Grass-roots Class Alignment

Current ontology alignment practices adopt a centralized approach: the alignment task is carried out by a domain expert, possibly with the help of some ontology alignment tools. In a Peer-to-Peer environment, each end-user (peer) may explicitly or implicitly generate ontology alignments for their own purposes during its own use of the semantic data. This kind of end-user-generated ontology alignments, which we call grass-roots ontology alignments, could be mined and generalized to help other users with their alignment tasks, thus enabling a p2p-style ontology alignment. Grass-roots ontology alignment, often generated as a side effect of other data manipulations, could be peer-specific, task-specific, approximate, or even contradictory. Its semantics is not necessarily the equivalency between classes or properties. This paper reports our work on reusing grass-roots class alignment for aligning class hierarchies. A grass-roots class alignment, though approximate, still reveals some facts about relationships between different classes. We formalize facts about class relationships that can be inferred from an alignment under different cases. We then apply forward-chaining inference to the facts knowledge base to infer more facts. The facts KB is then leveraged for ontology alignment purposes. To deal with uncertainty and inconsistency, each fact is associated with an evidence that tells how the fact is obtained. The evidences are used to select better-supported facts in case of inconsistency.