A STRUCTURED REPRESENTATION OF WORD-SENSES IrOR SEMANTIC ANALYSIS. 
Mafia Teresa Pazienza 
Dipartimento di Informatica c Sistcmistica, 
Universita' "La Sapienza", Roma 
Paola Velardi 
IBM Rome Scientific (\]cntcr 
ABSTRACT 
A framework for a structured representation of 
semantic knowledge (e.g. word-senses) has been defined at 
the IBM Scientific Center of Roma, as part of a project on 
Italian Text Understanding. This representation, based on 
the conceptual graphs formalism \[SOW84\], expresses deep 
knowledge (pragmatic) on word-senses. The knowledge base 
data structure is such as to provide easy access by the 
semantic verification algorithm. This paper discusses some 
important problem related to the definition of a semantic 
knowledge base, as depth versus generality, hierarchical 
ordering of concept types, etc., and describes the solutions 
adopted within the text understanding project. 
INTRODUCTION 
The main problem encountered in natural language 
(NL) understanding systems is that of the trade-off between 
depth and extension of the semantic knowledge base. 
Processing time and robustness dramatically get worse when 
the system is required to deeply understand texts in 
unrestricted domains. 
For example, the FRUMP system \[DEJ79\], based 
on scripts \[SHA77\], analyzes texts in a wide domain by 
performing a superficial analysis. The idea is to capture 
only the basic information, much in the same way of a 
hurried newspaper reader. 
A different approach was adopted in the 
RESEARCtlER system \[LEB83\], whose objective is to 
answer detailed questions concerning specific texts. The 
knowledge domain is based on the description of physical 
objects (MPs: Memory Pointers), and their mutual relations 
(RWs: Relation Words). 
A further example is provided by BORIS \[LEH83\], 
one of the most recent systems in the field of text 
understanding. BORIS was designed to understand as 
deeply as possible a limited number of stories. A first 
prototype of BORIS can successfully answer a variety of 
questions on divorce stories; an extension to different 
domains appears however extremely complex without 
structural changes. 
The current status of the art on knowledge 
representation and language processing does not offer 
readily available solutions at this regard. The system 
presented in this paper does not propose a panacea for 
semantic knowledge representation, but shows the viability 
of a deep semaatic approach even in unrestricted domains. 
The features of the Italian Text Understanding 
system are summarized as follows: 
Text analysis is performed in four steps: morphologic, 
morphosyntactic, syntactic and semantic analysis. At 
each step the results of the preceding steps are used to 
restrict Ihe current scope of analysis. Hence for 
example Ihe semantic analyzer uses the syntactic 
relations identified by the parser to produce an initial 
set of possiNe interpretations of the sentence. 
Semantic knowledge is represented in a very detailed 
form (word_sense pragmatics). Logic is used to 
implement in a uniform and simple framework the data 
structure representing semantic knowledge and the 
programs performing semantic verification. 
For a detailed .vcrview of the project and a description of 
morphological and syntactical analyses refer to \[ANT87\] In 
\[VEI,g7\] a texl generation system used for Nt. query 
answering is also described. 
The system is based on VM/PROLOG and analyzes 
press_agency releases in the economic domain. Even 
though the specific application oriented the choice of words 
to be entered in the semantic data base, no other restrictions 
where added. Press agency releases do not present any 
specific morphologic or syntactic simplification in the 
sentence structure. 
This paper deals with definition of knowledge 
structures for semantic analysis. Basically, the semantic 
processor collsi,qs of: 
1. a dictionary of word definitions. 
2. a parsing algorithm. 
We here restrict our attention to the first aspect: the 
semantic verification algorithm is extensively described in 
\[PAZ87\] 
The representation formalism adopted for word 
definitions is the conceptual graph model \[SOW84\], 
summarized in ,qectiml 2. According to this model, a piece 
of meaning (sm~teace or word definition) is represented as a 
graph of ~ r,m q, t~- a.d conceptual re\[alions 
249 
Section 3 states a correspondence between conceptual 
categories (e.g. concepts and relations) and word-senses. A 
dictionary of hierarchically structured conceptual relations is 
derived from an analysis of grammar cases. 
Section 4 deals with concept definitions and type 
hierarchies. Finally, Section 5 gives some implementation 
detail. 
The present extention of the knowledge base (about 
850 word-sense definitions) is only intended to be an 
test-bed to demonstrate the validity of the knowledge 
representation scheme and the semantic analyzer. The 
contribution of this paper is hence in the field of computer 
science and his objective is to provide a tool for linguistic 
experts. 
TIlE CONCEPTUAL GRAPH MODEL 
The conceptual graph formalism unifies in a 
powerful and versatile model many of the ideas that have 
been around in the last few years on natural language 
processing. Conceptual graphs add new features to to the 
well known semantic nets formalism, and make it a viable 
model to express the richness and complexity of natural 
language. 
