scholarly journals Quality Awareness over Graph Pattern Queries

Author(s):  
Philippe Rigaux ◽  
Virginie Thion
2019 ◽  
Vol 75 (5) ◽  
pp. 1082-1099 ◽  
Author(s):  
Rita Marcella ◽  
Graeme Baxter ◽  
Agnieszka Walicka

Purpose The purpose of this paper is to present the results of a study that explored human behaviour in response to political “facts” presented online by political parties in Scotland. Design/methodology/approach The study consisted of interactive online interviews with 23 citizens in North-East Scotland, in the run-up to the 2017 UK General Election. Findings Participants demonstrated cognitive and critical responses to facts but little affective reaction. They judged facts swiftly and largely intuitively, providing evidence that facts are frequently consumed, accepted or rejected without further verification processes. Users demonstrated varying levels of engagement with the information they consume, and subject knowledge may influence the extent to which respondents trust facts, in previously unanticipated ways. Users tended to notice facts with which they disagreed and, in terms of prominence, particularly noted and responded to facts which painted extremely negative or positive pictures. Most acknowledged limitations in capacity to interrogate facts, but some were delusionally confident. Originality/value Relatively little empirical research has been conducted exploring the perceived credibility of political or government information online. It is believed that this and a companion study are the first to have specifically investigated the Scottish political arena. This paper presents a new, exploratory fact interrogation model, alongside an expanded information quality awareness model.


2019 ◽  
Vol 30 (4) ◽  
pp. 24-40
Author(s):  
Lei Li ◽  
Fang Zhang ◽  
Guanfeng Liu

Big graph data is different from traditional data and they usually contain complex relationships and multiple attributes. With the help of graph pattern matching, a pattern graph can be designed, satisfying special personal requirements and locate the subgraphs which match the required pattern. Then, how to locate a graph pattern with better attribute values in the big graph effectively and efficiently becomes a key problem to analyze and deal with big graph data, especially for a specific domain. This article introduces fuzziness into graph pattern matching. Then, a genetic algorithm, specifically an NSGA-II algorithm, and a particle swarm optimization algorithm are adopted for multi-fuzzy-objective optimization. Experimental results show that the proposed approaches outperform the existing approaches effectively.


2021 ◽  
Author(s):  
Daniel Mawhirter ◽  
Samuel Reinehr ◽  
Wei Han ◽  
Noah Fields ◽  
Miles Claver ◽  
...  

2018 ◽  
Vol E101.D (3) ◽  
pp. 582-592 ◽  
Author(s):  
Takayoshi SHOUDAI ◽  
Yuta YOSHIMURA ◽  
Yusuke SUZUKI ◽  
Tomoyuki UCHIDA ◽  
Tetsuhiro MIYAHARA

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