Knowledge engineering rediscovered: towards reasoning patterns for the semantic web

The extensive work on Knowledge Engineering in the 1990s has resulted in a systematic analysis of task-types, and the corresponding problem solving methods that can be deployed for different types of tasks. That anal- ysis was the basis for a sound and widely accepted methodology for building knowledge-based systems, and has made it is possible to build libraries of reusable models, methods and code.
In this paper, we make a first attempt at a similar analy- sis for Semantic Web applications. We will show that it is possible to identify a relatively small number of task- types, and that, somewhat surprisingly, a large set of Semantic Web applications can be described in this ty- pology. Secondly, we show that it is possible to decom- pose these task-types into a small number of primitive (“atomic”) inference steps. We give semi-formal defini- tions for both the task-types and the primitive inference steps that we identify. We substantiate our claim that our task-types are sufficient to cover the vast majority of Semantic Web applications by showing that all en- tries of the Semantic Web Challenges of the last 3 years can be classified in these task-types.