The meaning of a sentence or word is represented 
by a directed graph of concepts and conceptual relations. In 
a graph, concepts are enclosed in boxes, and conceptual 
relations in circles; in the linear form, adopted in this paper, 
boxes and circles are replaced by brackets and parenthesis. 
Arrows indicate the direction of the relations among 
concepts. 
Concepts are the generalization of physical 
perceptions (MAN, CAT, NOISE) or abstract categories 
(FREEDOM, LOVE). A concept has the general form: 
\[NAME: referent\] 
The r~ferent indicates a specific occurrence of the concept 
NAME ~t'or example \[DOG: Fido\]). 
Conceptual relations express the semantic links 
between concepts. For example, the phrase "John eats ~ is 
:'cpresented as follows: 
\[PERSON: John\] < --(AGNT) < --\[EAT\] 
where (AGNT) is a diadic relation used to explicit the active 
role of the entity John with respect to the action of eating. 
In order to describe word meanings, in \[SOWg4\] 
several types of conceptual graphs are introduced: 
1. Type definitions. 
The type of a concept is the name of the class to 
which the concept belongs. Type labels are structured 
in a hierarchy: the expression C>C' means that the 
type C is more general than C' (for example, 
ANIMAl. - MAN); C is called the supertype of C'. 
A type C is defined in terms of species, that is the 
more general class to which it belongs, and differentia, 
that is what distinguishes C from the other types of the 
same species. The type definition for MAN is : 
\[ANIMAl ,\] .... (CHRC)-- > \[RATIONAL\] 
where (ClIP.C.) is the characteristic relation. 
2. Canonical graphs. 
Canonical graphs express the semantic constraints 
(or semantic expectations ruling the use of a concept. 
For example, the canonical graph for GO is: l 
\[GO 1- 
(AONT)-- > \[MOBILE_ENTITY\] 
(I)F~qT)-- > \[PLACE\] 
Many ~f the ideas contained in \[SOWS4\] have been 
used in our work. The original contribution of this paper 
can be summarized by the following items: 
find a clear correspondence between the words of 
natural language and conceptual categories (concepts 
and relations). 
• provide a lexicon of conceptual relations to express 
the semanlic formation rules of sentences 
use a l,ragmatic rather than semantic expectation 
approach to represent word-senses. As discussed later, 
the latter seems not to provide sufficient information to 
analyze m~t trivial sentences. 
To make a clear distinction between word-sense 
concepts and abstract types. It is not viable to arrange 
word-senscs in a type hierarchy and to preserve at the 
same time the richness and consistency of the 
knowledge base. 
The following sections discuss the above listed items. 
Concepts, relations and words. 
The pr()htem analyzed in this section concerns the 
translation of a words dictionary into a concept-relation 
dictionary. Which words are concepts? Which are relations? 
Which, if any. are redundant for meaning representation? 
Concepts and relations are semantic categories which 
have been adopted with different names in many models. 
Besides ct~nceplual graphs, Schank's conceptual dependency 
Word definitions in linear form are represented by wrighting in Ihe Ihsl line the name of the word W 
(concept or relation) to be defined, and in the following lines a lisl of graphs, linked on their left. side to 
W. 
250 
\[$HA72\] and semantic nets in their various 
implementations \[BRA79\] \[GRI76\] represent sentences as 
a net of concepts and semantic links. 
The ambiguity between concepts and relations is 
solved in the conceptual dependency theory, where a set of 
primitive acts and conceptual dependencies are employed. 
The use of primitives is however questionable due to the 
potential loss of expressive power. 
In the semantic net model, relations can be role 
words (father, actor, organization etc.) or verbs (eat, is-a, 
possess etc.) or position words (on, over , left etc.), 
depending on the particular implementation. 
In \[sowg4\] a dictionary of conceptual relations is 
provided, containing role words (mother, child, successor), 
modal or temporal markers (past, possible, cause etc.), 
adverbs (until). 
In our system, it was decided to derive some clear 
guidelines for the definition of a conceptual relation lexicon. 
As suggested by Fillmore in \[F1L68\], the existence of 
semantic links between words seems to be suggested by 
lexical surface structures, such as word endings, 
prepositions, syntactic roles (subject, object etc.), 
conjunctions etc. These structures do not convey a meaning 
per se, but rather are used to relate words to each other in a 
meaningful pattern. 
In the following, three correspondence rules between 
words, lexical surface structures and semantic categories 
are proposed. 
Correspondence between words and concepts. 
Words are nouns, verbs, adjectives, pronouns, 
not-prepositional adverbs. Each word can have synonyms or 
multiple meanings. 
RI: A biunivocal correspondence is assigned between 
main word meanings and concept names. Proper names 
(John, Fldo) are translated into the referent field of the 
entity type they belong to (\[PERSON: John\] ). 
Correspondence between determiners and referents 
Determiners (the, a, etc.) specify whether a word 
refers to an individual or to a generic instance. 
