Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health

Muhammad Amith,Rebecca Mauldin,Kayo Fujimoto,Cui Tao

doi:10.1186/s12911-020-01287-8

Abstract

BackgroundDyadic-based social networks analyses have been effective in a variety of behavioral- and health-related research areas. We introduce an ontology-driven approach towards social network analysis through encoding social data and inferring new information from the data.MethodsThe 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.ResultsOur 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.ConclusionUsing 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.

Highlights

Dyadic-based social networks analyses have been effective in a variety of behavioral- and healthrelated research areas
These attributes were extracted from the Adolescent to adult project (ADD) (Adolescent to Adult) Health project survey forms [14] and from YMAP (Young Men’s Affiliation Project of HIV Risk and Prevention Venue) [15], a project that examines the social networks of minority Young Men Who Have Sex with Men (YMSM)
To attain an initial basic evaluation of Friend of a Friend (FOAF)+, we measured FOAF+ in comparison with other similar social network ontologies like VIVO (v.1.7) [23] and the original FOAF using semiotic metrics proposed by Burton-Jones and colleagues [24]

Summary

Introduction

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. 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]. 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.

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