KNOI~LEDGE ORGANIZATION AND APPLICATION: BRIEF COMIIENTS ON PAPERS IN THE SESSION 
Aravind K. Joshi 
Department of Computer and Information Science 
The Moore School 
University of Pennsylvania, Philadelphia, PA 191O4 
Comments: 
My brief comments on the papers in this session are based 
on the abstracts available to me and not on the complete 
papers. Hence, it is quite possible that some of the 
comments may turn out to be inappropriate or else they 
have already been taken care of in the full texts. In a 
couple of cases~ I had the benefit of reading some 
earlier longer related reports, which were very helpful. 
All the papers (except by Sangster) deal with either 
knowledge representation, particular types of knowledge 
to be represented, or how certain types of knowledge are 
to be used. 
Brackman describes a lattice-like structured inheritance 
network (KLONE) as a language for explicit representation 
of natural language conceptual information. Multiple 
descriptions can be represented. How does the facility 
differ from a similar one in KRL? Belief representations 
appear to be only implicit. Quantification is handled 
through a set of "structural descriptions." It is not 
clear how negation is handled. The main application is 
for the command and control of advanced graphics 
manioulators through natural language. Is there an 
implicit claim here that the KLONE representations are 
suitable for both natural language concepts as well as 
for those in the visual domain? 
Sowa also presents a network like representation (con- 
ceptual graphs). It is a representation that is 
apparently based on some ideas of Hintikka on incomplete 
but extensible models called surface models. Sowa also 
uses some ideas of graph grammars. It is not clear how 
multiple descriptions and beliefs can be represented in 
this framework. Perhaps the detailed paper will clarify 
some of these issues. This paper does not describe any 
application. 
Sangster's paper is not concerned, directly with knowledge 
representation. It is concerned with complete and 
partial matching procedures, especially for determining 
whether a particular instance satisfies the criteria for 
membership in a particular class. Matching procedures, 
especially partial matching procedures, are highly rele- 
vant to the use of any knowledge representation. Partial 
matching procedures have received considerable attention 
in the rule-based systems. This does not appear to be 
the case for other representations. 
Moore and Mann do not deal with knowledge representation 
per se, but rather with the generation of natural lang- 
uage texts from a given knowledge representation. They 
are more concerned with the problem of generating a text 
(which includes questions of ordering among sentences, 
their scopes, etc.) which satisfies a goal held by the 
system, describing a (cognitive) state of the reader. 
The need for resorting to multi-sentence structures 
arises from the fact that for achieving a desired state 
of the reader, a single sentence may not be adequate. 
~cDonald's work on generation appears to be relevant, but 
it is not mentioned by the authors. 
Burnstein is primarily concerned with knowledge about 
(physical) objects and its role in the comprehension 
process. The interest here is the need for a particular 
type of knowledge rather than the representation scheme 
itself, which he takes to be that of Schank. Knowledge 
about objects, their normal uses, and the kinds of 
actions they are normally involved in is necessary for 
interjretation of sentences dealing with objects. In 
sentence (1) John opened the bottle and poured the wine, 
Burnstein's analysis indicates that the inference is dri- 
ven largely by our knowledge about open bottles. In this 
instance, this need not be the case. We have the same 
situation in John took the bottle out of the refrioerator 
and poured the--w-Tne. The inference here is dependent on 
knowing something about wine bottles and their normal 
uses; knowledge of the fact that the bottle was open is 
not necessary. 
Given the normal reading of (1), (l') John opened the 
bottle and ~ured the wine out of it will be judged as 
re~u'n-~an--t~-, be-Te't'~o'n'~f--redundant material in (l') gives 
(1). Deletion of redundant and recoverable material is a 
device that language exploits. The recoverability here, 
however, is dependent on the knowledge about the objects 
and their normal uses.lf a non-normal reading of (1) is 
intended (e.g., the wine bein 0 poured into the bottle) 
then (l") John opened the bottle and poured the wine into 
it is not felt redundant. This suggests that a prediction 
that a normal reading is intended can be made (not, of 
course, with complete certainty) by recognizing that we 
are dealing with reduced forms. (Of course, context can 
always override such a prediction.) 
Some further questions are: Knowledge about objects is 
essential for comprehension. The paper does not discuss, 
however, how this knowledge and its particular represen- 
tation helps in controlling the inferences in a uniform 
manner. Is there any relationship of this work to the 
common sense algorithms of Rieger? 
Lebowitz is also concerned with a particular type of 
knowledge rather than a representation scheme. Knowledge 
about the reader's purpose is essential for comprehension. 
The role played by the "interest" of the reader is also 
explored. The application is for the comprehension of 
newspaper stories. There is considerable work beyond the 
indicated references in the analysis of goal-directed 
discoursep but this has not been mentioned~ 
Finally, there are other issues which are important for 
knowledge representation but which have been either left 
out or only peripherally mentioned by some of the authors. 
Some of these are as follows. 
(i) A representation has to be adequate to support the 
desired inference. But this is not enough. It is also 
important to know how inferences are made (e.g., with 
what ease or difficulty). The interaction of the nature 
of a representation and the structure of the sentence or 
discourse will make certain inferences go through more 
easily than others. 
(ii) Knowledge has to be updated. Again the nature of 
the representation would make certain kinds of updates or 
modifications easy and others difficult. 
(iii) The previous issue also has a bearing on the 
relationship between knowledge representation and know- 
ledge acquisition. At some level, these two aspects 
have to be viewed together. 
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