R2: Determiners are mapped into a specific or 
generic concept referent. 
For example "a dog" and "the dog" are translated 
respectively into \[DOG: *\[ and \[DOG: *x\[, where * and *x 
mean "a generic instance" and "a specific instance". The 
problem of concept instantiation is however far more 
complex; this will be objective of luther study. 
Correspondence between lexical surface structures and 
conceptual relations 
The role of prepositions, conjunctions, prepositional 
adverbs (hef~re, under, without etc.), word endings (nice-st, 
gold-en) verb endings and auxiliary verbs is to relate 
words, as in "1 go by bus", modify the meaning of a name, 
as in "she is the nicest", determine the tenses of verbs as in 
"I was going", etc. 
Like w~rds, functional signs may have multiple 
roles (e.g. by, to etc.), derivable from an analysis of 
grammar cases. (The term case is here intended in its 
extended meaning, as for Fillmore). 
R3: A biunivocal correspondence is assumed between 
roles played t'.y./itnctional signs and conceptual relations. 
Conceptual relations occurrences which have a 
linguistic correspondent in the sentence (as the one listed 
above) are called e.~plicit This does not exhaust the set of 
conceptual relations; there are in fact syntactic roles which 
are not expressed by signs. For example, in the phrase 
"John eats" there exist a subject-verb relation between 
"John" and "eats"; in the sentence "the nice girl", the 
adjective "nice" is a quality complement of the noun "girl" . 
Conceptual relalions which correspond to these syntactic 
roles are called implicit 
A conceptual relation is only identified by its role 
and might have implicit or explicit occurrences. For 
example, the phrases "a book about history" and "an 
history book" both embed the argument (ARG) relation: 
\[BOOK\] .... (A RG)--:> \[HISTORY\] 
The translation of surface lexical structure into 
conceptual relations allows to represent in the same way 
phrases wilh the same meaning but different syntactic 
structure, as in the latter example. 
Conceptual relations also explicit the meaning of 
syntactic roles. For example, the subject relation, which 
expresses the active role of an entity in some action, 
corresponds m different semantic relation, like agent 
(AGNT) as in ".lohn reads", initiator (INIT) as in "John 
boils potatoes" (John starts the process of boiling), 
participant (I'ART) as in "John flies to Roma" (John 
participates to a flight), instrument (INST) as in '.'the knife 
cuts". The genitive case, expressed explicitly by the 
preposition "of" or by the ending "'s", indicates a social 
relation (SOC_I,~F,|,) as in "the doctor of John" or in "the 
father of my friend", part-of (PART-OF) as in "John's 
arm", a real ,~r metaphorical possession (POSS) as in 
"John's book" and "Dante's poetry", etc. (see Appendix). 
The idea of ordering concepts in a type hierarchy 
was extended to conceptual relations. To understand the 
need of a relati~m hierarchy, consider the following graphs: 
\[ B t tll.I ~1 NG\]-- > (AGE)-- > \[YEAR: #50\] 
\[BIfll DING\]--> (EXTEN)-- > \[HEIGHT: !130\] 
\[BI!II.I~ING\]--~-(PRICE)--> ELIRE: #5.000\] 
(AGI!). (F.XTEN) and (PRICE) represent 
respectively Ih~, age, extension and price relations. By 
251 
defining a supertype (MEAS) relation, the three statements 
above could be generalized as follows: 
\[BUILDING\]-- > (MEAS)-- > \[MEASURE: *x\] 
Appendix 1 lists the set of hierarchically ordered 
relation types. At the top level, three relation categories 
have been defined: 
Role. These relations specify the role of a concept with 
respect to an action (John (AGNT) eats), to a function 
(building for (MEANS) residence) or to an event (a 
delay for (CAUSE) a traffic jam). 
2. Complement. Complement relations link an entity to a 
description of its structure (a golden (MATTER) ring) 
or an action to a description of its occurrence (going to 
(D EST) Roma). 
3. Link. Links are entity-entity or action-action type of 
relations, describing how two or more kindred 
concepts relate with respect to an action or a way of 
being. For example, they express a social relation (the 
mother of (SOC_REL) Mary), a comparison (John is 
more (MAJ) handsome than Bill), a time sequence (the 
sun after (AFTER) the rain), etc. 
STRUCTURED REPRESENTATION OF CONCEPTS. 
This section describes the structure of the semantic 
knowledge base. Many natural language processing systems 
express semantic knowledge in form of selection restriction 
or deep case constraints. In the first case, semantic 
expectations are associated to the words employed, as for 
canonical graphs; in the second case, they are associated to 
some abstraction of a word, as for example in Wilk's 
formulas \[WlL73\] and in Shank's primitive conceptual 
cases \[SHA72\]. 
Semantic expectations however do not provide 
enough knowledge to solve many language phenomena. 
