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Author Ceolin, Davide ♦ Groth, Paul ♦ Maccatrozzo, Valentina ♦ Fokkink, Wan ♦ Hage, Willem Robert Van ♦ Nottamkandath, Archana
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Trust ♦ Machine learning ♦ Provenance ♦ Subjective logic ♦ Tags ♦ Uncertainty reasoning
Abstract Trust is a broad concept that in many systems is often reduced to user reputation alone. However, user reputation is just one way to determine trust. The estimation of trust can be tackled from other perspectives as well, including by looking at provenance. Here, we present a complete pipeline for estimating the trustworthiness of artifacts given their provenance and a set of sample evaluations. The pipeline is composed of a series of algorithms for (1) extracting relevant provenance features, (2) generating stereotypes of user behavior from provenance features, (3) estimating the reputation of both stereotypes and users, (4) using a combination of user and stereotype reputations to estimate the trustworthiness of artifacts, and (5) selecting sets of artifacts to trust. These algorithms rely on the W3C PROV recommendations for provenance and on evidential reasoning by means of subjective logic. We evaluate the pipeline over two tagging datasets: tags and evaluations from the Netherlands Institute for Sound and Vision’s $\textit{Waisda?}$ video tagging platform, as well as crowdsourced annotations from the $\textit{Steve.Museum}$ project. The approach achieves up to 85% precision when predicting tag trustworthiness. Perhaps more importantly, the pipeline provides satisfactory results using relatively little evidence through the use of provenance.
ISSN 19361955
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-01-01
Publisher Place New York
e-ISSN 19361963
Journal Journal of Data and Information Quality (JDIQ)
Volume Number 7
Issue Number 1-2
Page Count 28
Starting Page 1
Ending Page 28


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Source: ACM Digital Library