An expressive efficient representation: Bridging a gap between NLP and KR

Abstract

We do not know how humans reason, whether they reason using natural language (NL) or not and we are not interested in proving or disproving such a proposition. Nonetheless, it seems that a very expressive transparent medium humans communicate with, state their problems in and justify how they solve these problems is NL. Hence, we wished to use NL as a Knowledge Representation(KR) in NL knowledge-based (KB) sytems. However, NL is full of ambiguities. In addition, there are syntactic and semantic processing complexities associated with NL. Hence, we consider a quasi-NL KR with a tractable inference relation. We believe that such a representation bridges the gap between an expressive semantic representation (SR) sought by the Natural Language Processing (NLP) community and an efficient KR sought by the KR community. In addition to being a KR, we use the quasi-NL language as a SR for a subset of English that it defines. Also, it is capable of a general-purpose domain-independent inference component which is, according to semanticists, all what it takes to test a semantic theory in any NLP system. This paper gives only a flavour for this quasi-NL KR and its capabilities (for a detailed study see [14]).