The development of effective techniques for knowledge representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards representational power and execution performance. Therefore, KRR research is faced with a challenge of developing knowledge representation and reasoning structures optimized for large scale reasoning.

For a KBS to be really used in practice, another essential point is that the user understands and controls the whole process whose main steps are: building of a knowledge base, running the KBS, and obtaining results .


I am interested in graph based structures for representation and reasoning due to the following points:

  •      First, graphs are simple mathematical objects (they only use elementary naive set theory notions such as elements, sets and relations) which have graphical representations -thus, they can be visualized.
  •      Secondly, there is a rich set of efficient algorithms for processing graphs - thus, they can be used as effective computing objects (they are widely used, for instance, in Operational Research).
  •      Thirdly, they can be equipped with a logical semantics and they are provided with graph-based mechanisms which are sound and complete with respect to deduction in this logic.