Conceptual Structures: Leveraging Semantic Technologies

During the past half century, the field of artificial intelligence has developed a large number of theories, paradigms, technologies, and tools. Many AI systems are based on one dominant paradigm with a few subsidiary modules for handling exceptions or special cases. Some systems are built from components that perform different tasks, but each component is based on a single paradigm. Since people freely switch from one method of thinking or reasoning to another, some cognitive scientists believe that the ability to integrate multiple methods of reasoning is key to human-like flexibility. In his book The Society of Mind, Minsky (1986) presented an architecture for intelligence based on a society of heterogeneous agents that use different reasoning methods to solve different problems or different aspects of the same problem. That idea is intriguing, but it raises many serious issues:  how to coordinate multiple agents, distribute tasks among them, evaluate their results, encourage agents that consistently produce good results, inhibit agents that produce misleading, irrelevant, or unfruitful results, and integrate all the results into a coherent response. The most difficult problem is to enable multiple heterogeneous agents, acting independently, to produce the effect of a single mind with a unified personality that can pursue and accomplish coherent goals. This article discusses ways of organizing a society of heterogeneous agents as an integrated system with flexible methods of reasoning, learning, and language processing.

Formal concept analysis is recognized as a good paradigm for browsing data sets. Besides browsing, update and complex data are other important aspects of information systems. To have an efficient implementation of concept-based information systems is difficult because of the diversity of complex data and the computation of conceptual structures, but essential for the scalability to real-world applications. We propose to decompose contexts into simpler and specialized components: logical context functors. We demonstrate this allows for scalable implementations, updatable ontologies, and richer navigation structures, while retaining genericity.

In this paper two developments in Conceptual Knowledge Processing are combined, namely Contextual Logic introduced by Rudolf Wille and Temporal Concept Analysis introduced by the author. The basic structures connecting both theories are Relational Semantic Systems (RSS), each consisting of conceptual scales and a Relational Data Systems (RDS) for the representation of relational knowledge. We introduce the notion of a concept graph of a RSS. As opposed to the definition of a concept graph of a power context family the concepts used in the concept graph of a RSS are taken from the conceptual scales and not from the concept lattices of the contexts of k-ary relations. For the graphical representation of relational knowledge in information maps we modify tools from Temporal Concept Analysis and develop relational trace diagrams. Its usefulness is shown in a small example of a Relational Semantic System.