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Modelling Knowledge for a Natural Language

Understanding System

G u d r u n g l o s e , T h o m , x s P i r l e i n

I B M G e r m a n y S c i e n t i f i c C e n t e r

I n s t i t u t e f o r K n o w l e d g e B a s e d S y s t e m s P . O . B o x 80 08 80

D - 7 0 0 0 S t u t t g a r t 80

E m a i h K L O S E @ D S O L I L O G , P I R L E I N @ D S O L I L O G

A b s t r a c t

In the field of knowledge based systems for natural lan- guage processing, one of the most challenging aims is to use parts of an existing knowledge base for differ- ent domains and/or different tasks. We support the point that this problem can only be solved by using ad- equate metainformation about the content and struc- turing principles of the representational systems con- cerned. One of the prerequisites in this respect is the transparency of modelling decisions.

After a short introduction to our scenario, we will propose general dimensions for characterizing knowl- edge in knowledge based systems. These dimensions will be differentiated according to linguistic levels of investigation in order to deduce structuring principles

for the modelling process. The resulting criteria will be evaluated in a detailed e~ample taken from our proto- typical implementation.

We hope to contribute some promising steps towards a methodology of knowledge engineering with natural language and common sense orientation.

1

I n t r o d u c t i o n

In the following, we wmlt to sketch first results of knowledge engineering research which was under- taken for the LILOG project (Linguistic and logic methods). LILOG develops concepts for natural language systems for text understanding. Major reslflts are available in a prototype system LEU/21 (I, ILOG Experimentier- Umgebung) 2.

In order to reduce the complexity of the system, it has to be decomposed into modules.

! L e u / 2 is b e i n g d e v e l o p p e d at I B M G e r m a n y in c o o p e r s - lion w i t h s o m e u n l v e r a l t y p a r t n e r s , a n d il fully i m p l e m e n t e d in P r o l o g u n d e r AIX. T h e k n o w l e d g e b a , e for t h e d o m a i n un- d e r i n v e s t i g a t i o n c o n s i s t s of a b o u t 600 concept d e f i n i t i o n s , a m o n g t h e s e s o m e 100 b e l o n g i n g to t h e u p p e r l t r u c t u r e . T h e n u m b e r of a t t r i b u t e s for each of t h e s e c o n c e p t s ever- ages a r o u n d 20. At thlJ t i m e the n u m b e r of a x i o m s for o u r d o m a i n it a p p r o x i m a t e l y 300.

~ " L I L O G E x p e t i m e l l t a l E n v l r o n m e n C '

Our approach e m b o d i e s modules oriented to- wards levels oflingadstic investigation like morphol- ogy, syntax and semantics, h~ addition the modules differentiate between analysis and the generation processes. In the ideal case, all processes and mod- ules will be supported by conunonsense ]ulowledge. A crucial problem in tiffs context is the construc- tion of an adequ'ate background knowledge base. The: need for a methodology is obvious. First steps have been m a d e in expert system research, where both domain andtask are for the most part clearly specifiable. This does not hold for systems with natural language - and conunon sense orientation. In what follows, we will outline the lulowledge en- gineering approach in LILOG along three dimen- sions.

T a s k :

Domain and te~cts were selected in order to cover a wide variety of lingalistic phenomena to be han- dled by the linguistic parts of the system (i.e. pars- lug:and generating components). Iat order to prove the;appropriate understanding of the texts, the at. chitecture was d~sigqled a.o. as a question/answer system. Hence, we get the additional task to gen- erate language.

D o m a i n :

For L E U / 2 , the domain was restricted to travel guide information about the city center of Diisseldorf. As a first step, a set of written data was obtained by travel guides, supplemented by travel agencies and a local inspection of Dfisseldorf city center.

T h e set of different entities was to meet the fol- lowing eonditlona: it should be large enough for a relevant size of the knowledge base, interconnected enough to allow for interesting inferences but at the same thue small enough for being handled within a In'ototypical implementation.

