G., Peri, D., Vassallo, S.D.: Urban air quality monitoring using vehicular sensor networks. In: Gaglio, S., Lo Re, G. (eds.) Advances onto the Internet of Things: How Ontologies Make the Internet of Things Meaningful, pp. 311–323. Springer, Cham (2014)
De Paola, A., La Cascia, M., Lo Re, G., Morana, M., Ortolani, M.: User detection through multi-sensor fusion in an AmI scenario. In: Proceedings of the 15th International Conference on Information Fusion, pp. 2502–2509 (2012)
Lo Re, G., Morana, M., Ortolani, M.: Improving user experience via motion sensors in an ambient intelligence scenario. In: Pervasive and Embedded Computing and Communication Systems (PECCS), pp. 29–34 (2013)
Gaglio, S., Lo Re, G., Morana, M.: Human activity recognition process using 3-d posture data. IEEE Trans. Hum. Mach. Syst. 45(5), 586–597 (2015)
Ding, N., Melloni, L., Zhang, H., Tian, X., Poeppel, D.: Cortical tracking of hierarchical linguistic structures in connected speech. Nat. Neurosci. 19(1), 158–164 (2016)
Fu, K.S.: Syntactic Methods in Pattern Recognition. Mathematics in Science and Engineering, vol. 112. Academic, New York (1974)
Chomsky, N.: Syntactic Structures. Walter de Gruyter, Berlin (2002)
Gold, E.M.: Language identification in the limit. Inf. Control 10(5), 447–474 (1967)
Dupont, P., Miclet, L., Vidal, E.: What is the search space of the regular inference? In: Carrasco, R.C., Oncina, J. (eds.) ICGI 1994. LNCS, vol. 862, pp. 25–37. Springer, Heidelberg (1994). doi:10.1007/3-540-58473-0_134
Lang, K.J., Pearlmutter, B.A., Price, R.A.: Results of the Abbadingo one DFA learning competition and a new evidence-driven state merging algorithm. In: Honavar, V., Slutzki, G. (eds.) ICGI 1998. LNCS, vol. 1433, pp. 1–12. Springer, Heidelberg (1998). doi:10.1007/BFb0054059
Sebban, M., Janodet, J.-C., Tantini, F.: Blue: a blue-fringe procedure for learning dfa with noisy data
Black, K.: Business Statistics: For Contemporary Decision Making. Wiley, Hoboken (2011)
Chow, T.S.: Testing software design modeled by finite-state machines. IEEE Trans. Softw. Eng. 4(3), 178–187 (1978)
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427–437 (2009)
Balkić, Z., Šoštarić, D., Horvat, G.: GeoHash and UUID identifier for multi-agent systems. In: Jezic, G., Kusek, M., Nguyen, N.-T., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2012. LNCS (LNAI), vol. 7327, pp. 290–298. Springer, Heidelberg (2012). doi:10.1007/978-3-642-30947-2_33
Cottone, P., Ortolani, M., Pergola, G.: GI-learning: an optimized framework for grammatical inference. In: Proceedings of the 17th International Conference on Computer Systems and Technologies (Compsystech 2016). ACM, Palermo (2016)
Chon, J., Cha, H.: Lifemap: a smartphone-based context provider for location-based services. IEEE Perv. Comput. 2, 58–67 (2011)
Zheng, Y., Liu, L., Wang, L., Xie, X.: Learning transportation mode from raw Gps data for geographic applications on the web. In: Proceedings of the 17th International Conference on World Wide Web, pp. 247–256. ACM (2008)
Chen, X., Pang, J., Xue, R.: Constructing and comparing user mobility profiles. ACM Trans. Web 8(4), 21:1–21:25 (2014)
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Cottone, P., Gaglio, S., Lo Re, G., Ortolani, M., Pergola, G. (2016). Structural Knowledge Extraction from Mobility Data. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds) AI*IA 2016 Advances in Artificial Intelligence. AI*IA 2016. Lecture Notes in Computer Science(), vol 10037. Springer, Cham. https://doi.org/10.1007/978-3-319-49130-1_22
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