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<?xml version="1.0" standalone="yes"?> <Paper uid="J79-1024"> <Title>American Journal of Computational Linguistics Mi crof i che 2 4 THE SQAP DATA BASE FOR NATURAL LANGUAGE INFOR8MATION</Title> <Section position="5" start_page="10" end_page="10" type="metho"> <SectionTitle> HAPPY-P GIRL-P </SectionTitle> <Paragraph position="0"> ))A happy girl)) A statement like &quot;There is ad always happy girlrf or &quot;One girl 1s always happy&quot; would be represented in the same way, with an object node and two short relation6 on ~t to the two predicates, ATTR to the adjectival predicate, PRED to the nominal predicate. If we meet the natural language phrase &quot;One girl is nice todaytT, then we cannot represent it as simply. We have to affix a time to the relation between the girl and HAFPY*Pcc.</Paragraph> <Paragraph position="1"> One way to do this would be to always have the ability to add ertrs short relations to existing short relations, !Phis would require that short relations 'are represented in the data base in a form where there is a place ta add a list of extra shost relations, and this would triple the size of the data base, Tnstead we have an expanded form of those short relations to which we want to add other relations, This expanded form xs only used when it ia needed, the non-expanded form is used when there are no added short relations, The expanded form for the PRED short relatlon is a node of the type &quot;eventtt.</Paragraph> <Paragraph position="2"> WOne girl is happy todayw will thus be repreaented like in figure 3.</Paragraph> </Section> <Section position="6" start_page="10" end_page="12" type="metho"> <SectionTitle> T CASE </SectionTitle> <Paragraph position="0"> Figure 3 ))One girl is happy today)) The advantage of having such an extra concept in the data base is that we can easily add more short relations to the event node llOne girl is happy todq&quot;, for example to represent &quot;in the school&quot; or &quot;becautse of the weather&quot; or &quot;according to what Tom said&quot; .</Paragraph> <Paragraph position="1"> Since we want to deal with true statements, hypothetical statements and statements belonging to some person's belief structure, we always add a relation to an event node indicating which belief structure it belongs to, true events belong to the set &quot;Tfl#3+Sf1 of all true statements. Since the relation VART TRUE*Sn is so common, we represent it in pictures with the earth sign of electric charts 1 A .</Paragraph> <Paragraph position="2"> .. null The statement &quot;John believes that Mary loves him&quot; would thus be represented like in figure 4t Note that there is an earth sign on the true event, but no earth sign on the event belonging to Johnrs belief strucWre. Note that predicates and relations in natural language are often not represented directly as short relatlong in our data base. !'John is the father of Angelica&quot; is thus not represented by a short relation &quot;FATHER&quot; from &quot;Johnv to &quot;Angelicav but rather with an event node like in figure 54</Paragraph> </Section> <Section position="7" start_page="12" end_page="15" type="metho"> <SectionTitle> FA ER-P </SectionTitle> <Paragraph position="0"> r'lm Figure 5 %John is the father of Angelican 4& Quantifiers on the short re1at:ors. Every node in our data base can stand for a set of objects instead of for just a single objbct. Thus we can represent &quot;A11 nice girlsv with a node representing the set of all nice girls .</Paragraph> <Paragraph position="1"> This means that we need quantifiers on the short relations, to be able to express relationships between sets. Tf there is a short relation R between two sets A and B, then the relamtion R might not be true between any member of A and any member of B. We have several cases: !Fhese and 0th~ cases axe represented in our data base with three qu.an,tifiers ALL, SOME and ITS, The difference belmeen SOME and 12% is shown by the difference between Ucle second wd the third example. One quantifier is placed on each end of the rel&tion, The four examples above will thus in our data base look 1;ike this r A) (ALL A, R, ALL B) iv) (SOME A, R,*ITS B) The difference between ITS and SOME can be understood if you look at the statement 'Wvery man is in a carv This can mean that ffEvery man Is iflside one single car&quot; or it can mean !'For every man there is one car in which he is. The first ph~ase might in our data base be represented as '%very manM ALL IN SOME while the second might be represented as &quot;Every manu ALL IN IT The~e are simple rules to mmipulate. the quantifiers when the deduction Pules chain from node to node in the data base. This is described in Sandewall 1969.</Paragraph> <Paragraph position="2"> In %he following, if no quantifier is marked on a short relation in a figure, then ALL is implicit.</Paragraph> <Paragraph position="3"> 5, Deduction in the data base The data base does not contain all true statements explicitly, some of %hem have to be deduced when needed, Basically all deduction rules can be seen as pattern matching, You haue a pattern swing for example that Vf somethipg hot is near something inflammable then the irnflammable will catch fire&quot;. Then we h'ave sbme actual situation, explicit or deduced, e.g. &quot;The burnine cigarette is thrown in the petrol tankn. In our data base, as in figure 6.</Paragraph> <Paragraph position="4"> Before using the deduction rule, we must match the pattern to the actual situation. The p-attern can contain many interconnected rides, and the reality may not at first resemble the pattern directly, deduction may be necessary to see the resemblance.</Paragraph> <Paragraph position="5"> The simplest deduction rule possible is just a pattern of two short relations from ,which a third can be deduced: &quot;If A R1 B and B R2 C then A R3 C&quot;, a simple example: &quot;If A is subset of B, and B is subset of C, then A is subset of Ctf. Since such rules link together nodes through a chain of short relations, they are called chaining rules. Spme chaining rules require side relations on B to the fullfilled, for example &quot;A BY B, and A CASE C implies B RED CI1, but only if A is a true event.</Paragraph> <Paragraph position="6"> This is described in Sandewall 1969 and in Makila 1972.</Paragraph> </Section> <Section position="8" start_page="15" end_page="17" type="metho"> <SectionTitle> 6. Variables </SectionTitle> <Paragraph position="0"> The simplest kind of deduction pattern involves just one node.</Paragraph> <Paragraph position="1"> Such a node is calleC a variable, For example, VARIABLES are use& in the translation of 'Wery intelligent man is a bad soldierv which is represented like in figure 7; Y.a.riables have a new quantifier on them, DEF. This indicates that this short relation is part of the definition of that variable. The variable '%very intelligent mantt above corresponds to the set of all objects which satisfy the definition. This means that ad soon as we find an object in the data base for which we know or can deduce that it satisfies the definition, then we know that it belongs to the VARIABLE Above, and we can thus deduce that it is a bad soldier.</Paragraph> </Section> <Section position="9" start_page="17" end_page="19" type="metho"> <SectionTitle> 7. Keys </SectionTitle> <Paragraph position="0"> Sometimes the deduction requires a pattern of more than one node, Such patterns are called keys, The sentence &quot;If a motorboat meets a sailingboat, then the motorboat must steer away from the sailingboatn will in our data base be represented like in figure 8.</Paragraph> <Paragraph position="1"> nIf a motorboat meets a sailingboat, then the motorboat mdst steer away from tke sailingboatn To the left is the pattern of three nodes corinected by sholit relations wikh DEF on both ends of the relations. This shows that they are part of a key. To the right is the deduced statement, connected to the center of the key with an IFTHm short relation. Note that there is a new quantifier THAT above. This quantifier means that we should single out just that actual object which was matched to that part of the key. We do not want to say that &quot;Every motorboat meeting a sailingboat must steer away from every sailingboat being met by a motorboatv, and therefore we must single out the matched object only. Natural language sentences often refer to previously mentioned entities with conetructa like &quot;hew or old mann. Our system w&ll first translate a sentence into an independent data base frwent. An assimilation program will then merge this fragment with the data base. Other systems often m&e %his assimilation during the input translation. They mould then avoid some, but not all, of our problems, but would get some other problems instead.