We decided to work with a couple of short texts (frequently found in travel guides), which describe

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particular sightseeing items, and a one page narra- tive text about a group of people on a prototypical sightseeing tour. In the next step, the chosen texts were classified according to lingnistic criteria and analyzed for their propositional contents.

G r a n u l a r i t y :

hi order to obtain a first hint at the variety of text understanding tasks whidi L E U / 2 was in- tended to deal with, native speakers were asked to formulate questions and to provide acceptable an- swers concerning the contents of the texts.

The selection of items aud the way these native speakers talked about them, served as guideline to determine an appropriate granularity of the luiowl- edge base.

The overall performance of the system is d e t e r - mined by the interaction of it's components. Due to the modular approach, the relevant subtasks of

the kno,~ledge base had to be separated from those

of the lezical, syntactic, semantic analysis compo-

nents and the generation module. As a result of

this prelinfinary investigation, three d i m e n s i o n s o f k n o w l e d g e turned out to be crucial to the mod- elling process.

2

D i m e n s i o n s of K n o w l e d g e

We will discuss knowledge from two different per- spectives. On the one hand we have those:condi- tions which lead to qualitative requirements con- cerning the contents of the lu~owledge base. T h e other perspective concerns aspects induced by for- real devices, i.e. the kalowledge representation for- nmlism used.

2 . 1 Q u a l i t a t i v e D i m e n s i o n s

If you consider knowledge representation as a spe- cial case of model theory, you will get a hint of how to proceed. As to the breadth of the model, the first dimension at issue, this means:

The job of the representing world is to reflect some aspects of the represented wodd in some fashion.[Palmer, 1978]

As regarding grcznularily, the second dimension, a model reflects only a subset of the characteristics of the entities it represents. This, in turn, deter- mines the depth of the m o d e l

A tldrd dimension is given b y the complexity o]

the task the model is intended to cover.

All three dimensions are shown in picture 1. Some of the consequences for the model in LILOG following from this view of knowledge rep- resentation are described below.

2 . 2 F o r m a l D e v i c e s o f R e p r e s e n t a - t i o n

In the field of logic based formalisms for coding background knowledge in natural language process-

B r e a d t h of the domain

Depth of the model

-<__

Task orientation

Figure 1: Qualitative dimensions of knowledge

ing systems, there is some controversy on tile design and use of formal constructs. Topics in this de- bate are tile function of axioms compared to recent expert system teHmology, the function of struc- tured concept hierarchies [Monarch and Nirenburg, 1987], the quality and number of additional at- tributes (roles in K L - O N E like systems) or syn- tactic validation Criteria [Horacek, 1989]. Our ap- proach aims at finding useful sdectional criteria for different expressive means of the formalism L L w o o in order to bridge the actual [gap between problem

driven and technology driven ~ research.

We can make use of two kinds of formal con- structs:

s A frmne-des.cription language similar to KL- ONE (cf. e.g. [Brachman and Schmolze, 1985]), which serves to represent tile terminob ogy of the domain by means of !

sort expressions for classes of entities, or- ganized hierarchically as sets and subsets (i.e. the logical subsumption relation), mid

- two Place predicates and functions (i.e. features and roles), attached to specific sorts and constituting functional and re- lational connections between sorts, and

• axioms of first order predicate logic, express- ing inferential dependencies between domain terms hi form of the axiomatic semmltics for those terms.

So the formalism used h e r e ' is colnparable to e.g. K R Y P T O N (s.e.g. [Brachman et ~ . , 19851).

In the following, we will discuss the qualitative dimensions of knowledge in more detail. We will fo- cus the qualitative criteria by differentiating them according to our scenario.

SSee [Lehnert, 1988] for that distinction.

tFor a detailed description of the formalism LLILOO see [Pletat and yon Luck, 1089]

[image:2.596.345.525.110.245.2]
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3

Criteria for Structuring the

Ontology

3 . 1 D e m a n d s R e s u l t i n g f r o m t h e T a s k

As mentioned above, the task of our system is to simulate text understanding. This requires a trans- fer of insights from linguistic research into knowl- edge engineering. In the ideal case, structures of the model will be strongly influenced by natural language analyses.

