Name
Abstract | ||
|---|---|---|
Abstract. A clustering method,is presented which can be applied to relational knowledge,bases (e.g. DATALOG deductive databases). It can be used to discover interesting groups of resources through their (semantic) annotations expressed in the standard logic programming,languages. The method,exploits an effective and language-independent semi-distance measure for individuals., that is based on the resource semantics w.r.t. a number,of dimensions corresponding to a com- mittee of features represented by a group of concept descriptions (discriminating features). The algorithm is a fusion of the classic B ISECTING K-MEANS with ap- proaches based on medoids that are typically applied to relational representations. We discuss its complexity and potential applications to several tasks. 1 Unsupervised Learning with Complex Data In this work, we investigate on unsupervised learning for knowledge bases (KBs) ex- |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-68123-6_15 | ISMIS |
DocType | Citations | PageRank |
Conference | 1 | 0.42 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Fanizzi | 1 | 1124 | 90.54 |
| Claudia D'amato | 2 | 79 | 8.93 |
| Floriana Esposito | 3 | 2434 | 277.96 |