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

Dyadic-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. 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. 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. 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.

[1]  Ronald Rousseau,et al.  Social network analysis: a powerful strategy, also for the information sciences , 2002, J. Inf. Sci..

[2]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

[3]  Mark A. Musen,et al.  The protégé project: a look back and a look forward , 2015, SIGAI.

[4]  Mark Huisman,et al.  Treatment of non-response in longitudinal network studies , 2008, Soc. Networks.

[5]  Khalid Mahmood,et al.  A hybrid statistical and semantic model for identification of mental health and behavioral disorders using social network analysis , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[6]  Cui Tao,et al.  Architecture and usability of OntoKeeper, an ontology evaluation tool , 2019, BMC Medical Informatics and Decision Making.

[7]  Amanda Hicks,et al.  The ontology of medically related social entities: recent developments , 2016, Journal of Biomedical Semantics.

[8]  Dan Brickley,et al.  FOAF Vocabulary Specification , 2004 .

[9]  Ming Cao,et al.  Statistical adjustment of network degree in respondent-driven sampling estimators: Venue attendance as a proxy for network size among young MSM , 2018, Soc. Networks.

[10]  Mário J. Silva,et al.  The epidemiology ontology: an ontology for the semantic annotation of epidemiological resources , 2014, Journal of Biomedical Semantics.

[11]  N. F. Noy,et al.  Ontology Development 101: A Guide to Creating Your First Ontology , 2001 .

[12]  D. Ubelaker,et al.  Change in Hair Pigmentation in Children from Birth to 5 Years in a Central European Population (Longitudinal Study) , 2000 .

[13]  Vijayan Sugumaran,et al.  A semiotic metrics suite for assessing the quality of ontologies , 2005, Data Knowl. Eng..

[14]  C. Dudley Girard,et al.  Predicting Patterns of Exchange in Economic Exchange Networks , 2009, Journal of Social Structure.

[15]  Martin J. O'Connor,et al.  SQWRL: A Query Language for OWL , 2009, OWLED.

[16]  M. Jeanmougin SOLEIL ET PEAU , 1992 .

[17]  Charlene A Flash,et al.  Network Modeling of PrEP Uptake on Referral Networks and Health Venue Utilization Among Young Men Who Have Sex with Men , 2018, AIDS and Behavior.

[18]  Mark Huisman,et al.  Imputation of missing network data: Some simple procedures , 2009, J. Soc. Struct..

[19]  Marta Iglesias-Sucasas,et al.  The FAO Geopolitical Ontology: A Reference for Country-Based Information , 2013 .

[20]  Cui Tao,et al.  Modulated evaluation metrics for drug-based ontologies , 2017, J. Biomed. Semant..

[21]  Csongor Nyulas,et al.  The SWRLAPI: A Development Environment for Working with SWRL Rules , 2008, OWLED.

[22]  Steven Simoens,et al.  Opportunities and challenges for the inclusion of patient preferences in the medical product life cycle: a systematic review , 2019, BMC Medical Informatics and Decision Making.

[23]  D. McAdams Personality, Modernity, and the Storied Self: A Contemporary Framework for Studying Persons , 1996 .

[24]  Jon Corson-Rikert,et al.  The VIVO Ontology: Enabling Networking of Scientists , 2011 .

[25]  Liyang Yu FOAF: Friend of a Friend , 2011 .

[26]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[27]  Cui Tao,et al.  A Web Application Towards Semiotic-based Evaluation of Biomedical Ontologies , 2015, BDM2I@ISWC.

[28]  Raffaele Vacca,et al.  Designing a CTSA‐Based Social Network Intervention to Foster Cross‐Disciplinary Team Science , 2015, Clinical and translational science.

[29]  Christian Steglich,et al.  Beyond dyadic interdependence: Actor-oriented models for co-evolving social networks and individual behaviors , 2007 .

[30]  Subu Surendran,et al.  RDF approach on social network analysis , 2016, 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS).

[31]  Thomas W. Valente,et al.  Social Networks and Health , 2010 .

[32]  Stanley W. Borg Social Networks and Health: Models, Methods, and Applications , 2012 .

[33]  Syed Sibte Raza Abidi,et al.  Discovering Central Practitioners in a Medical Discussion Forum Using Semantic Web Analytics. , 2017, Studies in health technology and informatics.