Assessing Trust in Uncertain Information

Fokoue, Achille; Srivatsa, Mudhakar; Young, Rob

doi:10.1007/978-3-642-17746-0_14

Cited by 5 publications

(2 citation statements)

References 13 publications

(20 reference statements)

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“…The link between provenance and trust, mentioned in the survey of Artz and Gil, has been explored by Golbeck [9] but, mainly for addressing socio-related issues, while my my focus is on the data trustworthiness estimation. Uncertainty reasoning techniques are often used to make trust assessments, like in the work of Fokoue et al [8]. It is important to investigate further the possibility to represent these data by means of multiple layers of probabilities, because of their adequateness to deal with vast amounts of heterogenous data.…”

Section: State Of the Artmentioning

confidence: 99%

Trusting Semi-structured Web Data

Ceolin¹

2013

The Semantic Web: Semantics and Big Data

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The growth of the Web brings an uncountable amount of useful information to everybody who can access it. These data are often crowdsourced or provided by heterogenous or unknown sources, therefore they might be maliciously manipulated or unreliable. Moreover, because of their amount it is often impossible to extensively check them, and this gives rise to massive and ever growing trust issues. The research presented in this paper aims at investigating the use of data sources and reasoning techniques to address trust issues about Web data. In particular, these investigations include the use of trusted Web sources, of uncertainty reasoning, of semantic similarity measures and of provenance information as possible bases for trust estimation. The intended result of this thesis is a series of analyses and tools that allow to better understand and address the problem of trusting semi-structured Web data.I investigate about different aspects inherent to this problem: data, metadata and reasoning techniques useful to make adequate trust estimates. Research Question 1. The first problem that I focus on is the usage of trusted semi-structured Web data to make trust evaluations of semi-structured data (not necessarily coming from Web sources). This gives a first insight into the possibility to use Web data for assessing the trustworthiness of data. Hence the first research question is:Can Web data help the trust evaluation of semi-structured data?

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Section: State Of the Artmentioning

confidence: 99%

Trusting Semi-structured Web Data

Ceolin¹

2013

The Semantic Web: Semantics and Big Data

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“…subsumption) has already taken place. Hence, unlike for instance Fukuoe et al [16], that apply probabilistic reasoning in parallel to OWL [32] reasoning, we propose some models to address uncertainty issues on top of that kind of reasoning layers. These models, namely the parametric Beta-Binomial and Dirichlet-Multinomial, and the non-parametric Dirichlet process, use first-and second-order probabilities and the generation of new classes of observations, to derive safe conclusions on the overall populations of our data, given that we are deriving those from possibly biased samples.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Estimation and Analysis of Categorical Web Data

Ceolin¹,

Hage²,

Fokkink³

et al. 2014

Uncertainty Reasoning for the Semantic Web III

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Abstract. Web data often manifest high levels of uncertainty. We focus on categorical Web data and we represent these uncertainty levels as first-or second-order uncertainty. By means of concrete examples, we show how to quantify and handle these uncertainties using the BetaBinomial and the Dirichlet-Multinomial models, as well as how take into account possibly unseen categories in our samples by using the Dirichlet process. We conclude by exemplifying how these higher-order models can be used as a basis for analyzing datasets, once at least part of their uncertainty has been taken into account. We demonstrate how to use the Battacharyya stastistical distance to quantify the similarity between Dirichlet distributions, and use such results to analyze a Web dataset of piracy attacks both visually and automatically.

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