Connecting KOSs and the LOD Cloud

This paper describes a specific project, the current situation leading to it, its project design and first results. In particular, we will examine the terminology employed in the Linked Open Data cloud and compare this to the terminology employed in both the Universal Decimal Classification and the Basic Concepts Classification. We will explore whether these classifications can encourage greater consistency in LOD terminology. We thus hope to link the largely distinct scholarly literatures that address LOD and KOSs. 1.0 Introduction and Motivation Our research1 involves comparing the terminology employed within the Linked Open Data (LOD) Cloud with terminology employed within two KOSs: The Universal Decimal Classification (UDC) and the Basic Concepts Classification (BCC). In doing so we will connect two quite distinct literatures and communities of practice: the Semantic Web (SW) community, which has tended to be centered in computer science, and the knowledge organization (KO) community. In the SW community there have been increasing efforts to curate and preserve the machine-readable knowledge items as published on the Web using linked data formats (Beek, Rietveld at al. 2014; Beek at al. 2014). Controlled vocabularies play a prominent role in these efforts. They provide a way to index the knowledge graph, and they represent a semantically enriched layer in this graph. In knowledge organization (KO), systematic studies of KOSs have been proposed already (Tennis 2012), and such studies have also been executed for a number of small samples. The promise of the web-based LOD Cloud is to free up data, metadata and information to a large extent from what often is called “data silos”—isolated information systems, which come with their own domain-specific knowledge organization systems, and are often barely interoperable. The LOD Cloud promises to deliver machinereadable KOSs and their implementation in a way that enables easy cross-linking. For example, the platform GeoNames (http://www.geonames.org) publishes about eleven billion place names in machine readable form, and has been used by many other services to relate a term like “New York” to a specific geographic reference, which in turn enables other services to link other names to this location, e.g., “City of New York,” “New York City,” or the historic term “Nieuw Amsterdam.” To be able to compare the different terminologies expressed in vocabularies, one first has to have an overview of them. Hence, our research involves the initial step of surveying the terminologies that are currently employed in linked open data. This will result in an atlas of vocabularies. 1 Digging Into the Knowledge Graph, 2016 Digging Into Data Challenge https://diggingintodata.org/awards/2016/project/digging-knowledge-graph