In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

The Open Data movement has become a driver for publicly available data on the Web. More and more data—from governments and public institutions but also from the private sector—are made available online and are mainly published in so-called Open Data portals. However, with the increasing number of published resources, there is 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 order to get a more complete picture of the severity of these issues, the present work aims at developing a generic metadata quality assessment framework for various Open Data portals: We treat data portals independently from the portal software frameworks by mapping the specific metadata of three widely used portal software frameworks (CKAN, Socrata, OpenDataSoft) to the standardized Data Catalog Vocabulary metadata schema. We subsequently define several quality metrics, which can be evaluated automatically and in an efficient manner. Finally, we report findings based on monitoring a set of over 260 Open Data portals with 1.1M datasets. This includes the discussion of general quality issues, for example, the retrievability of data, and the analysis of our specific quality metrics.

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The quality of metadata in open data portals plays a crucial role for the success of open data. E-government, for example, have to manage accurate and complete metadata information to guarantee the reliability and foster the reputation of e-government to the public. Measuring and comparing the quality of open data is not a straightforward process because it implies to take into consideration multiple quality dimensions whose quality may vary from one another, as well as various open data stakeholders whodepending on their role/needs-may have different preferences regarding the dimensions' importance. To address this Multi-Criteria Decision Making (MCDM) problem, and since data quality is hardly considered in existing e-government models, this paper develops an Open Data Portal Quality (ODPQ) framework that enables end-users to easily and in real-time assess/rank open data portals. From a theoretical standpoint, the Analytic Hierarchy Process (AHP) is used to integrate various data quality dimensions and end-user preferences. From a practical standpoint, the proposed framework is used to compare over 250 open data portals, powered by organizations across 43 different countries. The findings of our study reveals that today's organizations do not pay sufficient heed to the management of datasets, resources and associated metadata that they are currently publishing on their portal.

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