</Paragraph> <Paragraph position="2"> For this merging we create temporary variables and keys during input translation. The sentence &quot;The man always with the gun is in the forestw is thus translated into figure 9: nThe man with the gun is in the forestr The merging program will use special deduction rules for these temporary variables, which we call DUMMIES. After merging, the D~~IES usually merge with some previous node in the data base, and they thus become CONSTANTS or VARIABLES depending on the type of that previous node.</Paragraph> </Section> <Section position="10" start_page="19" end_page="19" type="metho"> <SectionTitle> 9. Questions </SectionTitle> <Paragraph position="0"> Questions to a computer can require short or long answers.</Paragraph> <Paragraph position="1"> There are for example yes-no questions like rtIs a man with a balooh coming?&quot; which in our data base will be represented like in figure 10.</Paragraph> </Section> <Section position="11" start_page="19" end_page="19" type="metho"> <SectionTitle> BALO0N.P MAN-P PRED DEF </SectionTitle> <Paragraph position="0"> HIS a man wtth a baloon coming? n</Paragraph> </Section> <Section position="12" start_page="19" end_page="20" type="metho"> <SectionTitle> ITS WITH DEF </SectionTitle> <Paragraph position="0"> na bbaloom < COME-P where we put a question-mark on one-relation to show that the program shall try to &educe that relation from the prevjious howledge.</Paragraph> <Paragraph position="1"> Other questions requiw as answer a list of objects, for example &quot;Which of you can drive a carn or a description of a deduction chain ('%@yt1 or a description of an algorithm (tvhowu questions). We have-not yet tried to represent such questions in our data base structure.</Paragraph> <Paragraph position="2"> 10, Example of what our system can do This exmple shows a set of facts and questions, such that our system can answer the questions based on the facts.</Paragraph> <Paragraph position="3"> The input language to our system is not full natural english. the language is slightly simplified. me sentences in the example are mitten in this simplified englieh, A girl is a young woman, A boy is a young man, A woman is a human female. A man is a-human male. Every young man is a boy. Every sports cax is fast and expensive . Every fast car is dangerous. Anybody - with an expensive oar is rich, Every rich woman is frigh-bened by every poor nan, If a woman is meeting a man and she is fsightened by him but she is loved by him, then she will be despising him. If a man is loving a woman and she is despising him, then he is depressed. If a man is depressed and he is driving a car, then he is senseless and irrational. If a senseless man is drkving a dangerous car, then he is dangerous, and all traffic is in deadly peril.</Paragraph> <Paragraph position="4"> Everyone - on a public street is txaffic, Mazy is a mature girl, - with a sports tax, Eliza is a pretty girl - with long hair. The mature girl is ugly.</Paragraph> <Paragraph position="5"> I$ the ugly arl, rich? John is a man, He is young and poor. He is loving every fast car and every girl - with a fast car.</Paragraph> <Paragraph position="6"> 1s the pretty girl loved by John? Is the rich and ugly girl loved by; John? If Eliza had been a girl - nith a fast car, then would she be loved by John? Mary is meeting John and he is driving her car. Is the poor boy, dangerous? I I . The EQUAL relations The QUAL relation between ho singular elements means tlzt they me identical, However, sinoe we can put quantifiers on the E&UAL relation, we can alsa. use it for many set relationships, Some exmaples:</Paragraph> </Section> <Section position="13" start_page="20" end_page="20" type="metho"> <SectionTitle> ALL A QUAC ALL B means that the sets A and B me equal and </SectionTitle> <Paragraph position="0"> contain not more than one element each.</Paragraph> </Section> <Section position="14" start_page="20" end_page="25" type="metho"> <SectionTitle> ITS A EQUAL ALL B means that A is a subset of B. ITS A EQUAL ITS B means that A and B overlap. ALL A NOT EQUAL ALL B means that A and B a.re disjoint. StNE A EQUAL ALL A means that A is not empty. ALL A NOT EQUAL ALL A means that A is empty. SOME A EQUAL ALL A means that A is singular, that is contains </SectionTitle> <Paragraph position="0"> exactly one member.</Paragraph> <Paragraph position="1"> Natural language noun phrases are translated into nodes marked as singular in the data base, if: a) The noun phrase is not plural.</Paragraph> <Paragraph position="2"> b) The noun phrase is not translated into a VARIABLE in the data base.</Paragraph> <Paragraph position="3"> c) The noun phrase is interprekea in the special sense (like &quot;A man is walking on the streetu) and not in the general sense (like ~hrery man is a-male humanll).</Paragraph> <Paragraph position="4"> Data base nodes are marked as non-empty if they are of the type predicates (like &quot;the act of lightingT1 which we call LIGHT*P) . 12. Na%wal lan~uane noun phrases mere are many data base construata which correspond to natural language noun phrases. Noun phrases create many problems with their attributes, with composite objects which have several parts a.8.o. A number of chapters will discuss problem~l with representing that kind of facts. The necessary data base concepts are discussed, rather than the translation problems. Singulm noun phraaes without con Junctions are usually translated into one object-type node, An exception to this is some simple sentences in which noun-phrases are translated to predicate-type nodes, see chapter 21.</Paragraph> <Paragraph position="5"> This object-type node can be a CONSTUT, a DUMMY or a VARIABLE. CONSTANTS are created for simple positive sentences like I1A man is walking on a streetmtf Note however, that in an if-statement or a, question, the noun-phrases instead must be interpreted as VARIABGEs, e.g. &quot;If a man is walking on a street, then,. or IfIs a man wdking on a street?&quot;. In these cases, &quot;at mault1 does not introduce a new conrjtant , but represents a simple search pattern to be used in deduction, and VARIABLES are used for such search patterns in our data base system. For general-sense statements, the nonians are usually translated to VARIABLES. Example: Wvery good girl will kiss every brave soldier.&quot; These VARIABLES can be used in later deduction, to find out what happens if a good girl meets a brave soldier. Noun-phrases beginning with &quot;them or &quot;thid&quot; or &quot;thatf1 or some similar determiner are usually translated to DUMMIES. Here a search must be made in the data base for some previously known node to merge the DUMMY with, Pronouns like &quot;hen or l1itV or I1hertt are also translated into DUMMIES, for the same reason.</Paragraph> <Paragraph position="6"> The noun word i-bself indicates a property of that noun (e.g. ?tmmvf indicates the sex and species of &quot;a manvv). A predicate W*F is therefore created, and 'la mann gets a relation ?RED .t,o MAN*P Adjectives do nbt always indicate properties which are generally true for the noun phrase. They can mean many things Examples: The good teacher ex he teacher which is good as a teacher), The big ant ex he ant which is big for an ant), The red house ex he house which is red).</Paragraph> <Paragraph position="7"> Therefore, a weaker relation ATTR is used from a noun to its adjectives.