Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health
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RESEARCH
Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health Muhammad Amith1, Kayo Fujimoto2, Rebecca Mauldin3 and Cui Tao1*
Abstract Background: Dyadic-based social networks analyses have been effective in a variety of behavioral- and healthrelated research areas. We introduce an ontology-driven approach towards social network analysis through encoding social data and inferring new information from the data. Methods: The Friend of a Friend (FOAF) ontology is a lightweight social network ontology. We enriched FOAF by deriving social interaction data and relationships from social data to extend its domain scope. Results: Our effort produced Friend of a Friend with Benefits (FOAF+) ontology that aims to support the spectrum of human interaction. A preliminary semiotic evaluation revealed a semantically rich and comprehensive knowledge base to represent complex social network relationships. With Semantic Web Rules Language, we demonstrated FOAF+ potential to infer social network ties between individual data. Conclusion: Using logical rules, we defined interpersonal dyadic social connections, which can create inferred linked dyadic social representations of individuals, represent complex behavioral information, help machines interpret some of the concepts and relationships involving human interaction, query network data, and contribute methods for analytical and disease surveillance. Keywords: Ontology, Public health, Disease surveillance, Social network analysis, HIV Background Social network analysis is defined as a “broad strategy for investigating social structures” [1] and a “set of techniques used to understand these relationships and how they affect behaviors” [2]. Qualitative analyses and measures can help us interpret these network structures and understand the underlying behaviors and influences among individuals [3]. Furthermore, other methods like actor-oriented network dynamic modeling methodology can further analyze social network structures [4]. Social network analysis has long had an impact on such public *Correspondence: [email protected] 1 School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX 77030, USA Full list of author information is available at the end of the article
health research areas as social support, HIV/STIs, family planning and reproductive health, community health, and inter-organizational relations [2]. Ontologies and social networks share some features. Both express information in graph-like representations, yet each has its own way to elicit information. Ontologies are representational artifacts of domain knowledge in an electronic format. It abstract complex information pertaining to concepts, data, entities, properties, etc., and it use logical links between them that can imbue meaning to data and knowledge. In addition, because ontologies provide formal, normalized and semantic definitions, data can be standardized allowing for sh
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