From NeOn Wiki
|Developed by||Johanna Völker|
|Current Version||[[current version:= <ask format="template" template="CurrentVersion" limit="1" searchlabel="" sort="version number" order="descending" default="no version available"> 1.x/LeDA *</ask>]]|
|Homepage||[http://ontoware.org/projects/leda/ 1.x/LeDA Website]|
LeDA is a tool for the automatic generation of disjointness axioms based on machine learning classification. The classifier, that determines disjointness for any given pair of classes, is trained based on a gold standard of manually created disjointness axioms. Each axiom of the gold standard is represented by a pair of classes associated with a label - disjoint or not disjoint - and a vector of feature values. As in our earlier experiments, we used a variety of lexical and logical features, which we believe to provide a solid basis for learning disjointness. These features are used to build an overall classification model on whose basis the classifier can predict disjointness for previously unseen pairs of classes.
For performance and useability reasons the LeDA plugin works with the following reduced set of features. The full feature set as described in D3.8.1 is so far only available in the original version of LeDA (http://ontoware.org/projects/leda/).
The installation of this plugin does not require specific configuration steps, that might demand for in depth explanations. It can be performed by using the standard plugin update mechanisms of the NeOn Toolkit.
More information with regards to this plugin can be found in NeOn D3.8.1 and on the LeDA homepage (http://ontoware.org/projects/leda/).
A training (Training) or classification (Learn Disjointness) process can be triggered by means of a context menu, that is accessible by clicking on an OWL ontology in the navigator view on the left.
This screenshots shows a list of automatically generated disjointness axioms as displayed by the LeDA view.
The preference page is accessible from the main menu of Eclipse ("Window" -> "Preferences..." -> "LeDA Preferences"). It allows for setting a variety of parameters for both training and classification:
- General settings
- classifier: The Weka (http://www.cs.waikato.ac.nz/~ml/weka/) classifier to be used by LeDA.
- output directory: The arff files generated after training or classification will be stored in this directory.
- background ontology: A background ontology is used by some of the more advanced features (not available in this version of the plugin).
- training file (optional): This is an optional, but recommended parameter. If no training file is specified, positive and negative examples will need to be generated from the training ontology - a process, that is very time-consuming.
- training ontology: This has to be an ontology, which contains a complete set of manually created disjointness axioms.
- Learning disjointness / classification
- training arff: This ARFF file has been generated automatically at the end of the training phase. It contains all the necessary information for creating a classification model.