Name
Title | ||
|---|---|---|
Towards Numeric Prediction on OWL Knowledge Bases through Terminological Regression Trees |
Abstract | ||
|---|---|---|
In the context of semantic knowledge bases, among the possible problems that may be tackled by means of data-driven inductive strategies, one can consider those that require the prediction of the unknown values of existing numeric features or the definition of new features to be derived from the data model. These problems can be cast as regression problems so that suitable solutions can be devised based on those found for multi-relational databases. In this paper, a new framework for the induction of logical regression trees is presented. Differently from the classic logical regression trees and the recent fork of the terminological classification trees, the novel terminological regression trees aim at predicting continuous values, while tests at the tree nodes are expressed with Description Logic concepts. They are intended for multiple uses with knowledge bases expressed in the standard ontology languages for the Semantic Web. A top-down method for growing such trees is proposed as well as algorithms for making predictions with the trees and deriving rules. The system that implements these methods is experimentally evaluated on ontologies selected from popular repositories. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICSC.2012.35 | Int. J. Semantic Computing |
Keywords | Field | DocType |
towards numeric prediction,novel terminological regression tree,owl knowledge bases,description logic concept,terminological regression trees,classic logical regression tree,terminological classification tree,semantic knowledge base,knowledge base,new framework,logical regression tree,regression problem,new feature,description logics,relational databases,semantics,data models,knowledge based systems,ontologies,regression analysis,regression,semantic web | Ontology (information science),Data modeling,Computer science,Semantic Web,Description logic,Knowledge-based systems,Natural language processing,Artificial intelligence,Data model,Semantics,Machine learning,Ontology language | Conference |
Volume | Issue | ISBN |
6 | 4 | 978-1-4673-4433-3 |
Citations | PageRank | References |
3 | 0.37 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Fanizzi | 1 | 1124 | 90.54 |
| Claudia D'amato | 2 | 79 | 8.93 |
| Floriana Esposito | 3 | 2434 | 277.96 |
| Pasquale Minervini | 4 | 119 | 16.34 |