Commonsense is of primary interest to AI research since the inception of the field. Traditionally, commonsense knowledge is gathered by using humans to create and insert it in knowledge bases. Automating the collection of commonsense from text that is freely available can reduce the cost and effort of creating large knowledge bases and can enable systems that dynamically adapt to current releva...

Constraints are used to manage the integrity of knowledge. Knowledge-based systems applications are frequently constructed as a knowledge base, or rule base, component coupled with a relational database component. The most expensive of these two components o build and maintain is typically the knowledge base. Constraints, which play a key role in database management, are seldom mentioned in con...

This paper addresses verification issues in combining knowledge bases or theories from different sources. Tile knowledge may be acquired using direct knowledge acquisition or inductive learning methods. Keeping the consistency of knowledge becomes all important issue when integrating two or more knowledge bases. Several independently consistent knowledge bases may give an inconsistent system wh...

In this paper a low cost knowledge base system (KBS) framework is proposed for design of deep drawing die and procedure for developing system modules. The task of building the system is structured into different modules for major activities of design of deep drawing die. A manufacturability assessment module of the proposed framework is developed to check the manufacturability of deep drawn par...

There is no set of general guidelines to evaluate Knowledge Sharing Technology, specific ideas to evaluate user-independent ontologies in their own whole life cycle, whether their definitions are reused by KBS or they are shared among software agents. Instead of starting from the beginning, this paper discusses similarities and differences between knowledge bases and ontologies. The idea is to ...

Integration of machine learning methods into knowledge-based systems requires greater control over the application of the learning methods. Recent research in machine learning has shown that isolated and unconstrained application of learning methods can eventually degrade performance. This paper presents an approach called performance-driven knowledge transformation for controlling the applicat...

Adequate maintenance and debugging support is a key pre-requisite for successful industrial application of AI technology. Consistency-based diagnosis techniques have shown to be a promising approach for detecting faults in declarative knowledge bases in the domain of product configuration. We show how the efficiency of the basic diagnostic reasoning approach that uses (partial) configurations a...

When knowledge about a configurable system grows and user requirements change over time, the corresponding knowledge base of the configurator software has to change as well. That will also affect existing configurations based upon that knowledge base. The knowledge engineer must find the appropriate balance between organizing the different versions of constraints in the knowledge base and upgra...

The formalising of relations of the theory of relational databases encompasses in the main conventional entity set models and data structures. There is an analytical vehicle and formal definition of knowledge bases. The offered tool of meta-textual relational models of knowledge opens broad capabilities of development of the theory of relational knowledge bases. The basic fields of application:...

Research currently in progress at the University of Bielefeld is concerned with conceptualizing a methodological framework which allows large-scale knowledge bases to be constructed, utilized and maintained in a modular fashion. Challenges to be met span from the modular conception of domain knowledge in a given application area on the one hand to the adaption of principles for modular system d...