Semantic Representation of Natural Language with Extended Conceptual Graph

We intend to create an intelligent agent that is able to detect the objects and relations in its environment, and based on the received information is able to build up an internal knowledge base on the basis of which linguistic expressions can be formulated. The present investigations focus on modeling the agent’s ability to assign semantic representations to its observations. For this purpose we have developed a graphical conceptual modeling language. In this article we examine the model’s expressive power, that is to what extent it is able to model natural language semantics. The analysis is performed in comparison with predicate logic. In our view, first-order logic does not provide sufficient means for the translation of natural language expressions and we will argue that also higher-order logic needs some extensions.