Knowledge Base Creation, Enrichment and Repair

Abstract

Abstract. This chapter focuses on data transformation to RDF and Linked Data and furthermore on the improvement of existing or extracted data especially with respect to schema enrichment and ontology repair. Tasks concerning the triplification of data are mainly grounded on existing and well-proven techniques and were refined during the lifetime of the LOD2 project and integrated into the LOD2 Stack. Triplification of legacy data, i.e. data not yet in RDF, represents the entry point for legacy systems to participate in the LOD cloud. While existing systems are often very useful and successful, there are notable differences between the ways knowledge bases and Wikis or databases are created and used. One of the key differences in content is in the importance and use of schematic information in knowledge bases. This information is usually absent in the source system and therefore also in many LOD knowledge bases. However, schema information is needed for consistency checking and finding modelling problems. We will present a combination of enrichment and repair steps to tackle this problem based on previous research in machine learning and knowledge representation. Overall, the Chapter describes how to enable tool-supported creation and publishing of RDF as Linked Data (Sect. 1) and how to increase the quality and value of such large knowledge bases when published on the Web (Sect. 2).

Change-based causes in counterexample explanation for model checking

“…Other examples of fixing knowledge base structure modeling errors are [56,131,125,40,54,76,16]. Work [125] is dedicated to fixing TBox and unsatisfiable concepts, while work [40] focuses on integrity constraints.…”

“…Work [125] is dedicated to fixing TBox and unsatisfiable concepts, while work [40] focuses on integrity constraints. In [56], several tools are introduced for ontology repair and enrichment; one of them is [76] which helps in understanding undesired entailments (for instance, unsatisfiable classes or inconsistencies), and deals with the problem of class learning. Another tool presented is RDFUnit [70,71,72] which proposes test-driven Linked Data quality assessment.…”

The Seventies Sociality: Activist Publishers and the Digital Commonplacing of New Knowledge

“…RDF, the XML-based linked data format was an added output that would be nice to have, but that was not critical to ultimate success of the projects. The recent catalyzation of interest in linked open data in the context of TEI (including the genesis of projects like the Canadian Research Writing Collaboratory and the recent revitalization of digital humanities linked data special interest groups) is, however, a promising sign of our field's engagement with linked data, and our readiness to join international efforts to produce and publish linked data based on the resources we already publish and the skills we already have (Ciotti and Tomasi 2016;Huber et al 2014;Pattuelli et al 2013;Lehmann et al 2012;Shadbolt et al 2012;Hellmann et al 2014).…”