Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3233/978-1-61499-672-9-999 | Frontiers in Artificial Intelligence and Applications |
Field | DocType | Volume |
Data science,Computer science,Knowledge extraction,Artificial intelligence,Machine learning | Conference | 285 |
ISSN | Citations | PageRank |
0922-6389 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pietro Cottone | 1 | 21 | 2.87 |
| Salvatore Gaglio | 2 | 660 | 88.41 |
| Giuseppe Lo Re | 3 | 338 | 41.26 |
| Marco Ortolani | 4 | 209 | 21.31 |