Semantic knowledge network inference across a range of stakeholders and communities of practice. (November 2018)

• Record Type: Journal Article
• Authors: Alexandridis, Kostas; Takemura, Shion; Webb, Alex; Lausche, Barbara; Culter, Jim; Sato, Tetsu
• Is Part Of: Environmental modelling & software. Volume 109(2018)
• Page Start: 202
• Page End: 222
• Publication Date: 2018-11
• Subjects: Semantic networks -- Knowledge networks -- Social-ecological systems -- Integrated local ecological knowledge -- Latent semantic analysis -- Social network analysis; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.08.026
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: Analyzes the structure and characteristics of integrated local environmental knowledge in terms of semantic network analysis. Critically examines similarities and differences in knowledge self-organization across social groups and locations. Addresses semantic network neighbor, centrality, equivalence and positional characteristics in terms of semantic knowledge extraction. Provides evidence of scale-free distribution of environmental knowledge. Identifies knowledge transformations in terms of social-ecological complexity and the social understanding of reality. Provides a comprehensive structural analysis of the hierarchical structure of integrated environmental knowledge networks.