Gaining insight by structural knowledge extraction

Cottone, Pietro; Gaglio, Salvatore; Lo Re, Giuseppe; Ortolani, Marco

Abstract

The availability of increasingly larger and more complex datasets has boosted the demand for systems able to analyze them automatically. The design and implementation of effective systems requires coding knowledge about the application domain inside the system itself; however, the designer is expected to intuitively grasp the most relevant features of the raw data as a preliminary step.

In this paper we propose a framework to get useful insight about a set of complex data, and we claim that a shift in perspective may be of help to tackle with the unaddressed goal of representing knowledge by means of the structure inferred from the collected samples. We will present a formulation of knowledge extraction in terms of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples, and a proof-of-concept application in a scenario of mobility data.

Citation

Cottone, P., Gaglio, S., Lo Re, G., & Ortolani, M. (2016). Gaining insight by structural knowledge extraction.

• Conference Name: ECAI'16: Proceedings of the Twenty-second European Conference on Artificial Intelligence
• Start Date: Aug 29, 2016
• End Date: Sep 2, 2016
• Online Publication Date: Aug 29, 2016
• Publication Date: Aug 29, 2016
• Deposit Date: Dec 14, 2023
• Publisher: Association for Computing Machinery (ACM)
• ISBN: 978-1-61499-671-2
• Publisher URL: https://dl.acm.org/doi/abs/10.3233/978-1-61499-672-9-999