The accelerating pace of change in the economic, legal and social environment combined with tendencies towards increased decentralization of organizational structures have had a profound impact on the way we organize and utilize and organize knowledge. The internet as we know it today and especially the World Wide Web as the multimodal interface for the presentation and consumption of multimedia information are the most prominent examples of these developments. To illustrate the impact of new communication technologies on information practices Saumure & Shiri (2008) conducted a survey on Continue reading
Traditional KOSs include a broad range of system types from term lists to classification systems and thesauri. These organization systems vary in functional purpose and semantic expressivity. Most of these traditional KOSs were developed in a print and library environment. They have been used to control the vocabulary used when indexing and searching a specific product, such as a bibliographic database, or when organizing a physical collection such as a library (Hodge et al. 2000). Continue reading
Enabling and managing interoperability at the data and the service level is one of the strategic key issues in networked knowledge organization systems (KOSs) and a growing issue in effective data management. But why do we need “semantic” interoperability and how can we achieve it?
Interoperability vs. Integration
The concept of (data) interoperability can best be understood in contrast to (data) integration. While integration refers to a process, where formerly distinct data sources and Continue reading
With the increasing availability of semantic data on the World Wide Web and its reutilization for commercial purposes, questions arise about the economic value of interlinked data and business models that can be built on top of it. The Linked Data Business Cube provides a systematic approach to conceptualize business models for Linked Data assets. Similar to an OLAP Cube, the Linked Data Business Cube provides an integrated view on stakeholders (x-axis), revenue models (y-axis) and Linked Data assets (z-axis), thus allowing to systematically investigate the specificities of various Linked Data business models.
Mapping Revenue Models to Linked Data Assets
By mapping revenue models to Linked Data assets we can modify the Linked Data Business Cube as illustrated in the figure below.
The figure indicates that with increasing business value of a resource the opportunities to derive direct revenues rise. Assets that are easily substitutable generate little incentives for direct revenues but can be used to trigger indirect revenues. This basically applies to instance data and metadata. On the other side, assets that are unique and difficult to imitate and substitute, i.e. in terms of competence and investments necessary to provide the service, carry the highest potential for direct revenues. This applies to assets like content, service and technology. Generally speaking, the higher the value proposition of an asset – in terms of added value – the higher the willingness to pay.
Ontologies seem to function as a “mediating layer” between “low-incentive assets” and “high-incentive assets”. This means that ontologies as a precondition for the provision and utilization of Linked Data can be capitalized in a variety of ways, depending on the business strategy of the Linked Data provider.
It is important to note that each revenue model has specific merits and flaws and requires certain preconditions to work properly. Additionally they often occur in combination as they are functionally complementary.
Mapping Revenue Models to Stakeholders
A Linked Data ecosystem is usually comprised of several stakeholders that engage in the value creation process. The cube can help us to elaborate the most reasonable business model for each stakeholder.
Summing up, Linked Data generates new business opportunities, but the commercialization of Linked Data is very context specific. Revenue models change in accordance to the various assets involved and the stakeholders who take use of them. Knowing these circumstances is crucial in establishing successful business models, but to do so it requires a holistic and interconnected understanding of the value creation process and the specific benefits and limitations Linked Data generates at each step of the value chain.