Consider for example the following problems, encountered 
during the analysis of our text data base (press agency 
releases of economics): 
1. Metonimies 
"The state department, the ACE and the trade unions 
sign an agreement" 
"The meeting was held at the ACE of Roma" 
In the first sentence, ACE designates a human 
organization; it is some delegate of the ACE who 
actually sign the agreement. In the second sentence, 
ACE designates a plant, or the head office where a 
meeting took place. 
2. Syntactic ambiguity 
"The Prime Minister Craxi went to Milano for a 
meeting" 
"President Cossiga went to a residence for 
handicapped" 
In the first case, meeting is the purpose of the act go, 
in the second "handicapped" case specifies the 
destinat#m of a building. In both examples, syntactic 
rules are unable to determine whether the prepositional 
phrase should be attached to the noun or to the verb. 
Semantic expectations cannot solve this ambiguity as 
well: for example, the canonical graph for GO (see 
Section 2) does not say anything about the semantic 
validity of the conceptual relation PURPOSE. 
3. Conjtmctions 
"The slate department, the ACE and the trade unions 
sign an agreement" 
"A meeting between trade unionists and the Minister 
of tne Interior, Scalfaro" 
In the first sentence, the comma links to different 
human chillies; in the second, it specifies the name of a 
Minister. 
The above phenomena, plus many others, like metaphors, 
vagueness, ill formed sentences etc., can only be solved by 
adopting a pragmatic approach for the semantic knowledge 
base. Pragmatics is the knowledge about word uses, 
contexts, figures of speech; it potentially unlimited, but 
allows to handle without severe restrictions the richness of 
natural language. The definition of this semantic 
encyclopedia is a challenging objective, that will require a 
joint effort nf linguists and computer scientists, llowever, 
we do not believe in short cut solution of the natural 
language processing problem. 
Within our project, the following guidelines were 
adopted for 0w definition of a semantic encyclopedia: 
Each word-sense have an entry in the semantic data 
base; Ihis entry is called in the following a concept 
definition 
2. A concepl definition is a detailed description of its 
semantic expectations and of its semantically permitted 
uses (for example, a car is included as a possible 
subject of drinl~ as in "my car drinks gasoline", a 
purpose and a manner are included as possible 
relations fi~r go) 
3. F.ach word use or expectation is represented by an 
elementary ,2raph : 
(i)\[Wl.- (~aEl. CONC)-:->\[C\] 
where \\' is the concept to be defined, C some other 
concept tx'pe, and <-> is either a left or a right 
arrow. 
Partitioning a definition in elementary graphs makes it easy 
for the verificalion algorithm to determine whether a 
specific link between two words is semantically permitted or 
not. In facl, g ve ~ two word-senses W1 and W2, these are 
semantically related by a conceptual relation REL_CONC if 
252 
there exist a concept W in the knowledge base including the 
graph: 
\[W\] <- > (REL_CONC) <- > \[C\] 
where W> =WI and C> =W2. To reduce the 
extent of the knowledge base, C in (1) should be the most 
general type in the hierarchy for which the (1) holds. The 
problem of defining a concept hierarchy is however a 
complex one. The following subsection deals with type 
hierarchies. 
Word-senses and Abstract Classes 
Many knowledge representation formalisms for natural 
language order linguistic entities in a type hierarchy. This is 
used to deduce the properties of less general concepts from 
higher level concepts (property inheritance). For example, if 
a proposition like the one expressed by graph (1) is true, 
then all the propositions obtained by substitution of C with 
any of their subtypes must be true. However, generalization 
of properties is not strictly valid for linguistic entities; for 
example the graphs: 
(2) \[GO\]-- > (OBJ)-- > \[CONCRETE\] 
(3) \[WATCH\]-- > (AGNT)-- > \[BLIND\] 
are both false, even though they are specializations 
respectively of the following graphs: 
(4) \[MOVE\]-- > lOB J)-- > \[CONCRETE\] 
(5) \[WATCH\]-- > (AGNT)-- > \[ANIMATE\] 
In fact, the sentences "to go something" and "a blind 
watches" violate semantic constraints and meaning 
postulates: generalization does not preserve both 
completeness and consistency of definitions. In addition, if a 
pragmatic approach is pursued, one quickly realizes that no 
word-sense definition really includes some other; each word 
has it own specific uses and only partially overlap with other 
words. The conclusion id that is not possible to arrange 
word-senses in a hierarchy; on the other side, it is 
impractical to replace in the graph (1) the concept type C 
with all the possible word-senses Wi for which (1) is valid. 
A compromise solution has been hence adopted. The 
hierarchy of concepts is structured as follows: 
1. There are two levels of concepts: word-senses and 
abstract classes; 
2. Concepts associated to word-senses (indicated by italic 
cases) are the leaves of the hierarchy; 
Abstract conceptual classes, as MOVE_ACTS, 
HUMAN_ENTITIES, SOCIAL_ACTS etc. (upper 
cases) are the non-terminal nodes. 