Linguistic knowledge is relevant in various re- spects:

W o r d o r i e n t a t i o n , for example, implies close hxterrelationships with research on lexical knowledge: afrdiated generic terms, discrimi- nating features, idiosyncratic aspects of use, etc. However, you may run into difficulties by relating syntactic categories (like word classes) with conceptual structures. So thematic roles cannot be directly trmtsformed into ontological roles as a part of the background knowledge. In the sentence

The bus took the participants of the conference to the city center, s

the 'bus' is ml agent of art. event from the syn- tactic point of view attd at the same time con- ceptualized as instrument (and n o t agent) of mx event in an ontological sense.

S e n t e n c e o r i e n t e d linguistic investigation implies the reconstruction of knowledge on the sentence level, as opposed to the mean- ing of single words or of textual structures. As all illustration might serve temporal in- formation about the progress of actions or situations. Theoretical: work in this field was initiated e.g. by Z. Vendlcr [Vendler, 1967] with his analysis of verbs and times. His differentiation of states, activities, accom-

plishments and achievements has been estab-

lished as a well known classification of verbs. One i m p o r t a n t criterion for this disthtction is

the goal-orientedness of the concerned verbs:

states and activities are by definition not goal-oriented, whereas accomplishments and achievements are goal-oriented in a temporaUy extended or punctual way, respectively.

T h e aspect of goal-orlentedness turned out to be central in our domain, e.g. as to directional verbs of movement. T h e sentence

The tourists took the bus to the Rhine and went for a boat trip. s

S T h e G e r m a n version of t h e s e n t e n c e is p a r t of t h e t e x t ©orptm of L E O / 2 : " D e r B u s b r a c h t e die T e i l n e h m e r der K o n - f e r e n s in die I n n e n s t a d t " .

e " D i e T o u r i s t e n n a l u n e n d e n B u s bls EUlI1 R]lel,ll tad

m u c h , an e l n e n B o o , s a n , f l a g . "

allows to hlfer by default that the tourists reached their goal (the Rhine), because the lo- cation of the following event (the boat trip) is the stone as the arrival point of the bus ride. By introducing goal-orientedness as a part of the definition of events, it will hence be possi- ble to give an afflrmativc answer to the ques- tion

Were the!ourists at the Rhine? T

s Moreover, a text necessarily involves dis- c o u r s e o r i e n t e d information. Text under- standing phenomena like annphora resolution can only be accounted by accessing back- ground knowledge concerning interconceptual relation.

The tourists went for a boat trip. They took the seats on the sundeck, s

In order to capture the meaning of these sen- tences, three:steps have to be inferred: A boat trip is usually undertaken with a boat; a boat often has a sundeck; and a sundeck mostly of- fers seats.

3 . 2 D e m a n d s R e s u l t i n g f r o m t h e D o m a i n

In the L E U / 2 context, we have to deal with the comprehensive task of text understanding mtd a relatively narrow domain. Consequently, the general problem of conceptualization is lim- ited by a restricted number of entities relevant to our field. Modelling these entities includes b o t h the selection of concepts which appear ill the domain, and the plausible combination and sununhlg of recurrent concepts. The

plau-

sibility

of modelling decisions in this sense can be judged from an engineering point of view in terms of optimizing search space (system per- formance) and from a philosophical point of view in terms of the principle "of economy o/ the ontolog~

T h e concepts

RBSTAURATION,

CONSTRUCTION and RBNOVATION nlay serve as ml illustration taken from our domain. As they share simJlar aspects anti inferences, we decided to intro- duce the supersort MODIFICATION (see section 4).

