Neumaier, S. (2019). Semantic enrichment of open data on the Web - or: how to build an open data knowledge graph [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2019.72883
In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. A huge amount of datasets became available that could potentially be integrated and linked into the Web of (Linked) Data. However, with the increasing number of published resources, there are a number of concerns with regards to the quality of the data sources and the corresponding metadata, which compromise the searchability, discoverability and usability of resources. In this work, we define quality dimension and metrics, and subsequently report findings based on a continuous monitoring and quality assessment of numerous Open Data portals. Semantic Web technologies provide enhanced search functionalities and allow to explore related content across data portals. However, as our reports show, current Open Data lacks in sufficient data quality, rich/consistent descriptions, and uniform vocabularies. Having identified and measured the existing quality issues, we outline methods to restore the quality of published resources, methods to recover the semantics of tabular Open Data, and methods to extract taxonomic, spatial and temporal information. Eventually, the aim of this work is to improve the overall quality and value of Open Data and to use the extracted semantic information to build an Open Data Knowledge Graph.