</Paragraph> <Paragraph position="8"> Names are a very special kind of psedicates, ,and therefore a special relation NARlE goes from a noun node to its name. When a name such as rfJ~hnrl or tfCambridgev is used, we want to identify this with some previously know rfJohnv or lrCambridgeff Ln the data base, But we cannot give the node itself the name &quot;Johnn or trCa;mbridge&quot;, since there may be more than one &quot;Johnv and lfCambridge&quot; in the data base, Therefore, the last-mentioned which fits %he description is found, just as for other DUMMIES, &quot;The always Old Johnf1 will the~ef ore for example in our data base be translated into the DUMMY in figure 21.</Paragraph> <Paragraph position="9"> We have a special rule for VARIABLES with bnly one PRED as a definition, where this PRED goes to a predicate whose name comes from the input sentence. This variable gets the same name, but with lf*Sw in the end. Thus, &quot;Every manu is t~?anslated to W*S h-9 W*P This special rule is not really necessary, but has two advantages: a) The data base becomes more ~eadable, b) The data base routines will immediately see that all W*S nodes created by several different sentences can be merged into one, without having to do any deduction.</Paragraph> <Paragraph position="10"> You could sw that W*P is the property of being a man, while M.AN*S is the set of all men.</Paragraph> <Paragraph position="11"> 12b. Attributes on noun phrases This section describes things which are not yet implemented in the SQAP program when this is written (May 1974) For several reasons, attributes on a noun phrase cannot always be represented by a direct relation from the noun phrase to the attribute.</Paragraph> <Paragraph position="12"> Sometimes two or more attributes on a noun phrase are related. If you say PS.riend of Nixonn then this person is not always a friend (he may not be a friend of ~c~overn) and he is not always &quot;of Nixon&quot; (he may not be &quot;a son of N,ixonv although he is surely a son). If we represented the two attributes that he is a friend and that he is &quot;of Nixon&quot; as two separate independent relat.ions on his object node, then the data base deduction rules would not properly understand sentences like &quot;A friend of Nixon is an enemy of McGovernff. The deduction rules would wrongly deduce that since this object independently has the attribute of being a friend and the attribute of being &quot;of McGovernI1, the object is a friend of McGovern.</Paragraph> <Paragraph position="13"> To avoid this erroneous conclusion we must have only one single outgoing relation from the object to the composite property of being &quot;a friend of Nixon&quot;. The statement &quot;A friend of Nixon is an enemy of McGovernV might thus be represented like in fkgure I1 b</Paragraph> </Section> <Section position="15" start_page="25" end_page="25" type="metho"> <SectionTitle> FRIEND-R ENEMY-P </SectionTitle> <Paragraph position="0"> HA friend of Nixonu SUBSET ) enemy of McGovernr where two new ttevenktt nodes are introduced for being a friend of Nixon and being an enemy of McGovern. It seems as if only the preposition &quot;off1 and no other preposition in english creates this problem.</Paragraph> <Paragraph position="1"> The same klnd of representation can be used to correctly represent a statement like &quot;A big ant is a small animaltt, see figure llc.</Paragraph> </Section> <Section position="16" start_page="25" end_page="46" type="metho"> <SectionTitle> BIG-P ANT-P SMALL-P. ANIWI $ DEF $VBSP&quot;T </SectionTitle> <Paragraph position="0"> nA big antr > %A small anirndn</Paragraph> <Section position="1" start_page="25" end_page="46" type="sub_section"> <SectionTitle> Figure Ilc </SectionTitle> <Paragraph position="0"> Another reason why attributes cannot always be represented as direct relations on the noun is that the attribute may be restricted in time or space or in some other way. If we input the noun phrase &quot;A hungm girlu then the mmputer creates an object for this girl. But we may thereafter learn that the girl eats and is not hungry any more. Thus the same object, at a later time, does not any more have the attribute of being hungry. Here again, we must in-kroduce an EVENT pode for the fact that the girl is hungry as shown in figwre lld.</Paragraph> <Paragraph position="1"> In most. cases, the same time-restriction appliels to attributes as to the main verb in the sentence. If we say &quot;A hungq girl ate a cold buffet in a sundrenched meadow on a warm summer dayw then the time and space restrictians are valid for all the attributes on the vqrlous nouns in Che sentence. In such mes, the data base cou1.d be simplified if we introduced a special &quot;situationv node to represent the time and space, and then used a new shmt relation SIT from the various event nodes to this situation, This would be even moxe useful if a series of sentences all apply to the same situation.</Paragraph> <Paragraph position="2"> Prepositional attributes my also be situation restricted as for tHe sentence &quot;An angly man with a @;lln is coming at ten olclockvT, where the man at another time may not be &quot;with a -nu. This might be represented as shown in figure I le.</Paragraph> <Paragraph position="3"> If the computer is told that &quot;Every english spinster who comes into the church is awed1' then the computer can deduce that Eliza is awed if it knows that Eliza is english, is a spinster, and is cornin$ into the church, all at the same time. 13ut if Eliza was an english spinster five years ago, and comes into the church today, then we cannot make thls deduction. This can be solved in two ways. Elther the data base representation of &quot;Every english spinster who comes into the church is awed&quot; is changedinto &quot;If at a certain time, an english spinster comes into the church, then she is awed&quot; or else the deduction rules are changed so that the time-limitat:ions are implicitly carried along and combined during deduction.</Paragraph> <Paragraph position="4"> 13. Composite objects There is a need to describe the fact that objects can be parts of other 0bjec.t~. We ,therefore introduce the node type composite object. A composite object consists of a known or unhown number of elements, which may or may not be similar.</Paragraph> <Paragraph position="5"> If we know which the elements of a composite object are, then we use the ELENIENT short relation from the composite to one or more of its parts.</Paragraph> <Paragraph position="6"> Example: Vohn and Mary are married.&quot; would be translated inte figure 12.</Paragraph> <Paragraph position="7"> One might suggest that conjunctions between noun phr&ses are translated as two sepasate indenpendent object nodes, &quot;John and Mazy axe married&quot; w6uld thus be $ranslated in the same w~ a,s !'Johq is married and Mary is ma,rriedw . However, this is &v%ously not the same thing. Sometimes the difference is perhaps not there, fox example when we say &quot;John and Nary are humanv'. But the aafest way is to create a composite object.</Paragraph> <Paragraph position="8"> This of course requires deduction rules to decide when a property on a composite can be transferred to its elementary parts. TMs can almost always be done, but not in some oases, for example if we say ''John and Mary t-ogether are heavier than Peter.&quot; But in such cases hhere is usually some indication in natural language, like the word &quot;togetherv indicating that the property of the composite cannot be transferred to its elements, Look again at the picture above showing the translation of &quot;John and Mazy are married.&quot; ALL -the fiodes with DEF on %hem above me DUMMIES, This means that we first search for a previous-mentioned node with a flWAME: JOHN*Pw relation on it. If one is found, lr Johnf1 will merge with it, otherwise &quot;JohnI1 will become a new constant and the DEF is changed to ALL. The same thing is done for ItMaryl' Thereafter, when klJc)hnw and &quot;Mary1' have been found in the data base, we te to identify TfJohn and Maryff, that is to find in the aata base at node whose two elementary parts are just '1 Johnt1 and &quot;Maryw, If such a node is found, Vohn and Maryv will merge with it,, otherwise &quot;John and Maryq1 becomes a new constant and the DEF quantifiers are ohanged to ALL.</Paragraph> <Paragraph position="9"> This process ensures that if we first say IIJohn and Mary are rnazriedoFr and then say &quot;John and Mary are going to separate.lf then the two statements will refer to the same data base node &quot;John and Maryu for both sentences, There is a risk, however, if we say &quot;John and Mary- and their son me a and then say &quot;John and Mary. are going to London.