Name
Abstract | ||
|---|---|---|
The paper presents the ultimate version of a concept learning system which can support typical ontology construction/evolution tasks through the induction of class expressions from groups of individual resources labeled by a domain expert. Stating the target task as a search problem, a Foil-like algorithm was devised based on the employment of refinement operators to traverse the version-space of candidate definitions for the target class. The algorithm has been further enhanced including a more general definition for the scoring function and better refinement operators. An experimental evaluation of the resulting new release of DL-Foil, which implements these improvements was carried out to assess its performance also in comparison with other concept learning systems. |
Year | Venue | Field |
|---|---|---|
2018 | EKAW | Data mining,Expression (mathematics),Subject-matter expert,Computer science,Concept learning,Operator (computer programming),Ontology construction,Artificial intelligence,Search problem,Traverse |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Fanizzi | 1 | 1124 | 90.54 |
| Giuseppe Rizzo | 2 | 4 | 1.77 |
| Claudia D'Amato | 3 | 733 | 57.03 |
| Floriana Esposito | 4 | 2434 | 277.96 |