Massive amounts of data from different contexts and produc-ers are collected and connected relying often solely on statis-tical techniques. Problems to the acclaimed value of data lie in the precise definition of data and associated contexts as well as the problem that data are not always published in meaningful and open ways. The Linked Data paradigm offers a solution to the limitations of simple keywords by having unique, resolvable and shared identifiers instead of strings This paper reports on a three-year research project “Digging Into the Knowledge Graph,” funded as part of the 2016 Round Four Digging Into Data Challenge (https://diggingintodata.org/awards/2016/project/digging-knowledge-graph). Our project involves comparing terminol-ogy employed within the LOD cloud with terminology em-ployed within two general but different KOSs – Universal Decimal Classification and Basic Concepts Classification. We are exploring whether these classifications can encourage greater consistency in LOD terminology and linking the largely distinct scholarly literatures that address LOD and KOSs. Our project is an attempt to connect the Linked Open Data community, which has tended to be centered in comput-er science, and the KO community, with members from lin-guistics, metaphysics, library and information science. We focus on the shared challenges related to Big Data between both communitie
|Journal||BRAJIS - Brazilian Journal of Information Science: research trends|
|Publication status||Published - 2018|
- Linked Open Data; Knowledge Organisation Systems; Big Data; Knowledge Graph
Ávila, D. M., Smiraglia, R. P., Szostak, R., Scharnhorst, A. M., Beek, W., Siebes, R., Ridenour, L., & Schlais, V. (2018). Classifying the LOD Cloud: Digging into the Knowledge Graph. BRAJIS - Brazilian Journal of Information Science: research trends, 12(4), 6-10.