1. Using activation spreading
for ontology merging
Miłosław L. Frey
FGAN – FKIE
Neuenahrer Str. 20
53343 Wachtberg, Germany
RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

2. Overview Ontology The network Taxonomy merging Summary Definition
From ontology to network
The network
General idea
Learning
Creating hierarchy
Taxonomy merging
General
Method
Summary
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3. Ontology
Ontology (def.): In computational sciences an ontology is an explicit representation of knowledge in a given thematic domain.
Ontology as a network
From: Brachman, Schmolze, An Overview of the KL-ONE Knowledge Representation System, 1985
Ontology from Philosophy: knowledge about Being as such.
Not plausible for application within computer systems  needs to be re-defined  this leads to the given definition of ontology in computational sciences.
Knowledge narrowed to the specific thematic domains in order to keep it maintainable.
Ontology has usually a taxonomy as a backbone (image  thick arrows depict subsumption relation).
This taxonomy can be interpreted as a network of interconnected nodes. If a mechanism to spread the information is added, a taxonomy becomes an “alive” connectionist network.
Network is a localist connectionist network in the tradition of McClelland & Rumelhart and Gary Dell. But there is a big difference
RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

4. Ontology
Ontology (def.): In computational sciences an ontology is an explicit representation of knowledge in a given thematic domain.
Ontology as a network
From: Brachman, Schmolze, An Overview of the KL-ONE Knowledge Representation System, 1985
Ontology from Philosophy: knowledge about Being as such.
Not plausible for application within computer systems  needs to be re-defined  this leads to the given definition of ontology in computational sciences.
Knowledge narrowed to the specific thematic domains in order to keep it maintainable.
Ontology has usually a taxonomy as a backbone (image  thick arrows depict subsumption relation).
This taxonomy can be interpreted as a network of interconnected nodes. If a mechanism to spread the information is added, a taxonomy becomes an “alive” connectionist network.
Network is a localist connectionist network in the tradition of McClelland & Rumelhart and Gary Dell. But there is a big difference
RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

5. The network : general idea
Internal structure of a node
Spreading activation
The node’s state depend on the other nodes’ activations, connections weights and time: each node defines a point in the multidimensional space described by features
Learning (Sowa, 2002)
Rote learning
Connection weights change
Restructuring
Generalization
Improves the taxonomy
Allows for classification of unknown objects
Unlike most connectionist systems, the network has a sophisticated internal structure of a node. It bases on the findings, that a real neuron behaves like a traditional artificial neural network and can perform virtually any operation.
Thus a node in my network can does more than just summing up or integrating incoming signals.
As a mean to transport data through a network a spreading activation mechanism is applied. It transfers a signal (so called activation value) from one node to the other, thus node’s state depends on other nodes’ activations, connection strengths and time.
Similarly like in self organizing maps, each node defies a point in a multidimensional space which is described by a set of features.
The network, unlike most localist networks, is capable not only to perform, but also implements learning. All 3 basic types of learning defined by Sowa are used: list.
The network, can also generalize, what is again unusual in localist systems. The generalization can lead to the improvement of taxonomical structure as well as allows for building unknown objects into a taxonomy.
RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

6. The network: creating hierarchy by example (1)
Input data
One-dimensional example: 7 ellipses differentiated in the ratio of axes.
Object
Ratio
ellipse_0
2.0
ellipse_1
1.95
ellipse_2
1.15
ellipse_3
0.5
ellipse_4
0.55
ellipse_5
1.0
ellipse_6
0.95
How the network is created from the input, symbolic data?
A very simple example, namely classification of 7 ellipses, defined by axis-ratios.
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7. The network: creating hierarchy by example (2)
Just input data
Final network:
“discovery” and pruning
Here are consecutive steps, which lead from symbolic input data to the network representing full taxonomy.
At first:
The input data is read in. On the top figure the resulting structure is shown. Because the input data shows directly no hierarchy, also the network is not structured.
Secondly:
The network, thanks to its generalization ability, discovers some regularities in the input data. In this case there are three types of ellipsis: vertical ones, horizontal ones and ones almost like a circle. During the “discovery” phase, there are superfluous connections introduced, that do not represent direct class-superclass relation.
Finally:
The introspective process of network pruning is performed. On the basis of already acquired data the relation among nodes are compared and superfluous connections are removed. This process results with the final network, that reflects the taxonomical structure.
RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

8. Taxonomy merging: general
Taxonomy merging (by analogy to ontology merging) is a procedure of blending two or more taxonomies into a single one.
Two methods:
union (used in the method presented),
intersection.
For simplicity:
merging is regarded as completion: one taxonomy complements the other one.
There are two main methods of taxonomy merging :
Union, which is also used in the presented method, which means that the resulting taxonomy is a union of objects in the source taxonomies, or
intersection, where only the intersection of sets of objects in each taxonomy is taken into account.
The merging definition is here simplified and merging is reduced to complementing one taxonomy with the knowledge from another one.
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9. Taxonomy merging: method (1)
(1) Starting taxonomies
(2) Joining by features sharing
On the left-hand side, two source taxonomies are presented.
On the right-hand side, the first actual step of joining networks is shown: including the new nodes into the first taxonomy. The inclusion is done by analysis of features connected to the nodes in each taxonomy. This analysis gave the following results:
the node presented as a ellipse in the first network is identical with node C in the second network and was substituted
nodes F and G have some features common with other nodes in the first ontology, but also some additional features
The next step in taxonomy merging is to blend the “new” nodes into the existing structure. This is a process similar to the creation of a new network.
RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

10. Taxonomy merging: method (2)
(3) Restructuring
(4) Pruning
Left-hand side picture shows the network after the process of restructuring. This process means simply the already mentioned blending of new nodes into the main taxonomy. Generalization property allows that a new level of hierarchy is discovered, which was not present in either of the source taxonomy. This level is represented in the picture by a grayed node.
Finally the last step is refining the created network. It contains again many superfluous connections, which are subsequently removed by an introspective process. The final form of the network corresponds to the structure of a taxonomy, which is a union of two starting taxonomies enhanced by generalization property with some additional data.
RESARCH INSTITUTE FOR COMMUNICATION, INFORMATION PROCESSING AND ERGONOMICS

11. Summary Shown Further work: Preliminary ideas
Connectionist method to join two taxonomies
Illustration by an artificial example
Further work:
Apply to real-world data
Extend to other than is-a relations
Make the method symmetrical (no need to identify the main root node)
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12. Thank you for your Attention Questions and Comments
are appreciated
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