The Ontology Mapping Neural Network (OMNN) extends the ability of Identical Elements Neural Network(IENN) and its variants ' [4, 1-3] to represent and map complex relationships. The network can learn high-level features common to different tasks, and use them to infer correspondence between the tasks. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the networks. The output of one network in response to a stimulus to another network can be interpreted as an analogical mapping. In a similar fashion, the networks can be explicitly trained to map specific items in one domain to specific items in another domain. A more detailed version is published on the main conference [5].
The network architecture is shown in Figure 1. A in and B in are input subvectors for nodes from ontology A and ontology B respectively. They share one representation layer AB r . RA in represents relationships from graph A; RB in represents relationships from graph B. They share one representation layer R r .
In this network, each to-be-mapped node in graph is represented by a single active unit in input layers (A in , B in ) and output layers (A out , B out ). For relationships representation in input layer (RA in , RB in ), each relationship is represented by a single active unit. The network shown in Figure 1 has multiple sub networks shown in the following list.
has better F-measure than 9 of the 12 systems, OMNN's recall is significantly better than 10 of the systems. It should be noted that p-value< 0.05 means there is significant difference between two systems compared, then detailed data is visited to reveal which is one is better than the other.