Knowledge Management 3.0

Organization-wide knowledge structures - plain, usable, sustainable

Knowledge management 3.0 uses Semantic Web standards for the design and implementation of innovative solutions. Data formats and vocabularies, such as RDF and, and the analysis language SPARQL is used to structure and align organizational knowledge.

Business Requirements

Process, use & enhance

Business requirements derive from the activity patterns in business tasks and processes. Depending on the level of automation it comes to providing a structured, context-relevant data for preconfigured processes or task-related information, presented in a user-friendly manner.
The prototypes of semantic applications developed by students in the course of master classes initially address the support of business users and end users. These developments were triggered by real problems in the university environment.
For elicitation of business requirements CMMI specific practices were used, particularly from process area Requirements Development.

Knowledge Graphs & Linked Data

Merge, structure & link

Semantic Web technologies allow the representation of business knowledge in organization-wide or even globally standardized structures and patterns. Therefore, organizations can first of all apply the multiple W3C annotation and query standards, such as RDF, RDFS, OWL and SPARQL.
In the recent years a dynamically-growing community is concerned with the development of industry and domain vocabularies provided to the public based on open licenses. Some very prominent of them are Dublin Core, FOAF or
Another significant direction of development ensures that more and more structured data is openly accessible and linked to each other whenever possible. Important initiatives and platforms in this area are DBpedia, WikiData and Linked Open Data.
A fourth relevant pillar for knowledge engineering are tools and frameworks that allow data modeling and federating across multiple systems providing interfaces to them. The following free resources are used among others in the student development projects: Protégé, rdfEditor, D2RQ, Fuseki, Jena, RDF Translator.

Business Knowledge

Discover & collect

Business knowledge in companies and organizations become more and more an important resource. To provide the access to this resource in the required quantity and quality is a big challenge. According to Davenport knowledge is a fluid mix of professional experience, values, contextual information and individual expertise, which build a framework for the assessment and integration of new experiences and information.
The sources and the degree of structuredness of business knowledge are extremely diverse. This includes the knowledge of experienced employees, which can be characterized as unstructured – at least from a technical point of view. Further, there is a wealth of weakly structured, text- or image-based sources. Important sources of highly structured business knowledge are all kinds of databases. Unfortunately, the underlying data are mostly encapsulated, like in silos. Last but not least business knowledge can be found in organizational routines, processes, practices and standards. In addition, organizations have more and more possibilities to access external, open sources of knowledge.
The methods and tools for discovering and collecting business knowledge differ with respect to the kind of source and the level of structuredness of that knowledge. In the student development projects were applied the following: interviews with business experts, analysis and consolidation of textual documents, semantic mapping of relational databases, and systematic extraction of structured, external data.


Semantic IT Service Catalog Consolidated Platform to all IT services of an organization


Graph for Study Programs Decentralized information system for degree programs


Semantic University Search Faceted search over relational databases of existing CMS

Get in contact

Vera G. Meister
Professor of Business Information Systems

Wenxin Hu
Student Assistant