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Linked Data Quality Assessment: Methodologies, Dimensions, Metrics and Tools

Dr. Amrapali Zaveri

The development and standardization of semantic web technologies has resulted in an unprecedented volume of data being published on the Web as Linked Data (LD). However, we observe widely varying data quality ranging from extensively curated datasets to crowdsourced and extracted data of relatively low quality. In this talk, I will present the results of a survey conducted for gathering all the approaches for assessing the quality of LD. The survey unified and formalized commonly used terminologies across 30 core approaches related to data quality and provide a comprehensive list of 18 quality dimensions and 69 metrics. Additionally, a set of 12 tools were qualitatively analyzed using a set of attributes. The aim of this talk is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused towards data quality, specifically for LD.

06.04.2017 - 10:15
B 017