Automatic generation of domain ontologies - Revision history

AndreaBonarini at 15:33, 6 April 2010

2010-04-06T15:33:30Z

DavideEynard at 14:51, 9 October 2009

2009-10-09T14:51:33Z

DavideEynard at 14:50, 9 October 2009

2009-10-09T14:50:58Z

DavideEynard at 08:43, 30 July 2009

2009-07-30T08:43:37Z

DavideEynard: New page: {{ProjectProposal |title=Automatic generation of domain ontologies |image= |description=This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developin...

2009-07-30T08:43:02Z

New page: {{ProjectProposal |title=Automatic generation of domain ontologies |image= |description=This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developin...

New page

{{ProjectProposal
|title=Automatic generation of domain ontologies
|image=
|description=This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.
|tutor=MatteoMatteucci;AndreaBonarini;DavideEynard
|cfumin=20
|cfumax=20
|studmin=1
|studmax=2
|resarea=Machine Learning
|restopic=
|level=Ms
|type=Thesis
|status=Proposal
}}

@@ Line 12: / Line 12: @@
 |level=Ms
 |type=Thesis
-|status=Active
+|status=Closed
 }}

@@ Line 12: / Line 12: @@
 |level=Ms
 |type=Thesis
-|status=active
+|status=Active
 }}

@@ Line 2: / Line 2: @@
 |title=Automatic generation of domain ontologies
 |image=
-|description=This thesis to be developed together with [http://www.noustat.it/ Noustat S.r.l.], who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.
+|description=This thesis to be developed together with Noustat S.r.l. (see http://www.noustat.it), who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.
 |tutor=MatteoMatteucci;AndreaBonarini;DavideEynard
 |cfumin=20