  Most existing ontology matching methods utilize the literal information to
discover alignments. However, some literal information in ontologies may be
opaque and some ontologies may not have sufficient literal information. In this
paper, these ontologies are named as weak informative ontologies (WIOs) and it
is challenging for existing methods to matching WIOs. On one hand, string-based
and linguistic-based matching methods cannot work well for WIOs. On the other
hand, some matching methods use external resources to improve their
performance, but collecting and processing external resources is still
time-consuming. To address this issue, this paper proposes a practical method
for matching WIOs by employing the ontology structure information to discover
alignments. First, the semantic subgraphs are extracted from the ontology graph
to capture the precise meanings of ontology elements. Then, a new similarity
propagation model is designed for matching WIOs. Meanwhile, in order to avoid
meaningless propagation, the similarity propagation is constrained by semantic
subgraphs and other conditions. Consequently, the similarity propagation model
ensures a balance between efficiency and quality during matching. Finally, the
similarity propagation model uses a few credible alignments as seeds to find
more alignments, and some useful strategies are adopted to improve the
performance. This matching method for WIOs has been implemented in the ontology
matching system Lily. Experimental results on public OAEI benchmark datasets
demonstrate that Lily significantly outperforms most of the state-of-the-art
works in both WIO matching tasks and general ontology matching tasks. In
particular, Lily increases the recall by a large margin, while it still obtains
high precision of matching results.
