A Structural Approach to Infer Recurrent Relations in Data

Cottone, Pietro; Gaglio, Salvatore; Ortolani, Marco

Abstract

Extracting knowledge from a great amount of collected data has been a key problem in Artificial Intelligence during the last decades. In this context, the word “knowledge” refers to the non trivial new relations not easily deducible from the observation of the data. Several approaches have been used to accomplish this task, ranging from statistical to structural methods, often heavily dependent on the particular problem of interest. In this work we propose a system for knowledge extraction that exploits the power of an ontology approach. Ontology is used to describe, organise and discover new knowledge. To show the effectiveness of our system in extracting and generalising the knowledge embedded in data, we have built a system able to pick up some strategies in the solution of complex puzzle game.

Citation

Cottone, P., Gaglio, S., & Ortolani, M. (2014). A Structural Approach to Infer Recurrent Relations in Data. In Advances onto the Internet of Things (105-119). Springer. https://doi.org/10.1007/978-3-319-03992-3_8

• Online Publication Date: Jan 1, 2014
• Publication Date: Jan 1, 2014
• Deposit Date: Dec 14, 2023
• Publisher: Springer
• Pages: 105-119
• Book Title: Advances onto the Internet of Things
• Chapter Number: 8
• ISBN: 9783319039916; 9783319039923
• DOI: https://doi.org/10.1007/978-3-319-03992-3_8
• Keywords: Ontology Learning; General Unary Hypotheses Automaton (GUHA); Grammar Induction; Symbolic Knowledge Representation; Grammatical Inference
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-319-03992-3_8
• Additional Information: First Online: 1 January 2014