Name
Title | ||
|---|---|---|
Learning probabilistic Description logic concepts: under different Assumptions on missing knowledge |
Abstract | ||
|---|---|---|
Knowledge available through Semantic Web standards can be missing, generally because of the adoption of the Open World Assumption. We present a Statistical Relational Learning system for learning terminological naïve Bayesian classifiers, which estimate the probability that an individual belongs to a target concept given its membership to a set of Description Logic concepts. During the learning process, we consistently handle the lack of knowledge that may be introduced by the adoption of the Open World Assumption, depending on the varying nature of the missing knowledge itself. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2245276.2245349 | SAC |
Keywords | Field | DocType |
target concept,bayesian classifier,varying nature,description logic concept,probabilistic description logic concept,missing knowledge,different assumptions,statistical relational learning system,semantic web standard,open world assumption,semantic web,statistical relational learning,description logic,publish subscribe,sparql,rdf,rete | Naive Bayes classifier,Computer science,Statistical relational learning,Semantic Web,Description logic,SPARQL,Open-world assumption,Natural language processing,Artificial intelligence,Probabilistic logic,RDF,Machine learning | Conference |
Citations | PageRank | References |
4 | 0.39 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pasquale Minervini | 1 | 119 | 16.34 |
| Claudia D'Amato | 2 | 733 | 57.03 |
| Nicola Fanizzi | 3 | 1124 | 90.54 |