&quot; Then the data base might wrongly identify llJohn and Maryw in the second sentence with the composite llJohn and Mary and *t;heiw sonv1 in the first sentence, and thus wrongly conclude that the son is coming along to London. To stop this, we might require that if there are ELEMENT relations on a node, these must point out all the elements, and not some of them. Thus the data base would how that llJohnqf and llMaryll axe the only elements of the composite &quot;John and Maryq1, and therefore cannot Identify this with l1Sohn and Mary and their sont1 .</Paragraph> <Paragraph position="10"> The data base also ought to have a deduction rule which automatically can conc'lude that theye is a PART relation between two composites, if all elements of the first composite are also elements of the second, 3 1 14.</Paragraph> <Paragraph position="11"> Conjunctions between noun phrases in the generdl sense In the previous chapter I pointed out the ambiguity between sentences l'ike &quot;John and Mary are married&quot; and &quot;John and Masy are humann where the first sentence says that the composite was nsrried, while the second said that the elements individually were humans. I also said that such sentences could always be translated to composites, since properties of composites can in general be trasferred by deduction to the elementaq parts.</Paragraph> <Paragraph position="12"> This is not so easy in the general sense, see the following examples : &quot;A11 men and women are getting married.&quot; &quot;All men and women, a.re happy.</Paragraph> <Paragraph position="13"> &quot;Every man and woman standing together are a maxried couple.&quot; llAll men and women are young people.&quot; Noun phrases in the general sense me translated into VARIABLES in the data base, and these VARIABIiES are later used during deduction. In most of the sentences above, the best translation is to create am individual variable for each element, but no variable for my composite. If we say &quot;All men and women are happyu what we mean is &quot;Create a VARIABLE containing all men, and another VARIABLE containing all women, and put the property of being happy on all members of both variables. &quot; We do certainly not mean &quot;Create a VARIABLE of man-human couples, and put the property of being happy on all such couple^.^^ In the general sense, conjuncted nouns me therefore not combined ihto comwsite objects. An exception is when there is some special indication that such a combination is wanted, like the word lltogetherlv in the third example sentence above. 15. Plural nouns Plural nouns do not simply indicate a set of singular objects, There aze also properties which belong to the composite of all the elements together. One of these is the property of be~ng~lurd, that is of having more than one element. If we say &quot;Two horses are running;&quot;, then each horse is not plural, neither is each horse two, it is the composite which has these two properties.</Paragraph> <Paragraph position="14"> Plural nouns must theyefore often be translated into composite objects in the data base, since relations on sets in our data base alway-s refer to the lndivjdual members of the set, not to the set as a whole.</Paragraph> <Paragraph position="15"> An excep-[;ion from this rule is phrases in the general sense 'like &quot;A11 men are male humanstt. Rere the plurality is of little importance, and no composite object is created at 51put translation.</Paragraph> <Paragraph position="16"> Therefore, when a plural refers to all objects with a certain property, then a variable is created, but when the plural noun phrase refers to some special collection of objects, then a composite object is created.</Paragraph> <Paragraph position="17"> We introduce the new relation NTlM which goes from a composite object to the numeral of it. We also introduce the relation COMPLW which goes from a composite object to a predicate which applies not necessarily to the composite, but which applies to all its elements. Examples in figure 13, This means that the data base must be able to make deductions on numbers, e,g, to deduct that if a composite has the relation NUM 2, then the relation NOT NUM 1 can be deduced. This is necessary e.g. to merge these sentences into the data base in a correct way: IVTwo horses ae coming. One of the _horses is sick.&quot; To identify Ifthe horsest1 Ln the second sentence with &quot;two horsesv in the first sentence, deduction must infer NOT NUM I from NTTM 2.</Paragraph> <Paragraph position="18"> There may also Be a need to have in the data base management a routine for counting the number of elementary parts of a composite, so that the NTTM numeral can be deduced if all parts are known.</Paragraph> <Paragraph position="19"> 16. Fitting composite objects into the sentence The gener@ rule is that when two conjuncted nouns have been translated into a composite object, then it is this composite objects and not its pwts which is fitted into the sentence framework.</Paragraph> <Paragraph position="20"> This is obviously the correct translation e,g, when you say &quot;The road between Stockholm and Gothenburg&quot; where Hbetweenqq refers to the composite, but not to the elementary parts singularly. he road between Stockholmn is not right).</Paragraph> <Paragraph position="21"> However, for a phrase like ttEvery man and woman in the cityt1 we do not want to find only couples of men and women, so general sense noun phrases are not translated into my composites at all, The pmts are fitted sepazately into the sentence framework instead, If we say !?The father and the mother of Mary is comingn, then obviously it is the composite which is llcomingtt, but it is not so obvious that &quot;offT refers to the composite. Sytppose that we previously in the data base have got a father of Mary and a mother of Mary, but no com~osite of these two, &quot;The fatherf1 and !'The mother&quot; are translated into two DUMMIES, and we f.irst search to identify these in the data base, before trying to identify the composite. But when we try to identify &quot;The father&quot; we do not want to find the closest previous-menthioned father, we wmt to find the closestprevious-menthioned father of Mary. Therefore, the rule for prepositions ia that to the left, they refer to the elementary parts but to the right they refer to the composite.</Paragraph> <Paragraph position="22"> Se example in figure A4, Example: &quot;The road and railway between Stockholm and Gothenburg As seen from the picture, between goes from the elementary parts Itthe roadtf and &quot;the railway&quot; to the composite object IIS t ockholm and Gothenburg&quot; .</Paragraph> <Paragraph position="23"> 17. Noun ,phYases with just a number and nothing more Some natural languages contains constructs where a noun phrase consists of only a number, usually followed by a preposition. Example &quot;One of the horsesf1 or &quot;Two of the horsesff, Rere, as usual &quot;one&quot; creates a singular set, while any number except 'bonen creates a composite object, The relation &quot;ofn is in this case translated into 19. Problems with the dual representa-tl,ioa of nouns As has been explained above, nouns must sometimes be translated to singular sets (for special sense singular nouns), to defined sets (for general sense nouns) or to composite objects (for most plural nouns). This duality 1s necessw, but it also will make deduction more difficult, since the deduction rules must be able to make inferences from: the composite to its parts, The deduction rules must also sometimes be able to create arudlliary composites or audlljlrggr non-composi tes.</Paragraph> <Paragraph position="24"> Example I: &quot;One man and three women are coming. How many men and how many women are coming?&quot; Here, deductian will probably have to create a help-composite for the single man, since ~nly composites have numeral on tnem, and the question asks for this numeral.</Paragraph> <Paragraph position="25"> Example 11: ?!One or &ore men is comingDV The natural translation of this is into a composite object with MTM to &quot;One or more1!. But if we later learn, or can deduce from the data base, that it is only a single man, thep a singular non-composite node probably has to be created.