Tassilo Pellegrini

Thoughts on KOS (Part 2): Classifying Knowledge Organisation Systems

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

Tassilo Pellegrini

Thoughts on KOS (Part1): Getting to grips with “semantic” interoperability

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

Thomas Thurner

Data to Value & Semantic Web Company agree partnership to bring cutting edge Semantic Management to Financial Services clients

The partnership aims to change the way organisations, particularly within Financial Services, manage the semantics embedded in their data landscapes. This will offer several core benefits to existing and prospective clients including locating, contextualising and understanding the meaning and content of Information faster and at a considerably lower cost. The partnership will achieve this through combining the latest Information Management and Semantic techniques including:

  • Text Mining, Tagging, Entity Definition & Extraction.
  • Business Glossary, Data Dictionary & Data Governance techniques.
  • Taxonomy, Data Model and Ontology development.
  • Linked Data & Semantic Web analyses.
  • Data Profiling, Mining & Discovery.

This includes improving regulatory compliance in areas such as BCBS, enabling new investment research and client reporting techniques as well as general efficiency drivers such as faster integration of mergers and acquisitions. As part of the partnership, Data to Value Ltd. will offer solution services and training in PoolParty product offerings, including ontology development and data modeling services.

Nigel Higgs, Managing Director of Data to Value notes; “this is an exciting collaboration between two firms which are pushing the boundaries in the way Data, Information and Semantics are managed by business stakeholders. We spend a great deal of time helping organisations at a grass roots level pragmatically adopt the latest Information Management techniques. We see this partnership as an excellent way for us to help organisations take realistic steps to adopting the latest semantic techniques.”

Andreas Blumauer, CEO of Semantic Web Company adds, “The consortium of our two companies offers a unique bundle, which consists of a world-class semantic platform and a team of experts who know exactly how Semantics can help to increase the efficiency and reliability of knowledge intensive business processes in the financial industry.”

Tassilo Pellegrini

Introducing the Linked Data Business Cube

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.

Linked Data Business Cube_Full

 

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.

Linked Data Business Cube_Revenue-Type

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.

Linked Data Business Cube_Stakeholders

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.

Read more: Asset Creation and Commercialization of Interlinked Data