User Interfaces to the Web of Data based on Natural Language Generation

We explore how Virtual Research Environments based on Semantic Web technologies support research interactions with RDF data in various stages of corpus-based analysis, analyze the Web of Data in terms of human readability, derive labels from variables in SPARQL queries, apply Natural Language Generation to improve user interfaces to the Web of Data by verbalizing SPARQL queries and RDF graphs, and present a method to automatically induce RDF graph verbalization templates via distant supervision

Managing Data Quality in Observational Citizen Science

University of Minnesota Ph.D. dissertation.December 2017. Major: Computer Science. Advisor: Loren Terveen. 1 computer file (PDF); viii, 103 pages.Observational citizen science is an effective way to supplement the environmental datasets compiled by professional scientists. Involving volunteers in data collection has the added educational benefits of increased scientific awareness and local ownership of environmental concerns. This thesis provides an in-depth exploration of observational citizen science and the associated challenges and opportunities for HCI research. We focus on data quality as a key lens for understanding observational citizen science, and how it differs from the related domains of crowdsourcing, open collaboration, and volunteered geographic information. In order to understand data quality, we performed a qualitative analysis of data quality assurance practices in River Watch, a regional water quality monitoring program. We found that data quality in River Watch is primarily maintained through universal adherence to standard operating procedures, rather than through a computable notion of “accuracy”. We also found that rigorous data quality assurance practices appear to enhance rather than hinder the educational goals of the program participants. In order to measure data quality, we conducted a quantitative analysis of CoCoRaHS, a multinational citizen science project for observing precipitation. Given the importance of long-term participation to data consumers, we focused on volunteer retention as our primary metric for data quality. Through survival analysis, we found that participant age is a significant predictor of retention. Compared to all other age groups, participants aged 60-70 are much more likely to sign up for CoCoRaHS, and to remain active for several years. We propose that the nature of the task can profoundly influence the types of participants attracted to a project. In order to improve data quality, we derived a general workflow model for observational citizen science, drawing on our findings in River Watch, CoCoRaHS, and similar programs. We propose a data model for preserving provenance metadata that allows for ongoing data exchange between disparate technical systems and participant skill levels. We conclude with general principles that should be taken into consideration when designing systems and protocols for managing citizen science data

Shortipedia aggregating and curating Semantic Web data.

Shortipedia is a Web-based knowledge repository, that pulls together a growing number of sources in order to provide a comprehensive, diversified view on entities of interest. Contributors to Shortipedia can easily add claims to the knowledge base, provide sources for their claims, and find links to knowledge already available on the Semantic Web. © 2011 Elsevier B.V. All rights reserved