Changes between Version 5 and Version 6 of generator_HotSpot

Timestamp:

09/27/06 14:53:15 (18 years ago)

Author:

endres

Comment:

--

generator_HotSpot

@@ -9 +9 @@
 This '''experimental''' AlignmentGenerator tries to tackle large scale ontology alignment, for which other approaches fail due to high memory and/or performance requirements. The general idea is a "divide and conquer" approach, continously aligning a number of parts of the ontologies and joining them it to a single, large alignment.
 
-A basic concept, that is used within this approach, is the ContextOntology. It defines a subontology by taking a single class of another ontology, called anchor, and adding all related classes within a certain distance to it. The relation currently used is the subclass relation, but any other class to class relation could be used just as well. The distance is to be called context ''depth''. The resulting set of classes is then interpreted as a new (sub)ontology. Every relation/property of the original classes could be used, but have to be truncated to the classes that are actually contained in the new ontology.
-
-In this context, a ''Hotspot'' consists of the following attributes:
- * a source ContextOntology
- * a target ContextOntology
+A basic concept, that is used within this approach, is the ContextOntology. It defines a subontology by taking a single class of another ontology along with its context. Now, a ''Hotspot'' consists of the following attributes:
+ * a source ContextOntology, a subontology of the original source ontology.
+ * a target ContextOntology, a subontology of the original target ontology.
  * an alignment between those two
 
-The general idea of the algorithm is now, to identify a Hotspot using single ''sure matches'' as anchors. Then, this Hotspot is optimised in size with respect to a hotspot quality measure. Finally, the resulting hotspot is joined with every other hotspot it overlaps (in both, source and target). 
+The general idea of the algorithm is now, to identify a Hotspot using single ''sure matches'' as anchors. Then, this Hotspot is optimised in size with respect to a hotspot quality measure. Finally, the resulting hotspot is joined with every other hotspot it overlaps (in both, source and target). These steps are repeated until a satisfactory cover rate is reached.
 
-To implement this approach, the algorithm requires two exchangeable modules, which are specified by the interfaces HotspotIdentifier and HotspotMeasure. 
+Since the motivation for this approach was the alignment of large scale ontologies, the hotspots are only allowed to grow to a certain size limit during ontimisation. Hotspots can, however, become *very* big by being joined with others. Therefore, the joining algorithm does not involve any similarity measures, but merely joins their [wiki:ContextOntology ContextOntologies] and relations sets, while adding a prior to the hotspot of higher quality.
 
-
+To implement this approach in a generic way, the algorithm requires two exchangeable modules, which are specified by the interfaces HotspotIdentifier and HotspotMeasure. They provide the method to identify new hotspots in the ontologies and to judge the quality of the hotspot at hand. See the definitions of these two modules to learn about their various implementations.
 
 == Characteristics ==
+
+Generally, the algorithm is very sensitive to the performance of the HotspotIdentifier and HotspotMeasure used. Tests with smaller ontologies revealed acceptable results, even with simple implementations of these modules. Evaluation for larger ontologies (like the [http://oaei.ontologymatching.org/2006/anatomy/ anatomy] or the [http://www.few.vu.nl/~wrvhage/oaei2006/ food] scenario) is pending due to the lack of a ground truth.
 
 == Evaluation/Performance ==