  Data heterogeneity hampers the effort to integrate and infer knowledge from
vast heterogeneous data sources. An application case study is described, in
which the objective was to semantically represent and integrate structured data
from sensor devices with unstructured data in the form of local indigenous
knowledge. However, the semantic representation of these heterogeneous data
sources for environmental monitoring systems is not well supported yet. To
combat the incompatibility issues, a dedicated semantic middleware solution is
required. In this paper, we describe and evaluate a cross-domain middleware
architecture that semantically integrates and generate inference from
heterogeneous data sources. These use of semantic technology for predicting and
forecasting complex environmental phenomenon will increase the degree of
accuracy of environmental monitoring systems.
