Evidence map›Paper›PMID 33319708›Full record

ArticleBMC medical informatics and decision making2020

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

Muhammad Amith, Kayo Fujimoto, Rebecca Mauldin, Cui Tao

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Muhammad AmithSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX, 77030, USA.
Kayo FujimotoSchool of Public Health, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 2514, Houston, TX, 77030, USA.
Rebecca MauldinThe University of Texas at Arlington, 211 South Cooper Street, Box 19129, Arlington, TX, 76019, USA.
Cui TaoSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St Suite 600, Houston, TX, 77030, USA. cui.tao@uth.tmc.edu.ORCID 0000-0002-4267-1924

Funding

Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
YMAP: Young Men's Affiliation Project of HIV Risk and Prevention VenueR01MH100021 · NIMH · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FUJIMOTO, KAYO, SCHNEIDER, JOHN · 2013 to 2017
$3.0M
Patient Medical History Representation, Extraction, and Inference from EHR DataR01LM011829 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TAO, CUI · 2014 to 2018
$1.7M
Using big data and deep learning on predicting HIV transmission risk in MSM populationR56AI150272 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FUJIMOTO, KAYO, SCHNEIDER, JOHN · 2020 to 2020
$801k
Cancer Prevention and Research Institute of Texas (US) RP160015NIAID NIH HHS R01 AI130460NIAID NIH HHS R56 AI150272NIMH NIH HHS R01 MH100021NLM NIH HHS R01 LM011829
6 · The paper itself

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.

Indexed as

FriendsPublic HealthHumansKnowledge BasesSocial NetworkingDisease surveillanceHIVOntologyPublic healthSocial network analysis

Identifiers

PMID33319708
PMCPMC7737278

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.