In this hierarchy word-sense concepts are never linked by 
supertype relations to each other, but at most by 
brotherhood. Definitions are provided only for 
word-senses; abstract classes are only used to generalize 
elementary graphs on word uses. 
This solution does not avoid inconsistencies; for 
example, the graph (included in the definition of the 
word-sense person): 
(6) \[person\] "--(AGNT) <--\[MOVE_ACT\] 
is a semantic representation of expressions like: John moves, 
goes, jumps, runs etc. but also states the validity of the 
expression "John is the agent of flying" which is instead not 
valid if John is a person. However the definition offly will 
include: 
(7) Ifly\]-- " (AC~NT)-- > \[WINGED_ANIMATi?~S\] 
(8) \[fly\]-- -(I'ARTICIPANT)--> \[HUMAN\] 
The semantic algorithm (described in \[PAZ87\]) asserts the 
validity of a link between two words WI and W2 only if 
there exist a conceptual relation to represent the meaning of 
that link. In c,rder for a conceptual relation to be accepted: 
1. This relation must be included in some elementary 
graph (~f W1 and W2 
2. The type constraints imposed by the elementary graphs 
must bc satisfied for both W1 and W2. 
In conclusion, it is possible to write general conditions on 
word uses wiHmut get worried about exceptions. The 
following section gives an example of concept definition. 
Concept definitions 
Concept definitions have two descriptors: 
classilTcation and de l?nition. 
1. Classificalkm. 
Besides the supertype name, this descriptor also 
includes a type definition, introduced in Section 2. For 
example, the type definition for house 
is "building for residence", which in terms of 
conceptual graphs is: 
\[BUII,1)ING\] ." --(MEANS) < --\[RESIDENCE\] 
were I~IIII.I)ING represents the species, or 
supertype, and (MEANS)<--\[RESIDENCE\] the 
differentia. 
2. Definition. 
This descriptor gives the structure and functions of a 
concept. The definition is partitioned in three subareas, 
correspnnding to the three conceptual relation 
categories introduced in the previous section. 
a. P, cde. For an entity, this field lists the actions, 
/'ttnrli,gns and events, and for an action the 
subjects, objects and proposition types that can be 
related to it by means of role type relations. For 
exnmple, Ihe role subgraph for think would be 
(A(;NT) .... \[IIUMAN\] 
(o I~J!- --lTVO P \] 
253 
b. 
e. 
(MEANS)-- > \[brain\] 
(PURPOSE)-- > \[AIM'\] 
while for book would be: 
(MEANS)<--\[ACT OF COMMUNICATION\] 
(OBJ) < --\[MOVE_POSITION\] 
Complement. 
This graph describes the structure of an entity 
or the occurrence (place, time etc.) of an action. 
This is obtained by listing the concept types that 
can be linked to the given concept by means of 
complement type relations. A complement 
subgraph for EAT i~: 
(STAT)-- > \[PLACE\] 
(TIME)-- > \[TIME\] 
(MANNER)-- > \[GUSTATORY_SENSATION\] 
(QUALITY)-- > \[QUALITY_ATI'RI BUTE\] 
(QUANTITY)-- > \[QUANTITY: *x\] 
while for book is: 
(ARG) < --\[PROPOSITION: *\] 
(MA'I'FER)-- > \[paper\] 
(PART_OF)-- > \[paper_.sheet\] 
Link. 
This graph lists the concepts that can be 
related to a given concept by means of link type 
relations. A link subgraph for house is: 
(POSS) ": --\[I 1UMAN\] 
(INC, I ,)--:-\[HUMAN\] 
(I NCI ,) .... \[ DO M F,q'FIC_AN I M ALl 
(INCI ,) .... \[FURNITURE\] 
and for eat: 
(AN I))--:- \[drink\] 
(0 P POS I'r E) -: --\[starve\] 
(PR F,C)-- :- \[hunger\] 
(A r: I'I~P,)--,-\[satiety\] 
Note that sume elementary graph expresses a relation 
between two terminal nodes (as for example the opposite of 
eal); in most cases however conditions are more general. 
AN OVHIVIEW OF TIlE SYSTEM. 
This paper focused on semantic knowledge 
representation issues, lIowever, many other issues related 
to natural language processing have been dealt with. The 
purpose of lhis section is to give a brief overview of the text 
understanding system and its current status of 
implementatim~. Figure 1 shows the three modules of the 
text analyzer. 
a\] The Text Analyzer 
~de lalcmn =in. rood=Ix ~ MORPHOLOGY 
I gremmor rule= ~-~ b-~fNTACTICS 
tlonary ~ SEMANTICS 
b) A sample output 
The Prime MiniBter 
...decides a meettng with partle=... 
decide= - verb.3.=lng.pre=, meeting - naun..Ing.masc. 
