Semantic knowledge network inference across a range of stakeholders and communities of practice

This paper provides empirical and experimental assessments of thematic knowledge discourses based on two case studies in the US Virgin Islands and Florida. We utilize a latent semantic indexing analysis over natural language corpus to classify and categorize knowledge categories. We computed TF*IDF scores and associated co-occurrence Jaccard similarity scores to construct semantic knowledge networks. Using network analysis, we computed structural metrics over four composite groups: neighbor-based, centrality, equivalence and position. The analysis show that structural network characteristics of environmental knowledge can exponentially predict associations between knowledge categories. We show that connectivity play a critical role on acquisition, representation, and diffusion patterns of knowledge within local communities. We provide evidence of a global prevalence of a shared knowledge core. We show that core social-ecological attributes of knowledge follow scale-free, power law distributions and stable, equilibrium network structures. We identify two distinct models of bidirectional translation: a bottom-up and a top-down.