</Paragraph> <Paragraph position="26"> Example 111: &quot;Soldiers are cmel. This is because they are scared.&quot; In the first sentence, I1soldiersV1 is used in the general sense and thus a defined set is created, But in the second sentence, the translation will firgt traaslate &quot;theyll into a DUMMY looking for a composite object, When the routine for merging the second sentence into the data base finds the defined set for t!soldierslv, it must recognize that a DUMMY looking for a composite object can merge with a non-composite defined set in the data base.</Paragraph> <Paragraph position="27"> An even more oomplex problem for the deduction routines will occur if we say &quot;Two of the horses in the stable me sick. Is any horse in the stable sick?!! which will be translated like in figure 23.</Paragraph> <Paragraph position="28"> The question-answering routine will be asked to arnswer the question &quot;ITS EQUAL&quot; (that is: SUBSET) and to do this it must in some way recognize that a member of the defined set SICK*S is an element of the composite &quot;two of1?, and thus is sick. 20. Equality between composite oob jects The natural lanmage phrase &quot;The father and the mother is John and Mazy&quot; cannot be translated with an EQUAL relation between the two composites for &quot;the father and the motherw and 'I3oh.n and Mary&quot;. EQUAL says that all members of two sets are the same so such a translation would say that there is a composite object which has four elementary parts : &quot;the f atherlt , Vhe motherw, Johnft and tfM~tta Therefore a new relation SW is introduced into the data base. SM goes between two composite objects, or between a composite object and a non-composite object. SAME says that both object nodes represent the same reality, but viewed from different viewpoints, described by a different set of descriptions, &quot;John and Mazy are a masricd couplett will also be translated with a SAME relation between the two nodes. &quot;John and Mary&quot; is a compos5te object, and &quot;a married coupleu is a non-composite, and EQUAL would therefore mean that a node can be both composite and hon-composite at the same time, To avoid this confusion, the SAME. relation is used.</Paragraph> <Paragraph position="29"> SBME is thus used to indicate a relation between two different descriptions of the same reality. But SAMl3 cannot be used between a composite object and a defined set containing its elementary parts. ELEMENT might be used here, but ELEMENT refers to one of the parts, not to all the parts. Pherefore a new relation OBJGOMPLEX is used. OBJCOMPLEX refers from a composite object to a set of all its parts.</Paragraph> <Paragraph position="30"> &ample: &quot;TWQ girls are citizens of Norway&quot; would be translated like in figure 24, Second example: &quot;All people in the roam aro thd two girlstt is translated into figure 25; This means that some simple sentences can be translated as relations between predicates, For example, the sentence &quot;Every man is a burrianu can be translated like in figure 27.</Paragraph> <Paragraph position="31"> To be able to give this simple translation to adjectival predicates, we also have the short relation SUBATTR, so that &quot;Every man is a male humanTt can be translated to figure 29. !he difference between SUBAT'fR and SUBPRED is &quot;ce same as bebeen ATTXi and PRED in figure 11. In the above case, there is no semantic 22. Event nodes difference.</Paragraph> <Paragraph position="32"> Many natural language phrases combine several nodes (objects, defined sets, into a statement which can have limitations in space, in time, in its truth value, and which can have a cmse, a result etc.</Paragraph> <Paragraph position="33"> The centrd node in the translation of such phrases is the event node. Went nodes are used in our system not only for typical events like &quot;John went to Dhe cinema with Maryff but also for more sustained lfeventstl like tlJohn is the fiancee of Maryf1 or even &quot;John is the father of Marytf The most important relations on an event node are BY to the subject CASE to the predicate, OBJ to the object, and PART vaiidi ty to the- en~mmt . Example: &quot;John is riding the bikev is translated as in figure 30.</Paragraph> <Paragraph position="34"> ,John is riding the bike, From a valid &vent node (that is at the time and place of the event etc.) the deduction procedures can deduce e.g, a PRED relation from &quot;Johnft to 'RDE*~', and these deduced relations are very useful in later deduction. There is also a symmetric relation OBJPRED from the object to the predicate. If we can deduce that some object has OBJPRED to a predicate like m~*p, then we can deduce that that object is being ridden, that is that the predicate RDED+P (the passive of ~WP) is appliable to the object.</Paragraph> <Paragraph position="35"> We can therefore draw the following figure 31 of relations: ))John is riding the bike)) & pThe bike is ridden by Johnm including implicit short relations All of these relations do not have to be produced for every sentence, since some of them can be deduced from some others by chaining rules like :</Paragraph> <Paragraph position="37"> Several more triangles in the figure form such chaining rules, although not all of them. (~ven if John is riding and the bike is ridden, we cannot therefore conclude that John is riding just that bike). All the chawing rules involving the event node aze true only when that event node is true, or valid when the event node is valid, If the data base contains a verb both in zctive and passive form, then there must be a relation PASS between them to permit deduction. Since passive forms are less common than active, this PASS relatibn is generated whenever a passive verb appears.</Paragraph> </Section> </Section> <Section position="17" start_page="46" end_page="46" type="metho"> <SectionTitle> MAN-P DEPRESSED-P PASS </SectionTitle> <Paragraph position="0"> can of course easily be deduced.</Paragraph> <Paragraph position="1"> In the same way, &quot;The bike is ridden by John&quot; is translated like in figure 33,</Paragraph> </Section> <Section position="18" start_page="46" end_page="49" type="metho"> <SectionTitle> [T~F.~-P IC PASS </SectionTitle> <Paragraph position="0"> The CASE relation from the event to RIDW is not output explicitly, but can of course easily be deduced.</Paragraph> <Paragraph position="1"> One could argue that we could avoid passive verbs altogether in our data base by always using the CASE and OBJPRED relations. There are two arguments against thls: a) 1.t is valuable always to have the relatlon PRED from a noun to all properties on that noun, It is not systematic to need the relation OBJPRED to some properties, b) Our representation makes it very easy to represent statements like &quot;Someone who is killed, is deadu simply by KILLED*P SUBATTR DW*P which otherwise would have to be represented by a VARIABLE in the way in figure 34.</Paragraph> <Paragraph position="2"> One can see event nodes as a way of adding restrictions in time and space etc on PRED relations. The event nodes are necessary because our data base does not permit us to put short relations on short relations.</Paragraph> <Paragraph position="3"> Sometimes there, is a need to extend the SUBSET relation in the same way. PRED and SUBSET are very si..milar relations, although PRED goes to a predicate, SUBSET to an object set. Since SUBSM! is a special case of the EQUAL relation, it is really the EQUAL relation which we want to extend into an event. We therefore introduce a new relation OBJCASE so that y BY X & Y OBJCASE Z implies X EQUAL Z whenever the event Y</Paragraph> </Section> <Section position="19" start_page="49" end_page="60" type="metho"> <SectionTitle> EQUAL </SectionTitle> <Paragraph position="0"> The OBJCASE relation is used when natural language equality has +o be translated into relations between object nodes.</Paragraph> <Paragraph position="1"> AII example is given in figure 36.</Paragraph> <Paragraph position="2"> ))Every evening, John is a singer in the clubn From this network, we can deduce that if the event is valid, e.g. in the evening, then llJohnlf is a SUBSET of &quot;singer in the clubt1.