Name
Title | ||
|---|---|---|
Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-relational Association Rules in the Semantic Web. |
Abstract | ||
|---|---|---|
We carry out a comparison of popular asymmetric metrics, originally proposed for scoring association rules, as building blocks for a fitness function for evolutionary inductive programming. In particular, we use them to score candidate multi-relational association rules in an evolutionary approach to the enrichment of populated knowledge bases in the context of the Semantic Web. The evolutionary algorithm searches for hidden knowledge patterns, in the form of SWRL rules, in assertional data, while exploiting the deductive capabilities of ontologies. |
Year | Venue | Field |
|---|---|---|
2018 | EuroGP | Ontology (information science),Evolutionary algorithm,Computer science,Inductive programming,Semantic Web,Description logic,Theoretical computer science,Fitness function,Association rule learning,Artificial intelligence,Natural language processing |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tran Duc Minh | 1 | 11 | 0.87 |
| Claudia D'Amato | 2 | 733 | 57.03 |
| Binh Thanh Nguyen | 3 | 1 | 1.03 |
| Andrea Tettamanzi | 4 | 667 | 84.56 |