Schema Evolution publication categorizer

Ontology matching plays a key role for semantic interoperability. Many
methods have been proposed for automatically finding the alignment between
heterogeneous ontologies. However, in many real-world applications, finding the
alignment in a completely automatic way is highly infeasible. Ideally, an ontology
matching system would have an interactive interface to allow users to provide
feedbacks to guide the automatic algorithm. Fundamentally, we need answer the
following questions: How can a system perform an efficiently interactive process
with the user? How many interactions are sufficient for finding a more accurate
matching? To address these questions, we propose an active learning framework
for ontology matching, which tries to find the most informative candidate matches
to query the user. The user’s feedbacks are used to: 1) correct the mistake matching
and 2) propagate the supervise information to help the entire matching process.
Three measures are proposed to estimate the confidence of each matching
candidate. A correct propagation algorithm is further proposed to maximize the
spread of the user’s “guidance”. Experimental results on several public data sets
show that the proposed approach can significantly improve the matching accuracy
(+8.0% better than the baseline methods).