3 , 3 G r a n u l a r i t y : D e p t h o f M o d - e l l i n g a n d I n f e r e n c l n g

In the third qualitative dimension of knowl- edge we have to face the problem of dellmi- tatlng the depth of the model in order to re- duce complexity. As it is not possible to give

r °'Waren die To~tEisten a n t R h e l n ? "

t T h e G e r m a n version of t h e s e n t e n c e is p a r t o f t h e text c o r p u s of L E U / 2 t ~ D i e T o t ~ i s t e n m a c t t t e n e l n e n B o o , t a u t . flux. Sic n a h m e a dl.'e P l a t t e a u f d e m $ o n n e n d e c k e i n " .

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an exhaustive system of categories o, it Seems legitimate to deternfine primitive concepts de- pendent on the chosen task and domain. In addition, selectional criteria for clusters 'of in- ferences have to be determined. (See example in section 4). As a possibility of measuring the depth of a model, tlayes ({llayes, i979]) proposed a ratio of axioms per concept. : Aside from measuring the expression of dimen- sions of knowledge by me/ms of quantitative data, it is important to consider qualitative de- pendencies between the

depth

and task Of the model on the one hand and between t h e

depth

and

domain

on the other.

D e p t h in r e l a t i o n t o t h e t a s k

Within the task of text understmlding,i some requirements of representation are e.g.: goal orientation, cuhnination, causal connections, intention, etc. [Trabasso and Sperry, 1985]. lal all these cases the dlosen granularity has strong impact upon the resohttion of interre- lations in the texts) °

D e p t h in r e l a t i o n t o t h e d o m a i n

This connection cmt be illustrated by the fol- lowing exmnple: A typical event of our domain

is RBSTAURATION. ~n our scenario, t0uristic

aspects like the architect (agent), the time and the object concerned (e.g.,, tlle facade) will be of crucial importance. Given a different sce- nario like the protection of historical m o n u - ments, we would have to face an interest in considerably more details, requiring the cholcc of a deeper granularity.

4

D e s i g n of t h e K n o w l -

edge B a s e

Ill this section, we first want to give a brief sur- vey of the ontology. After that, wc will take up the sorts and regularities mentioned so far and present a structured exemplary mo~lel for- malized in LLXLOO •

Sort expressions arc used to represent the cate- gories of our domain model. T h e upper strut-

lure of the resulting ontology portrays some

generaLized schemes of organigation of relative domain-independence. W h e n descend!ng the model towards the lower

#fracture,

the cate- gories arc defined m u c h closer to the word level and therefore domain-specific in the sense of

ezplicit text ]ulowledge. |I

As already nlentioncd, we want to simulate un- derstanding of basically two different types of ...

°See for example [Tamas, 1986, p. 509]

t ° F o r a m o r e detailed di.cu.sion, see [Pirleln, 1900].

t l This differentiation between u p p e r and l o w e r ~tructure

o f the model is introduced by [Maim et al., 1985].

texts, i.e. short texts describing single sight- seeing items and narrative texts dealing with sequences of events. This leads us to the re- qulremcnt of both all object-oriented and all event-oriented part of the eoncephlal hierar- chy.

Consequently, one of our basic design decisions is due to J. Hobbs (cf. [llobbs et al., 1987]) and results ill a reification of predicates. So in our model all events, states etc. have concept status on their own.

This technique enables us to model the case frames for verbs in all analogical manner to the lexical entrie~ of the analyzing component as well as to incorporate tile structures for events etc. within the categories alike the definitions for objects 121 It makes sense to think about objects as w c U as about events in terms of their spatial mad temporal environment, although these knowleklge specifications will obviously be quite different.

A n example taken from the event cluster m a y serve as an illustration of several consequences of the criteria mentioned above. As to the b r e a d t h of the model, the relevance of the event part of the ontology appears intuitively plausible with respect to our domain, namely a scenario of cities, with modifying events. W e have to deal with sights of the city like facades of important.buildings, and the events of mod- ification related to them show a considerable resemblance o f ilnportant features of meaning - although the verbs are no real synonyms in the linguistic sense.

