  Integrating disparate and distributed vegetation data is critical for
consistent and informed national policy development and management. Australia's
National Vegetation Information System (NVIS) under the Department of Climate
Change, Energy, the Environment and Water (DCCEEW) is the only nationally
consistent vegetation database and hierarchical typology of vegetation types in
different locations. Currently, this database employs manual approaches for
integrating disparate state and territory datasets which is labour intensive
and can be prone to human errors. To cope with the ever-increasing need for up
to date vegetation data derived from heterogeneous data sources, a
Semi-Automated Hybrid Matcher (SAHM) is proposed in this paper. SAHM utilizes
both schema level and instance level matching following a two-tier matching
framework. A key novel technique in SAHM called Multivariate Statistical
Matching is proposed for automated schema scoring which takes advantage of
domain knowledge and correlations between attributes to enhance the matching.
To verify the effectiveness of the proposed framework, the performance of the
individual as well as combined components of SAHM have been evaluated. The
empirical evaluation shows the effectiveness of the proposed framework which
outperforms existing state of the art methods like Cupid, Coma, Similarity
Flooding, Jaccard Leven Matcher, Distribution Based Matcher, and EmbDI. In
particular, SAHM achieves between 88% and 100% accuracy with significantly
better F1 scores in comparison with state-of-the-art techniques. SAHM is also
shown to be several orders of magnitude more efficient than existing
techniques.