</Paragraph> <Paragraph position="3"> Since all relations expand into a chaining rule with EQUAL: &quot;X R Y & &quot;Y EQUAL Z implies X R Z&quot; and since EQUAL can be expanded into an event node using BY and OBJCASE, this can be used to expand any short relation into an event node. For example, &quot;After 1972, Britain is a part of EECM requires us to expand the PART relation between Britaln and EEC into an event, to be able to add a time limitation to that PART relation. This can easzly be done by expanding EQUAL into BY x OBJCASE in the way in figure 37, x~fter 1972, Britain is a part of EECr 23b. Quantifiers on event nodes Consider the sentence ItA girl 1s givlng every man a flower&quot;. This sentence could be interpreted in the following way: &quot;There is a set of events, one for each man. One and the same girl is giving a different flower in each such event1'. In our data base, this is represented like this: In the tgmslation above, the two noun phrases &quot;a girl&quot; and &quot;a flower&quot; are interpreted in different ways. &quot;a girl&quot; is interpreted as one single girl, while &quot;a flower&quot; is interpreted as a set of diffeyenl flowers, one for each man.</Paragraph> <Paragraph position="4"> These two interpretations of &quot;au are called respectively the singular sense and the distributed sense. Other determiners than Itaft have the same ambiguity, for example &quot;someM. lfa carrf in the sentence &quot;Every man is in a car&quot; can be interpreted in the singular sense (one single car) or in the distributed sense (one car for each man).</Paragraph> <Paragraph position="5"> In the singuhr sense the interpretation will be: One can note that we can later refer back to the car only with the singular sense interpretation. Example: &quot;Every man is in a car. The car drove away1'. This also corresponds to the .interpretations in the figures above, where only the singular sense provides a node to refer back to.</Paragraph> <Paragraph position="6"> Such a back-referencing could thus be used to disambiguate this kind of ambiguous sentence.</Paragraph> <Paragraph position="7"> If you compare the two figures above, an important difference is -that -t;he event node is a constant in the singular sense, a variable in the distributed sense.</Paragraph> <Paragraph position="8"> The translation rule is that if all the noun phrases marked with &quot;a&quot; or &quot;some&quot; are to be interpreted in tke singular sense, then the event can become a constant. If, however, one of the noun phrases marked with Itat' or is to be interpreted in the distributed sense, then we must have one copy of the event node for each copy of the distributed noun, so the event must become a variable, If the event is translated as a variable, then the quantifiers on the relations between the event and the noun phrases should be: DEF-ALL to singular sense and constant nouns, DEF-ITS to distributed sense nouns, ITS-ALL to nouns marked with a general quantifier like lteverylf or &quot;allt1 or 'leach&quot;.</Paragraph> <Paragraph position="9"> 24. Deduction patterns and natural language if-clauses A natural language statement like &quot;If the weather is rainy and a person outdoors and the person is not wearing any raincoat, then the persoh will become wet.&quot; introduces deduction rules into the data base. These rules are only valid if a pattern of facts in the data base can fit into the pattern created by the deduction rule.</Paragraph> <Paragraph position="10"> Such deduction rule patterns are called keys in our system. After merging into the data base, the statement above may look like in figure 38.</Paragraph> <Paragraph position="11"> ))If the weather ts rainy and a person is outdoors and the person is not wearing any raincoat, then the person will become wet, Figure 38 A new quantifier &quot;THAT&quot; is introduced above. The reason for this is that if there axe two different persons, one who is outdoors, and another who is not wearing a raincoat, then we do not want to conclude than any of them necessarily will become wet. We therefore have to single out in the data base one person and two events in which this person is the subject. One of the events should say that he is outdoors, the other that he is not wearing a raincoat. We therefore have a key of one person and two events, which have to be fitted with facts in the data base when the deduction rule is used.</Paragraph> <Paragraph position="12"> The quantifier HTHATvl refers from a conclusion to the deduction pattern key. It means to single out that member of the referred set to which the whole pattern has been matched, In the figure above, lithe weather is rainyf1 is not part of the pattern, But in reality, there is in *he natural lavllguage text an implicit time and place indication: &quot;If, at a certain place, at a certain time.. .I1 and this place and time will fit the weather into the pattern key.</Paragraph> <Paragraph position="13"> Our program does not yet handle such implicit time and place indications.</Paragraph> <Paragraph position="14"> There are two short relations for &quot;imply&quot; in our data base: COND and IFTHEN, COND refers to necessazy conditions, IFTREN to sufficient conditions. To handle cause and effect patterns, and the resulting structure of situations depending on each other, probably more such relations are necessary, but we have not introduced them yet.</Paragraph> <Paragraph position="15"> A somewhat simpler notation is available for some simple cases. This is the COP short relation, which refers to a hypothetical copy. Thus, the bridge is loat. and weak, then it will breakvv can be translated like in figure 39, rlf the bridge is low and weak, then it will breakx Thus, if-statements in natural language introduce a pattern key of variables, connected with DEF-DEF relations. And the conclusion refers to this pattern with relations with the quantifier THAT on the pattern end.</Paragraph> <Paragraph position="16"> A nat~rd language if-statement in a question is translated in a quite different way. The statement &quot;If the weather is rainy and John is outdoors, will he then be wet?&quot; is translated like this: &quot;Add the temporary facts that the weather is rainy and that John is ouet;do-ors into the data base. Thereafter try to .deduce if he will be wet. When the question has been answered, then remove the tempwary facts from the data base again.</Paragraph> <Paragraph position="17"> CQ-ed to other natural language systems, one characteristic of om systiem~is +he representation of deduc%ion rules as variable patterns in the data base. Other often used representations me a) Predicate cslculus clauses.</Paragraph> <Paragraph position="18"> b) Exeautable programs in some spacial programming language. 'Phe advantage with our system is that the representation of deduction rules is so closely integrated with the represenhtfon of facts* The simplest deduction rules, the chafning rdle8, sihply are des for traversing the data base graph from node .t;o node. The more complex deduction rules are patteras very similar to the data base facts which these petterns are to match during dedGotion.</Paragraph> <Paragraph position="19"> If a predicate cslculus representation is used, then efficient deduction requires some algorithm for selecting .%hose clauses which might match the clauses in the deduction We. mas, tihe pattern matching problem isr not avoided, and an ef$f'cienf deduction algorithm probably will hwe to have an underlying netwoxk pattern similar to ours, although not so visible.</Paragraph> <Paragraph position="20"> be advantage with predicate calculus representation is however that the theory of deci&%ility is much fuller aeveloped fhr that representation than for ours.</Paragraph> <Paragraph position="21"> Ekecatable programs in some special programing la,nguage is potentidly a more powerful representation than ours.</Paragraph> <Paragraph position="22"> Heuristic des guiding the order of the dedkction search we easier to include into such a deduction rule, However, the power in an achl system is of course limited to the set of pro@ams which the input translator can generate.