portle= - noun.plur.ma=c, 
VP VP 
/ , NP V# N~' 
decldn " declde~ ' /" \' 
NP PP \ a \ PP 
4 + meetImJ 
// ",\with parH.. ' ',, 
a meeting ".,, 
with partln 
I~F'TING j_ - ! PARTIC : .... POI._PARTY_____'I 
Figure I. Scheme of the Text Understanding System 
All the modules are implemented in VM/PROLOG and run 
on IBM 3812 mainframe. The morphology associates at 
least one lemma to each word; in Italian this task is 
particularly complex due to the presence of recursive 
generation mechamsrns, such as alterations, nominalization 
of verbs, etc. I.~r example, from the lemma casa (home) it 
is possible I, derive the words cas-etta (little home), 
cas-ett-ina (nice little home), cas-ett-in-accia (ugly nice little 
i 254 
home) and so on. At present, the morphology is complete, 
and uses for its analysis a lexicon of 7000 lemmata 
\[ANT87\]. 
The syntactic analysis determines syntactic 
attachment between words by verifying grammar rules and 
forms agreement; the system is based on a context free 
grammar \[ANT87\]. Italian syntax is also more complex 
than English: in fact, sentences are usually composed by 
nested hypotaetical phrases, rather than linked paratactical. 
For example, a sentence like "John goes with his girl friend 
Mary to the house by the river to meet a friend for a pizza 
party ~ might sound odd in English but is a common 
sentence structure in Italian. 
Syntactic relations only reveal the surface structure 
of a sentence. A main problem is to determine the correct 
prepositional attachments between words: it is the task of 
semantics to explicit the meaning of preposition and to 
detect the relations between words. 
The task of disambiguating word-senses and relating 
them to each other is automatic for a human being but is 
the hardest for a computer based natural language system. 
The semantic knowledge representation model presented in 
this paper does not claim to solve the natural language 
processing problem, but seems to give promising results, in 
combination with the other system components. 
The semantic processor consists of a semantic 
knowledge base and a parsing algorithm. The semantic data 
base presently consists of 850 word-sense definitions; each 
definition includes in the average 20 elementary graphs. 
Each graph is represented by a pragmatic rule, with the 
form: 
(1) CONC_REL(W,*x) < -COND(Y,*x). 
The above has the reading :"*x modifies the word-sense W 
by the relation CONC_REL if *x is a Y". For example, the 
PR: 
AGNT(think,*x) < -COND(H UMAN_ENTITY,*y). 
corresponds to the elementary graph: 
\[think\]-- > (AGNT)-- > \[HUMAN_ENTITY\] 
The rule COND(Y,*x) requires in general a more complex 
computation than a simple supertype test, as detailed in 
\[PAZ87\]. The short term objective is to enlarge the 
dictionary to 1000 words. A concept editor has been 
developed to facilitate this task. The editor also allows to 
visualize, for each word-sense, a list of all the occurrences of 
the correspondent words within the press agency releases 
data base (about 10000 news). 
The algorithm takes as input one or more parse 
trees, as produced by the syntactic analyzer. The syntactic 
surface structures are used to derive, for each couple of 
possibly related words or phrases, an initial set of 
hypothesis fi~r the correspondent semantic structure. For 
example, a noun phrase (NP) followed by a verb phrase 
(VP) could be represented by a subset of the LINK relations 
listed in the Appendix. The specific relation is selected by 
verifying type cnnstraints, expressed in the definitions of the 
correspondent concepts. For example, the phrase "John 
opens (thc door)" gives the parse: 
NP:- NOUN(.Iohn) 
VP = V F.l~, ll(opens) 
A subject-verb relation as the above could be interpreted by 
one of tile following conceptual relations: AGNT, 
PARTICII~ANT, INSTRUMENT etc. Each relation is 
tested for ~emanlic plausibility by the rule: 
(2) RFI._CON¢?(×,y) <- (x: REL_CONC(x,*y= y) )& 
(y: REI._CONC(*x = x,y) ). 
The (2) is proved by rewriting the conditions expressed on 
the right end side in terms of COND(Y,*x) predicates, as in 
the (I), and Ihcn attempting to verify these conditions. In 
the above cxamplc, (1) is proved true for the relation 
AGNT, because: 
AGNT(open,person: John)<- (open: AGNT(open,*x = person: John) )& 
(person: AGNT(*y = open,person: John)). 
(open: AGNT(open,*x) < -COND(HUMAN_ENTITY,*x). 
(person: AGNT(*y,person) < -COND(MOVE ACT,*y)). 
The conceptual graph will be 
\[PERSON: John 1 .: --(AGNT) < --\[OPEN\] 
For a detailed description of the algorithm, refer to 
\[PAZ87\] At the end of the semantic analysis, the system 
produces two possible outputs. The first is a set of short 
paraphrases of the input sentence: for example, given the 
sentence "The ACE signs an agreement with the 
government" gives: 
The Society ACE is the agent of the act SIGN. 
AGP, EEM ENT is the result of the act SIGN. 