Figure 2 shows a screen dump witll the rele- vant part of' the concept hierarchy. The pic- ture illustrates the effect of bundling that the introduction!: of adequate superconcepts has, and which allows for structured inferencing in terms of system efficiency, hi this part of our concept l l i c r ~ c h y the boarderlinc between Up- per Structure attd Lower Structure is clearly identifiable. When descending the hierarchy, the sort K O N S T R U K T I V S I T falls out into sev- eral domain-dependent subsorts.

T h e figure is followed by the respective sort expressions written in the bt.xLoo list struc- ture(the sort K O N S T R U K T I V S I T in the figur© corresponds to CONSTRUC~PION in the English list of sort expressions), expanded by roles and features which do not appear in tile graphic representatimt. It should be n o t e d here that a third kind of information is omitted even in the list notation. More general roles and fen- tures (llke e.g. agent, time m~d so on) are in- herited by supcrconcepts and not visible in nei- ther presentation. (The short lille in tile u p p e r left corner of some concept boxes indicate the existence o f additional hidden superconcepts.)

I 2 A slmillar tecimlq,te you call filld e.g. in [Matin et ai.,

toss].

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t ~

~b(~,rm a

~tlntlt~t

~ t KIx~" t

~¢¢ ' i

Ilk i

kt t

r l ~ m t a t ~ IMrIIeLePI I Pole I :

Ple*m ~hrr ~rmw*.~ I .l~eu f m q m ~ l tacat~M IllmeJ

r l t t ~ nte,r ptr~eeterl l role ) :

flus* eBtt~ i~rt.et~n { .l~eu fenV~" t~ckt, rm~l l i b e l

-~, In~o black ,~bile black -> pro,ross black .tile black -> bf

-~ lgels -> sleDS

-+ display black . t i l e blacb -> qets

- , llst black .tile black

r°le

j j ~ tte._l r a~,.msd~

Figure 2: Lnplementation of the 'modification'- event

The definition of the relevant event concepts in LLILOO is followed by an axiom which trans- fers information about the time of a construc- tion event to the beginning of lifetime of the concerned object. This kind of structured modelling allows to dispense with writing sim- ilar axioms for a number of resembling events.

In order to demonstrate t a s k orientation, it would be necessary to consider a broader part of the ontology, because aspects like intention, causality or culmination h a v e been modeUed separately. In addition, one would have to take a closer look at the ensemble of connected com- ponents in the system. T h e limitation of the d e p t h of the model can be seen from the fact that the event concepts discussed do not have more differentiated subconcepts mid, of course, from the fact that not nil possible roles and features have been integrated into the model. In a scenario "protection of historical monu- ments", for example, the instruments of ren- ovation might be central and would induce a partly different granularity in the model.

D e f i n i t i o n in LLILOQ:

[image:5.596.36.551.102.440.2]

s o r t a o d i l l o a t i e n s o r t e s s e n t i a l n e d i £

| i t u a t i e n .

< tnd(Iodifioation,

e s s e n t i a l _ o b j : o b j e c t ) .

s o r t v a r i t t l o a

s o r t o s n s t l " ~ o ~ i o n l o f t d e s t r u e t ~ o n

s o r t aaterlal, variation

< e t s o n t i a l m o d i . ( . < e s s o n t i a l a s d i ~ . < e s s e n t i a l J o d i £ . a n d ( v a r i a t i o n .

e l l e n t i a l obj : m a t e r i a l o b j o o t ) .

s o r t r e s t a u ~ r ~ i o n < n a t e r i a l v a r i a t i o n .

s o r t m a t e r i a l o o n s t r u o t i o n < a n d ( o o n s t r u e t l o n . o l s e n t i t I + o b j :

n a t e r l a l o b j e o t ) .

s o r t n e n t t l e 0 n s t r u o t i o n < a n d ( c o n s b r u o t i o n . o J o o n t i t l . o b j :

n o t ( u a t o r i a l o b j a o t ) ,

s o r t b u i l d i n g < m a t e r i a l ¢ o n s t r u o t i o a .