</Paragraph> <Paragraph position="23"> &my of the programs will probably in reality not contain anything else thsn our chaining rules, variables and patterns, and such system will also require some mere or less hidden underlying network to select rules and facts of interest dusing a certain deduction process.</Paragraph> <Paragraph position="24"> On the outemnost surface level, we have until now only iwglemented yes-no questions in; our system. Other kinds of qnes tiona u;m however appear as sub-ques tions during the deduction, proceso. A question is f n many ways similar to a natural lrtoguaere if-statement, Iri both cases, a pattern of variables is created, aad we want to identify this patte;m with the data base.</Paragraph> <Paragraph position="25"> A typical question like &quot;Is John father of a blond girl1! will thus be translated like in figure 40.</Paragraph> <Paragraph position="26"> nIs john father of a blond girl?)) In this simple case, there was no need to introduce pattern keys of more than one variable, so the .translation was very simple. One central relation, in this case the BY relation, is marked with a question-mark, which means that it is this relakion which deduction should try ijo; prove.</Paragraph> <Paragraph position="27"> The processing of a question therefore usually begins with the introduction of temporary data (in this case the VARIABLE for rlall blond girls1' and the VARIABLE for &quot;all fathers of blond girlsff) and then on a single quostion relation to prove. This is what our system is capablle of today. However, some complex questions will create patterns where part of the paktem refers to other patterns, just as fox if-statements in the previous section of this paper.</Paragraph> <Paragraph position="28"> Look for example at the question l11s the father of all the children of any of Johnt s daughters married to that daughter?f1 In the translation of this question there will be a VARIABLE for !'the fatherw and a VARIABLE for &quot;that daughterv. And these two VARIABLES must pairwise match, It is not enough to find that the father is married, not even enough to find that he is married to one of John's daughters, He must be married to just that daughter whose children are all also his children. The translation will therefore have to be something like in figure 41.</Paragraph> <Paragraph position="29"> ))Is the father of all the children of any of ~ohn's daughters married to that daughter? r Look at the OF relation from &quot;the fathern to &quot;all the childrenn. This OF relation should single out just the ahildren of his wife, not the children of all her sisters. We have not yet found out how to do this. We hope that this complex kind of questions will not be common.</Paragraph> <Paragraph position="30"> 26. DUMMIES = temporw variables fBr data base merging A shart presentation of the concept of a DU?@fY was made in ~ection 8. I)lDdIdI:ES and problems with them will be more fully treated here.</Paragraph> <Paragraph position="31"> When natural language uses constructs like &quot;HeVt or &quot;the manw or &quot;this object in the sk;yV thm this usually refers to something which the reciever is supposed to know already. Often, the thing referred to has been mentioned a short time ago in the previous natural 1 anguage input.</Paragraph> <Paragraph position="32"> We therefore introduce a special kind of VARIABLE. This is call-ed a DUMMY. An ordinary VARIABLE is kept in the data base to be used at some later time for deduc.tion. A DUMMY causes an immediate search in the data base for a matching previously known object.</Paragraph> </Section> <Section position="20" start_page="60" end_page="66" type="metho"> <SectionTitle> 6 I </SectionTitle> <Paragraph position="0"> The order of this search is important. If there are several previous objects matching the descriptions, the last-mentioned one shall usually be found. However, the subject usually goes before other noun phrases. If we say llIPS a card is below mother card, then it cannot be seen.&quot; then refers to the subject &quot;a cardw, not to the prepositional %nother card&quot; even though this was mentioned later.</Paragraph> <Paragraph position="1"> Our program will therefore make a list, the so-called CURRENT list, of previous-mentioned ob jeets. This is searched backwards. null We have at present two search routines for DUMMY matching, the &quot;theu routine and the tlthis&quot; routine. One difference between them is that if no matching node is found, the &quot;thist1 routine will ask the user to rephrase his statement. The &quot;thet1 routine will in that case accept that this is something which the user knows, but not the computer. It will therefore enter a new node if no previous-mentioned is found.</Paragraph> <Paragraph position="2"> ke pxoroblem whlch we 80 fsr liare not completely solved is how to do with patterns of DUMMIES. If we soy &quot;the behind Johnw then there sre two nstttrd way8 to translate this into oar b) A pattern key of tao mnfually depenqent DUBMtES, whexe qE3' BEElXD ALLw 5n the figure 42 is uhanged to &quot;DEF BEfflMD DEF&quot;, 'Fhe first translation +a neoe88~ in those oases where only one of the DUMKE hes a match in the data baae, e,g. for a atatement like &quot;Lf a man is late, then the man behind him is even later.&quot; Here, there is no previously known man, and the seoond translation wikh the pattern key would not match &quot;a manw in the if-ata%ement at all.</Paragraph> <Paragraph position="3"> Horevez, if solution a) is adopted, this text will not be treated correotly *A man dth a dog is ooming. Another do~is barking. The man with the dog is frightened.&quot; Solution a) will first find the other dog, and then create a new man who is with that other dog, and let that other man be frightened. A more complex algorithm may be necessary to solve this problem.</Paragraph> <Paragraph position="4"> Another example which will cause difficulty is &quot;John and his brother are in the wood, His brother is leaving.&quot; If no DUMMY pattern key is created, then &quot;his&quot; in the second sentence will identify with &quot;brother&quot; in the previous sentence. &quot;His brotherH in the second sentence will then identify with &quot;His brother's brothern in the first sentence, which is not correct.</Paragraph> <Paragraph position="5"> 27. DUMMIES which refer to VARIABLES Look at the natural language sentence &quot;If a lion meets an elephant, then the elephant will run to the forest.&quot; There are two DUMMIES in the main clause, &quot;the elephantv and &quot;the f orestft . &quot;The elepbmtv will match the VARIABLE created by &quot;an elephm&quot;ct in the if-clause. ?'The forestl1 will match a previously known, probably CONSTANT forest.</Paragraph> <Paragraph position="6"> In general, only after doing the refer-back search in the data base will we know whether a DUMMY will match a VARIABLE or a 'CONSTANT.</Paragraph> <Paragraph position="7"> If a DUMMX matches a VARIABLE, then that DUMMY may be adding definitions to that VARIABLE. Look for example at the sentence &quot;If a lion meets an elephant, -and if the lion sees the elephant, then. . . Here, the DUMMIES in the second phrase will add to the pattern key being built up, and thus add to the definitions of the VARIABLES Ifa liontr and &quot;an elephanttf, This means that there are two kinds of DEF-marked relations on DUMMIES, Phe first of them are those which axe to be used during the refer-back search. And the second are those which are to be added to the VARIABLE, if the DlJMMY matched a variable, In our system, we intend to distinguish between these by first giving the relations which are to be used in the refer-back search. Then the refer-back search is done, ad thereafter the relations are given which add DEF-s to the definition of the matched VARIABLE.</Paragraph> <Paragraph position="8"> Another interesting case is where there are two DUMMIES, one dependent on the other, and one of them matches a VARJXBLE. Look for example at- the sentence &quot;If a girl is in trouble, then her mother will be angryeff Here, &quot;heru becomes a,n indepen-dent DUMMY, while Ither mothern becomes a dependent DUMMY. The jtherff DDMEdY will match the VARIABLE &quot;a girlff in the ifclause. The DUMMY Ither motheru will not find any match at all. And the interesting thing is that because the independent Dm matched a VARIABLE, the dependent DUMMY Ifher motherv which matches nothing should in this case not crea%e a new CONSTANT but a new VARIABLE. For every different girl, there is a different mother who will be angry, so a CONS'PBNT will not do.