Name
Abstract | ||
|---|---|---|
In the last decade, new approaches focused on modeling uncertainty over complex relational data have been developed. In this paper, one of the most promising of such approaches, known as probabilistic relational model (PRM), has been investigated and extended in order to measure and include semantic relationships for addressing web page classification problems. Experimental results show the potential of the proposed method of capturing the "strength" of existing relationships (links) and the capacity of including this information into the probability model. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1142/S0218001413500134 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Web document classification, probabilistic relational models | Probability model,Web page,Information retrieval,Relational database,Computer science,Statistical relational learning,Web modeling,Artificial intelligence,Probabilistic relational model,Probabilistic logic,Machine learning | Journal |
Volume | Issue | ISSN |
27 | 4 | 0218-0014 |
Citations | PageRank | References |
2 | 0.39 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elisabetta Fersini | 1 | 140 | 20.70 |
| Enza Messina | 2 | 214 | 23.18 |