The GOVERN M EN'F participates to the AGREEMENT. 
The second output is a conceptual graph of the sentence, 
generated using a graphic facility. An example is shown in 
Figure 2. A PROI.OG list representing the graph is also 
stored in a ,:la~ahase for future analysis (query answering, 
deductions etc.). 
As far aq lhe semantic analysis is concerned, current 
efforts are directed towards tile development of a query 
answering system and a language generator. Future studies 
will concentrate on discourse analysis. 
255 
fo. ,oo><g) <_ I ,o, 1÷ <:o "ICONTRACT 
~" ( PART 
-~_ .. 
Figure 2. Conceptual graph for the sentence "The ACE signs a contract with the government" 
APPENDIX 
CONCEPTUAL RELATION ItlERARCHY. 
This Appendix provides a list of the three conceptual 
relation hierarchies (role, complement and link) introduced 
in Section 3. For each relation type, it is provided: 
1. The level number in the hierarchy. 
2. The complete name. 
3. The correspondent abbreviation. 
3. SIMII,ARITY (SIMIL) 
2. ORDERING (ORD) 
3. TIME SPACE ORDERING (POS) 
4. VI(~NI'I'Y (~IEAR) The house near the lake. 
4. PRF.CF, I)F, NCE (BEFORE) 
4. ACCOMPANIMENT (ACCOM) Mary went with .Iohn 
4. SIJPI)OI~,T (ON) The book on the table 
4. INC, I,IJSION (IN) 
3. LOGIC ORDERING (LOGIC) 
4. C, ON~IIN(2TION (AND) I eat and drink. 
4. I)IS.IIINCTION (OP,) Either you or me. 
4. (2ONTRAPI)OSITION (OPPOSITE) 
3. NUIIIF, RIC ORDERING (NUMERIC) 
4. ENIIMERATION (ENUM) Five political parties 
4. PARTITION (PARTITION) Two of us 
4. ADI)ITION (ADD) Fie owns a pen and also a book. 
For some of the lower level relation types, an example 
sentence is also given. In the sentence, the concepts linked 
by the relation are highlighted, and the relation is cited, if 
explicit. Bold characters are used for not terminal nodes of 
the hierarchy. 
The set of conceptual relation has been derived by an 
analysis of Italian grammar cases (the term "case" is here 
intended as for \[FIL68\] ) and by a careful study of 
examples found in the analyzed domain. The final set is a 
trade-off between two competing requirements: 
2. 
A large number of conceptual relations improves the 
expressiveness of the representation model and allows a 
"fine" interpretation; 
A small number of conceptual relations simplifies the 
task of semantic verification, i.e. to replace syntactic 
relations between words by conceptual relations 
between concepts. 
Link relations 
I. LINK (LINK) 
2. HIERARCHY (HIER) 
3. POSSESSION (POSS) The house of John 
3. SOCIAL RELATION (SOC_REL) The mother of. Jolm 
3. KIND O-F (KIND_OF) The minister of the Interiors 
2. COMPA-RISON (COMe) 
3. MAJORITY (MAJ) He is nicer than me 
3. MINORITY (MIN) 
3. EQUALITY (EQ) 
Complement relations 
I.COMPI.EMEN 7" (COMPL) 
2. OCCURRF.NCE ( OCCURR) 
3. PI, ACI:" (PLACE) 
4.STATIJS_IN (STAT_IN) I live in Roma 
4. ,$IOVE (151OVE) 
