Name
Title | ||
|---|---|---|
Mining Linked Open Data through Semi-supervised Learning Methods Based on Self-Training |
Abstract | ||
|---|---|---|
The paper tackles the problem of mining linked open data. The inherent lack of knowledge caused by the open-world assumption made on the semantic of the data model determines an abundance of data of uncertain classification. We present a semi-supervised machine learning approach. Specifically a self-training strategy is adopted which iteratively uses labeled instances to predict a label also for unlabeled instances. The approach is empirically evaluated with an extensive experimentation involving several different algorithms demonstrating the added value yielded by a semi-supervised approach over standard supervised methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICSC.2012.54 | Semantic Computing |
Keywords | Field | DocType |
mining linked open data,different algorithm,inherent lack,semi-supervised learning,open data,added value,self-training strategy,semi-supervised machine,data model,semi-supervised approach,extensive experimentation,open-world assumption,data mining,data models,open systems,predictive models,semantic web,knowledge based systems,prediction algorithms,learning artificial intelligence | Data mining,Data modeling,Data stream mining,Semi-supervised learning,Computer science,Knowledge-based systems,Linked data,Unsupervised learning,Artificial intelligence,Data model,Machine learning,Semantic computing | Conference |
ISBN | Citations | PageRank |
978-1-4673-4433-3 | 2 | 0.39 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Fanizzi | 1 | 1124 | 90.54 |
| Claudia D'amato | 2 | 79 | 8.93 |
| Floriana Esposito | 3 | 2434 | 277.96 |