  The purpose of this paper is to describe and analyze the development of a
knowledge-based infrastructure to support MSP decision-making processes. The
paper emerged from a study to define specifications for a knowledge-based
infrastructure to provide decision support for community-level MSPs in the
Canadian province of Quebec. As part of the study, a process assessment was
conducted to understand the needs of communities as they collect, organize, and
analyze data to make decisions about their priorities. The result of this
process is a portrait, which is an epidemiological profile of health and
nutrition in their community. Portraits inform strategic planning and
development of interventions and are used to assess the impact of
interventions. Our key findings indicate ambiguities and disagreement among MSP
decision-makers regarding causal relationships between actions and outcomes,
and the relevant data needed for making decisions. MSP decision-makers
expressed a desire for easy-to-use tools that facilitate the collection,
organization, synthesis, and analysis of data, to enable decision-making in a
timely manner. Findings inform conceptual modeling and ontological analysis to
capture the domain knowledge and specify relationships between actions and
outcomes. This modeling and analysis provide the foundation for an ontology,
encoded using OWL 2 Web Ontology Language. The ontology is developed to provide
semantic support for the MSP process, defining objectives, strategies, actions,
indicators, and data sources. In the future, software interacting with the
ontology can facilitate interactive browsing by decision-makers in the MSP in
the form of concepts, instances, relationships, and axioms. Our ontology also
facilitates the integration and interpretation of community data and can help
in managing semantic interoperability between different knowledge sources.