5. MOVF,_TO (DI2£;T) 
5. MOVETROUGH (PATH) 
5. MOVE_IN (MOVE_IN) 
5. MOVE FROM (SOURCE) 
3. TIME ( TI,I, fE) 
4. I)F, TIH~MINED TIME (PTIME) I arrived attire 
4. T1M F, I ,ENGI-IT (TLENGI IT) The movie lasted 
for three hours 
4. STARTI NG TIME (START) The skyscraper was built 
since 1940 
4. I-NI)ING TIME (END) 
4. PIIAgF, (I'IIASE) 
3. CONTEXT (CONTEXT) 
4. STATFMF, NT (STATEMENT) I will surely come 
4. I'OSSIIIII,ITY (POSSIBLE) 
4. NEGATION (NOT) 
4. QI~I~RY (QUERY) 
4. IH:,I,IF, F (BF, I,IEF) I think that she will arrive 
3. QIIAI,ITY (QUALITY) 
3. QUANITI'Y (QUANTITY) 
3. INITIAl VAI,I, JE (IVAI,) The shares increased their value 
fi'om 1000 dollars 
3. FINAl, VAIAIF, (FVAL) to I500 
2. S'I'RU(TT~"RI £ (STRUCT) 
3. SUBSI,I Ix,'('/: (SUBST) 
256 
4. MA'VFER (MATTER) Wooden window 
4. ARGUMENT (ARG) 
4. PART OF (PART OF) John's arm. 
3. SU/i Pe "(SH/I eE) 
4. CHARACTERISTIC (CHRC) John is nice. 
4. MEASURE (MEltS) 
5. AGE (AGE) 
5. WEIGHT (WEIGHT) 
5. EXTENSION (EXTEN) A five feet man 
5. LIMITATION (LIMIT) She is good at mathematics. 
5.PRICE (PRICE) 
Role relations 
I. ROLE (ROLE) 
2. HUM/IN_ROLES (HUM_ROL) 
3. AGENT (AGNT)The escape of the enemies 
3. PARTICIPANT (PART) Johnfiies to Roma. 
3. INITIATOR (INIT) John boils eggs. 
3. PRODUCER (PRODUCER) John's advise 
3. EXPER1ENCER (EXPER) John is cold. 
3. BENEFIT (BENEFIT) Parents sacrifice themselves to the sons. 
3. DISADVANTAGE (DISADV) 
3. PATIENT (PATIENT) Mary loves John 
3. RECIPIENT (RCPT) I give an apple to him. 
2. EVENT_ROLES (EV_ROL) 
3. CAUSE (CAUSE) fie shivers with cold. 
3. MEANS (MEANS) Profits increase investments 
3. PURPOSE (PURPOSE) 
3. CONDITION (COND) lfyou come then you will enjoy. 
3. RESULT (RESULT) He was condemned to damages. 
2. OBJECT ROLES ( OB_ROL) 
3. INSTRUMENT (INST) The key opensthe door. 
3. SUBJECT (SUB J) The ball rolls. 
3. OBJECT (OBJ) John eats the apple. 
\[ANTS7\] 
\[BRA79\] 
\[DEJ79\] 
\[FlI~82 
\[GRI76\] 
REFERENCES 
Antonacci F., Russo M. Three steps 
towards natural language understanding : 
morphology, morpho~ntax, syntax. 
submitted 1987 
Brachman P. On the Epistemological 
Status of Semantic networks in Associative 
Networks: Representation and use of 
Knowledge by Computers, Academic Press, 
N.Y. 1979 
De Jong G.F. Skimming stories in real 
time: An experiment in integrated 
understanding. Technical Rept. 158, Yale 
University, Dept. of Computer Science, New 
Iteaven, CT, 1979 
Fillmore The case for case Universal in 
Linguistic Theory, Bach & ltarms eds., New 
York 1968 
Griffith R. Information StruetnreslBMSt. 
Jose, 1976. 
EIIFIS6\] 
\[lInlS6\] 
\[ I.F.B83.\] 
\[I,1:.118.~\] 
\[I ,V,l.JSsll 
\[MIN75\] 
\[PAZ,q7\] 
\[RIE79\] 
\[Sl IA72\] 
\[SIIA77\] 
Esowsal 
\[sows61 
\[ V FI ,g7 I 
\[W11,73 1 
llcidorn G.E. Augmented Phrase Strneture 
Grammar. in Theoretical Issues in Natural 
Language Processing, Shank and 
Nash-Webber eds, Association for 
Computational Linguistics, 1975 
I Ieidorn G.E. PNLP: q\]le Programming 
l,anguage for Natural Langnage Processing. 
Forthcoming 
I,ebowitz M., Researcher: an overview. 
Proc. of A A A I Conference, 1983. 
I,ehnert W.G., Dyer M.G., Johnson P.N., 
Yang C.J., flarley S. BORIS- An 
Experiment in ln-Depht Under~anding of 
Narratives. Artificial Intelligence, Fol 20. 
1983 
I,euzzi S., Russo M. Un analizzatore 
morfologico della lingua ltaliana. GUI_,P 
Conference, Genova 1986 
Mmsky M. A framework for representing 
Knowledge in Psichology for Computer 
Vision, Winston, 1975. 
M.T. Pazienza, P. Velardi Pragmatic 
Knowledge on Word Uses for Semantic 
Analysis of Texts in Knowledge 
P, epresentation with Conceptual Graphs 
edited by John Sowa, Addison Wesley, to 
appear 
b',ieger C., Small S. Word expert parsing. 
I, ICA\[, 1979. 
Shank R.C. Conceptual Dependency: a 
theory of natnral language understanding. 
Cognitive Psicology, vol 3 1972 
Shank R., Abelson R, Scripts, Plans, Goals 
and Understanding. L. Erlbaum Associates, 
1977 
Sowa, John F. Conceptual structures: 
Information Processing in Mind and 
Machine. Addison- Wesley, Reading, 1984 
Sown, John F. Using a lexicon of canonical 
graphs in a . conceptual parser. 
Computational Linguistics, forthcoming. 
P. Velardi, M.T. Pazienza, M. De' 
Giovanetti Utterance Generation from 
Conceptual Graphs submitted 
Y. A. Wilks Preference Semantics 
,~4emoranda from the Artificial Intelligence 
I.aboratory, MIT 1973 
257 
