  The Winograd Schema (WS) challenge, proposed as an al-ternative to the Turing
Test, has become the new standard for evaluating progress in natural language
understanding (NLU). In this paper we will not however be concerned with how
this challenge might be addressed. Instead, our aim here is threefold: (i) we
will first formally 'situate' the WS challenge in the
data-information-knowledge continuum, suggesting where in that continuum a good
WS resides; (ii) we will show that a WS is just special case of a more general
phenomenon in language understanding, namely the missing text phenomenon
(henceforth, MTP) - in particular, we will argue that what we usually call
thinking in the process of language understanding involves discovering a
significant amount of 'missing text' - text that is not explicitly stated, but
is often implicitly assumed as shared background knowledge; and (iii) we
conclude by a brief discussion on why MTP is inconsistent with the data-driven
and machine learning approach to language understanding.