</Paragraph> <Paragraph position="9"> This means that the &quot;the&quot; qwnmy algorithm must be able to decide if a CONSTANT or a VARIABLE is to be created when a DUMMY fihds no explicit match, 28, The problem of dual representation We have of course during the writing of the SQAP system encountered many problems, For some of them we have found solutions, for some not, Many of the problems have &ready been presented in this paper, and those problernswhich belong more to input &quot;canslation or -t;o deduction than to data base structure do not fit into the subject of this paper, Looking at the problems we have met, there seems to be one problem which recurs several times, Th,is is the fact that the same natural language construct can be represented in several ways in our data base, We have found that this is unavoidable, since one yepresentation is necessary in some cases and another in other cases. But on the other hand, this difference in representation will make the deduction difficult, including the deduction during the merging of new text into a prevfous data base, One solution to this problem is that when there is two different representations, then for sentences giving one of them, both of them is created includ'ing the relationship between them. This solution is used for the duality of the representation of nouns. The noun tfbooku corresponds in our data base both to the predioate BOOK*P (=the property of being a book) and to the defined set BOOK*S (=the set of all books). But whenever BOOK*S is crea-bed in input traslation, BOOK*P is also created and the relation BOOKW DEF PRED BOOK*P is created, (1f this already exists in the data base, then of course the same thing is not put there twice.) This means that whenever both BOOMP and BOOK*S occurs in our data base, the relation between them also exists.</Paragraph> <Paragraph position="10"> Another example where the same solution is used is active and passive verbs. Whenever a passive predicate, e. g. KILLEDXP is put into the data base, we also put in the active form KILL*P and the relation between them: KILL*P PASS KILLEID*P. In this war we ensure that if both KILL%P and KILLED*P are in our data base, then the relation PASS between them is also there.</Paragraph> <Paragraph position="11"> The same solution could be used, but would be cumbers~me and memoryconsuming in other cases. For example, a number of objects can be regarded both as a composite object and as a set, for which we have two different representation. There is a short relation, OBJCOMPLEX, in our system, from a composite object to a set of all its parts. But this relation cannot solve the whole problem, and it would also be very cumbersome always to have to put out both representations for certain phrases. This is discussed further in section 19 of this paper.</Paragraph> <Paragraph position="12"> Another problem of this kind is that our system is very much based on the idea that simple facts should be stored in a simple way ad more complex facts in ,a more complex way. &quot;A man is a maJ.en is therefore in our data base stored like in figure 43.</Paragraph> <Paragraph position="13"> In this case, a relation between the predicates was enough. But for the slightly more complex statement ffEvery human male is a manff, a defined set is necessary as in figure 44.</Paragraph> </Section> <Section position="21" start_page="66" end_page="69" type="metho"> <SectionTitle> HUMAN-P MALE-P </SectionTitle> <Paragraph position="0"> ))Every human male)) ,a> MAN-P ))Every human male 1s a man)) Figure 44 If there is some limitation in tru-bhfulness or validity, e.g. a time-limit, then the PRED must be expandad to REV BY times CASE, e.g. for the phrase tfEvery human male was that year a soldier&quot;, in figure 45.</Paragraph> <Paragraph position="1"> rEvery human male was. that year a soldier)) The difficulty with this is that when a new fact is going to be added to old facts, then the expanded version may be necessary. Also, a question may be asking for the expanded version, and the deduction routines may then have to do the expanding during deduction, which is surely possible; but difficult to manage in an efficient way, Example: IIEvery male is an animal. If he is humm, then he is also a man.&quot; Here, I1helI in the second sentence creates an object, the data base merging routine will find it difficult to understand that this refers to the nmalelT in $he previous sentence, since this Ivmalett was translated as a predicate, not as an object, 29. What our system can do and cannot do Our system can at least partly manage the following natural language constructs: Nouns, articles, quantifiers, adjectives, numerals, rnos-l; pronouns, the conjunction rqandI1 , passive and active verbs, objects, predicate complements, genitive, prepositional attributes and adverbials, if-clauses, yes-no questions.</Paragraph> <Paragraph position="2"> Some of the things we are not ready with yet are other conjunctions than &quot;andtf, relative pronouns, interrogative pronouns, negation, awcilliazy verbs other than llben, compazative adjectives.</Paragraph> <Paragraph position="3"> We do not yet try to resolve ambiguity by reference to the data base.</Paragraph> <Paragraph position="4"> The kind of facts which our system can handle are basically a passive description of a tme set of facts about the vorld. We can thus not yet handle properly a description of a sequence of events changing the world step by step. Neither can we handle properly facts which are part of someone's belief structure, Statements about statements cannot be handled (e.g. &quot;This is a diffioult problemf1 or &quot;This should not be construed to mean that...&quot;).</Paragraph> <Paragraph position="5"> 30. A short comparison with other systems Shapiro '1971, Simmons 1971 and others have presented systems very sim5.l~ to our, Most okher systems do not have quantifiers on the short relations as we have, and we feel that this is an addition which adda to the power of the rep~esentation. Special in our system may also be that one short relation om be extented when necessary into an event. This saves much memory compared to repmsentations where the fullest; form is always used, even though it is in most cases not needed. It is for example true that fox a statement like that in figure 3, there may be doube about only the BY relation, or only the AT-TJ3D3 relation, or only the CASE relation. (we may be sure that girl is happy&quot;, but not so sure about the day, or we rnay be sure that there is happiness today, but not sure where. ) A full representation would therefore require a place to insert doubt on my short relation, whether there is doubt or not, and this would double the data base size.</Paragraph> <Paragraph position="6"> In our system, the deduction rule can for any node in the data base find all outgoing and incoming short relations directly, and follow them. In-spi.t;e of this, we can store a whole short relation in just 64 bits(t-o 24-bit adresses plus 16 additional bits). 'Phis compact representation increases the efficiency of systems storing the data base in virtual memories.</Paragraph> <Paragraph position="7"> The basic ideas for our sptem were initially conceived by Erik Sandewall and were presented in his papers in the bibliography.</Paragraph> <Paragraph position="8"> Our system was developed as a team-work between me, Erik Sandewall and Kalle Makkil'd. I have been working with input translation, Kdle Makila with data base managment and deduction, and Erik Sandewall has guided us in our work. It is difficult to pinpoint who solved each of our problems, shce they were solved through discussions from which a solution sooner or later emerged.</Paragraph> <Paragraph position="9"> Siv Sjijgren has been working with the problem of adapting our system to the swedish and espermto languages,</Paragraph> </Section> class="xml-element"></Paper>