AbstractWe present a GDPR-compliant data privacy and access use case of a distributed data wallet and we explore its modeling using two options, AspectOWL and Prova. This use case requires a representation capable of expressing the dynamicity and interaction between parties. While both approaches provide the expressiveness of non-monotonic states and fluent state transitions, their scope and semantics are vastly different. AspectOWL is a monotonic contextualized ontology language, able to represent dynamic state transitions and knowledge retention by wrapping parts of the ontology in isolated contexts, called aspects, while Prova can handle state transitions at runtime using non-monotonic state transition semantics. We present the two implementations and we discuss the similarities, advantages, and differences of the two approaches.
The ROC ontology for country responses to COVID-19 provides a model for collecting, linking and sharing data on the COVID-19 pandemic. It follows semantic standardization (W3C standards RDF, OWL, SPARQL) for the representation of concepts and creation of vocabularies. ROC focuses on country measures and enables the integration of data from heterogeneous data sources. The proposed ontology is intended to facilitate statistical analysis to study and evaluate the effectiveness and side effects of government responses to COVID-19 indifferent countries. The ontology contains data collected by OxCGRT from publicly available information. This data has been compiled from information provided by ECDC for most countries, as well as from various repositories used to collect data on COVID-19.
PurposeThe purpose of this paper is to explore to which extent the quality of social media short text without extensions can be investigated and what are the predictors, if any, of such short text that lead to trust its content.Design/methodology/approachThe paper applies a trust model to classify data collections based on metadata into four classes: Very Trusted, Trusted, Untrusted and Very Untrusted. These data are collected from the online communities, Genius and Stack Overflow. In order to evaluate short texts in terms of its trust levels, the authors have conducted two investigations: (1) A natural language processing (NLP) approach to extract relevant features (i.e. Part-of-Speech and various readability indexes). The authors report relatively good performance of the NLP study. (2) A machine learning technique in more precise, a random forest (RF) classifierusing bag-of-words model (BoW).FindingsThe investigation of the RF classifier using BoW shows promising intermediate results (on average 62% accuracy of both online communities) in short-text quality identification that leads to trust.Practical implicationsAs social media becomes an increasingly new and attractive source of information, which is mostly provided in the form of short texts, businesses (e.g. in search engines for smart data) can filter content without having to apply complex approaches and continue to deal with information that is considered more trustworthy.Originality/valueShort-text classifications with regard to a criterion (e.g. quality, readability) are usually extended by an external source or its metadata. This enhancement either changes the original text if it is an additional text from an external source, or it requires text metadata that is not always available. To this end, the originality of this study faces the challenge of investigating the quality of short text (i.e. social media text) without having to extend or modify it using external sources. This modification alters the text and distorts the results of the investigation.
