  The demand for a huge amount of data for machine learning (ML) applications
is currently a bottleneck in an empirically dominated field. We propose a
method to combine prior knowledge with data-driven methods to significantly
reduce their data dependency. In this study, component-based machine learning
(CBML) as the knowledge-encoded data-driven method is examined in the context
of energy-efficient building engineering. It encodes the abstraction of
building structural knowledge as semantic information in the model
organization. We design a case experiment to understand the efficacy of
knowledge-encoded ML in sparse data input (1% - 0.0125% sampling rate). The
result reveals its three advanced features compared with pure ML methods: 1.
Significant improvement in the robustness of ML to extremely small-size and
inconsistent datasets; 2. Efficient data utilization from different entities'
record collections; 3. Characteristics of accepting incomplete data with high
interpretability and reduced training time. All these features provide a
promising path to alleviating the deployment bottleneck of data-intensive
methods and contribute to efficient real-world data usage. Moreover, four
necessary prerequisites are summarized in this study that ensures the target
scenario benefits by combining prior knowledge and ML generalization.
