Knowledge Engineering Bottleneck Research Articles

Generating Chunks for Cognitive Architectures

Knowledge engineering is an important task for creating and maintaining a knowledge base for cognitive models. It involves acquiring, representing, and organizing knowledge in a form that computers can use to make decisions and solve problems. However, this process can be a bottleneck for designing and using cognitive models. Knowledge engineering is a time-consuming and resource-intensive task that requires subject matter experts to provide information about a domain. In addition, models can acquire knowledge but require significant mechanisms to structure that information in a structured format appropriate for general use. Given the knowledge engineering bottleneck, we propose a solution that relies on natural language processing to extract key entities, relationships, and attributes to automatically generate chunks encoded as triples or chunks from unstructured text. Once generated, the knowledge can be used to create or add to a knowledge base within cognitive architectures to reduce knowledge engineering and task-specific models.

Function approximation with decomposed fuzzy systems

If the number of input signals increases, a fuzzy system gets increasingly intractable for two reasons. On the one hand the knowledge acquisition suffers increasingly from the knowledge engineering bottleneck. On the other hand, computational and memory demands of fuzzy systems increase strongly, thus suffering from the curse of dimensionality. The first problem is classically addressed by learning techniques, the second by decomposing the fuzzy system. The NetFAN-approach (Network of Fuzzy Adaptive Nodes) combines decomposition with learning in order to apply fuzzy techniques to more complex applications. Different ways to decompose a fuzzy system are discussed in this paper. For the favorite decomposition principle, it is shown to be an universal function approximator, despite decomposition. The real-world example of controlling a pneumatic positioning system finally demonstrates the applicability and benefits of this type of hierarchical decomposition, namely a significantly reduced number of rules.

Inductive text classification for medical applications

Abstract Text classification poses a significant challenge for knowledge-based technologies because it touches on all the familiar demons of artificial intelligence: the knowledge engineering bottleneck, problems of scale, easy portability across multiple applications, and cost-effective system construction. Information retrieval (IR) technologies traditionally avoid all of these issues by defining a document in terms of a statistical profile of its lexical items. The IR community is willing to exploit a superficial type of knowledge found in dictionaries and thesaurae, but anything that requires customization, application-specific engineering, or any amount of manual tinkering is thought to be incompatible with practical cost-effective system designs. In this paper those assumptions are challenged and it is shown how machine learning techniques can operate as an effective method for automated knowledge acquisition when it is applied to a representative training corpus, and leveraged against a few hours of routine work by a domain expert. A fully implemented text classification system operating on a medical testbed is described and experimental results based on that testbed are reported.

Capturing expertise by rule induction

AbstractThis paper examines the claim that machine induction can alleviate the current knowledge engineering bottleneck in expert system construction. It presents a case study of the rule induction software tool known as Expert-Ease and proposes a set of criteria which might guide the selection of appropriate domains.

Capturing Expertise by Rule Induction

AbstractThis paper examines the claim that machine induction can alleviate the current knowledge engineering bottleneck in expert system construction. It presents a case study of the rule induction software tool known as Expert-Ease and proposes a set of criteria which might guide the selection of appropriate domains.