8 Linked Data Utilization along the Content Value Chain – Observations and Implications Georg Neubauer University of Applied Sciences St. Poelten Matthias Corvinus Str. 15, 3100 St. Poelten, Austria dm131520@fhstp.ac.at Tassilo Pellegrini University of Applied Sciences St. Poelten Matthias Corvinus Str. 15, 3100 St. Poelten, Austria tassilo.pellegrini@fhstp.ac.at ABSTRACT The authors present the results of a longitudinal investigation in the utilization of Linked Data technologies along the content value chain. The authors analyzed 71 papers in the period from 2006 to 2014 that used Linked data technologies in editorial workflows. By coding the primary and secondary research topics addressed in the paper the authors draw a conclusion of the maturity of Linked Data technologies as support systems along the content value chain. The survey indicates that Linked Data technologies are constantly maturing as a support infrastructure for editorial processes. The validity of the survey results for application domains not related to editorial tasks is open to discussion. Categories and Subject Descriptors E.0 [General]; K.4.3 [Organizational Impacts] General Terms Management, Economics, Human Factors, Standardization Keywords Linked Data, Content Value Chain, Semantic Metadata, Semantic Web, Data Journalism, News Production, Editorial Workflows, Media Economics, IPR, Data Licensing 1. INTRODUCTION The growing recognition of Linked Data among the research community as “Semantic Web done right” [14] motivates to take a closer look if and how Linked Data research has evolved over the recent years. Such an investigation allows to gain insights into research trends and interdependencies thereof, and it allows to draw conclusions whether the research field has reached a significant degree of maturity in terms of technology diffusion and application areas. As illustrated in Figure 1 a survey about the occurrence of the phrase “Linked Data” in research publications of the ACM digital library from the period 2006 to 2014 reveals the growing popularity of this technological concept in the computer sciences till 2013 with a decline in 2014. Linked Data as a generic technology for data management is being applied across various application areas and industries, making it very hard to come to a general statement concerning its level of maturity and industry adoption. So is this distribution from figure 1 an indicator for the growing maturity of a research field? And if yes, how can this maturity be operationalized empirically? Figure 1. ACM Publications containing the term “Linked Data” from 2006 – 2014 (N = 1921) To tackle these questions the authors chose to analyze a subset of research papers from the ACM database that address the application of Linked Data within editorial workflows. This subset allowed us to apply a unified classification scheme – known as the content value chain [1] – to the various application areas of Linked Data. The content value chain can be described as a process model that is comprised of several sequential steps contributing to the content production process. By looking at the application area of Linked Data in editorial workflows it was possible to identify primary and secondary areas of utilization, thus allowing us to draw conclusions towards the diffusion and appropriability of Linked Data for the production of media content. 2. CLASSIFICATION SCHEME & RELATED WORK The original concept of the value chain as developed by Michael Porter in 1979 is used as an analytical framework for the analysis of value creation processes at the firm level or the industry level [15]. Over recent years the concept of the value chain has also gained popularity in the context of open data in general [4; 6; 16] and Linked Data in special [3; 5]. Especially research that investigated the organizational and economic impact of Linked Data refers to the concept of the value chain [13]. In this paper we refer to a generic abstraction of the content value chain consisting of five steps: 1) content acquisition, 2) content editing, 3) content bundling, 4) content distribution and 5) content consumption. As illustrated by [1] Linked Data can contribute to each step by supporting its associated intrinsic production function. These are in detail: Content acquisition is mainly concerned with the collection, storage and integration of relevant information necessary to 33 47 47 88 208 317 368 447 366 0 100 200 300 400 500 2006 2007 2008 2009 2010 2011 2012 2013 2014