Project description

Knowledge-based, intelligent systems have made their way into practice: Model-based systems are generating diagnostics for automobiles, configurator applications help us assembling complex products from simpler components, recommender systems generate product proposals that match our needs and requirements, just to name a few. Being the cornerstone for success for many of these applications, the knowledge acquisition problem has been addressed in the past in different dimensions, the main focus lying on knowledge representation and conceptualization issues as well as on process models for capturing and formalizing a domain expert's knowledge.

Historically, one main assumption of these approaches was that there shall exist one single point of knowledge formalization and in consequence one (user-oriented) conceptualization and a central knowledge acquisition tool. In most cases in real world, however, the domain knowledge is in the heads of different stakeholders, typical examples being cross-department or cross-organization business rules or new types of applications, in which large user communities are sharing knowledge in an open-innovation, web-based environment.

Only recently, with the emergence and spread of Web 2.0 and Semantic Web technologies, the opportunities and also the problems of collaborative knowledge acquisition have again become a topic of interest. With regard to the types of knowledge to be acquired, the main focus of these recent developments, however, is on acquiring 'structural' knowledge, i.e., on terms, concepts, and relationships among them.

The proposed V-KNOW project shall build upon these new developments, but it however aims at going a step further and target at the collaborative acquisition and refinement of domain-constraints and business rules as they represent the most crucial, frequently updated, and thus costly part in many knowledge-based applications. The main questions answered in the project among others comprise the following: How can we automatically detect and resolve conflicts if knowledge acquisition is distributed between different knowledge contributors? How can we assist the knowledge contributors to acquire knowledge by asking them the "right" questions, i.e. minimizing the interaction needed? How can we generate "good" proposals for changing the knowledge base from different, possibly only partially-defined knowledge chunks, i.e. find plausible (in the eyes of the contributors) changes of the knowledge base?

The results of the proposed research project are methods and algorithms for answering these questions and an evaluation by implementing and testing a prototypical implementation.