The twofold ~nodelllng of PHYSICAL and MBN- T A b C O N S T R U C T I O N is e . g . n e c e s s a r y t o dis- tinguish ideas developped by an architect from the realization of the building. 13

For constructive events one can define the fol- lowing regularity (axiom):

a x i o m r u l e 3 0 g e r a l l DI : c o n s t r u c t i o n , 02 : o b j e c t , T3 : t i n e i n t o r v a l l ;

e s s o n t i a l . o b j ( D X , 0 2 ) a n d l i v e t i m o ( O 2 , T 3 ) - >

m o e t s ( D I , T 3 ) .

The relation meet,: is one expression of our axionlntizati0n of Allen's time interval logic [Allen, 19831 in LLILOG . Rule30 exemplifies a transformation rule between the clusters of events nnd objects, respectively.

Our task setting implies certain ways of in- teraction between Knowledge Engineering and the generation component. 1t" you want to ob- tain flexibility for the generation component with respect to the possible diversity of an- swers, information should be available in cases of object centered questions

("What do you know about object xy ...")

as well as in comparable event oriented re- quests

("What happened after ...").

I t F o r r e a s o n s o f c l a r i t y we r e n o t m c e d o n s h o w i n g a l l re- s p e c t i v e S u p e r s o r t 8 ,

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5

C o n c l u s i o n

O n e of the m o s t discussed topics in t h e field of text n n d e r s t a n d i n g is t h e s e p a r a t i o n b e t w e e n s e m a n t i c k n o w l e d g e on the one h a n d a n d com- m o n sense k n o w l e d g e or world ]mowledge on the o t h e r . D u r i n g t h e c o n c e p t i a n a n d imple- m e n t a t i o n of the m o d u l e s in o u r p r o t o t y p e ,

this discussion was reflected by a considerable flexibility in the division Of functions between semantic analysis and inferential processes.

During the integration, descriptive p a r t s of lin- guistic theories had to be completed with pro- cedural or functional aspects. Typical tats fits a p p e a r e d eadl time it was clear what should be ezpre~sed within certMn modules (like mor- phology or syntax), but it was unclear how to proceed from one module to the next. In the ideal case, this allowed for conclusions:on in- compatibilities between the levels of linguistic analysis corresponding to the respective, mod- ules.

One of these p h e n o m e n a is the identification of adjectival passive constructions versus regular verb:

The museum will be opened at 11 a . l n . 14 .

The illuSelllU is open from 9 to 15 t ' .

According to Vendler's classifies,toni open should be categorized as an event in the first sentence and, combined with to be in the sec- ond case, as a state. T h e integration I of the modules showed t h a t none of the s y s t e m com- ponents was able to deliver this differentiation - in this case, the reason was the incompati- bility between unsorted unification grauunars and the necessity to overwrite default vMues.

l.n the fold of Knowledge Engineering, the question how to make contents of o n e lu~owl. edge base available to n second one (n6rmally with quite a n o t h e r kind of task setting) has been receiving g r o w i . g attention. One of the

m o s t i n t e r e s t i n g p a r t s of this p r o b l e m ~:onsists

in the iuterrelationship between c o t l u u o n sense - and domain specific knowledge. We hope to contribute some i m p o r t a n t steps towards han- dling this problenx by m a k i n g explicit a nunx-

b e t of c o m m o n sense oriented modelling deci- sions within the L I L O G context. It is obvious, though, t h a t b o t h background knowledge for n a t u r a l language processing and the adequate i m p l e m e n t a t i o n of m e t a i n f o r n m t i o n for knowl- edge base contents will be an ongoing affair for the next years.

Acknowledgement: We thank Bars Courts, Ti. bor Kiss, Ewald Lang, K a i uon Luck and Mar-

t*,,Das Museum wlrd tun 11 Uhr ge~ffnet" IS"Das Musettrn is, yon 9 bls 16 Ohr ge~ffnet"

tin Mezger ]or useJul ideas and stimulating dis. cessions.

R e f e r e n c e s

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about temportd intervals. Communications o]the

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Figure

Figure 1: Qualitative dimensions of knowledge
Figure 2: Lnplementation of the 'modification'- event

References

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