From: AAAI-86 Proceedings. Copyright 1986, AAAI ( All rights reserved.

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1 From: AAAI-86 Proceedngs. Copyrght 1986, AAAI ( All rghts reserved. INFERENCE IN A TOPICALLY ORGANIZED SEMANTIC NET Johannes de Haan and Lenhart K. Schubert Department of Computng Scence, Unversty of Alberta Edmonton, Alberta, Canada T6G 2Hl ABSTRACT A semantc net system n whch knowled; ge s topcally organzed around concepts has been under development at the Unversty of Alberta for some tme. The system s capable of automatc topcal classfcaton of modal logc nput sentences, concept and topc orented retreval, and property nhertance of a general sort. Ths paper presents an nference method whch effcently determnes yes or no answers to relatvely smple questons about knowledge n the net. It s a deductve, resoluton based method, enhanced by a set of specal nference methods, and reles on the classfcaton and retreval mechansms of the net to mantan ts effectveness, unencumbered by the volume or dversty of knowledge n the net. I INTRODUCTION In 1975, Scott Fahlman and Drew McDermott dscussed the so-called symbol-mappng problem (Fahlman 1975, McDermott 1975). In essence, ths s the problem of makng smple nferences quckly n a system wth a potentally very large, vared knowledge base. Among the examples they dscussed were the nference that Clyde s grey, gven that Clyde s an elephant and elephants are grey, and the nference that Clyde does not lve n a teacup or play the pano, gven standard knowledge about elephants, teacups, and panos. What makes the problem hard s not the complexty of the requste knowledge or reasonng, whch are qute modest, but the fact that the rght knowledge may be very hard to fnd: we have a needle-n-ahaystack problem. More than a decade later, the problem cannot be consdered satsfactorly solved. Progress has been made n customzed understandng and reasonng systems - systems that can make a wde range of nferences n a crcumscrbed doman (e.g., the dvorce story doman of BORIS, descrbed n Lehnert et al. (1983), or the varous domans of expertse of expert systems n medcne, computer confguraton, prospectng, and so on). However, the problem of scalng up to systems wth a large amount of knowledge about a wde varety of subjects s stll very much wth us. The work reported here s part of a contnung effort to develop a queston-answerng and Englsh conversatonal system (ECOSYSTEM) w h* c h s unencumbered by the volume or dversty of ts knowledge. Our approach to effcent questonanswerng for large knowledge bases was frst sketched n Schubert et al. (1979). Ths sketch motvated the desgn of a 3-level semantc net organzaton: at the hghest level, knowledge resdes n a man net (for real world knowledge) and n arbtrarly nested subnets (prmarly for mental worlds and narratve worlds ); the next level s the level of concepts, whch are used n each subnet as access ponts for knowledge drectly nvolvng them; and the thrd level s the level of topcs, whch L:,,,,,L:,.,ll..,..l.A:..:.J, CL, 1,-a...l-A--..l-.-..c- -: L IIIGL (II LlllL(LllJ JU UI lut; bllt: KU wltxlgc: ca Llb a I; tm 1; 1 1cep IJ nto topcally related subsets of facts (Covng ton & Schu bert, 1980). Ths organzaton allows hghly selectve retreval of knowledge relevant to a query. For example, the queston Is the wolf n the story of Lttle Red Rdng Hood grey? (posed n logcal form) would prompt access of the Lttle Red Rdng Hood subnet, followed by access wthn that subnet of the node for the wolf, followed by access of colourng nformaton about the wolf and ts superordnate concepts. (Addtonal nformaton may be accessed durng the nference attempt - see Secton III.) The mplementaton provdes for nput of modal logc sentences whch are automatcally converted to clause form, topcally classfed, and nserted at approprate concept nodes. In addton, specal nference methods have been developed to short-cut taxonomc reasonng and reasonng about colours and tme. The new deductve algorthm bulds on ths work; t effcently determnes yes or no answers to the sorts of questons dscussed by Fahlman and McDermott, relyng upon the classfcaton and retreval mechansms of the knowledge base. In some respects, the method has goals that are smlar to those of automatc theorem provers. However, the domans of natural language understandng and theorem-provng are dfferent, and two fundamental dfferences dstngush ths method from a theorem-prover: Sze of the knowledge base. Theorem provers work on problems n well defned logcal or mathematcal domans. These systems are artfcal and are usually axomatzed by some small set of statements. Ths s not true of the semantc net, whch wll ncorporate a very large body of knowledge, suffcent at least to carry on an ntellgent conversaton. Deductve ablty. Theorem provers are judged manly by ther deductve ablty - better provers solve logcally more complex problems. People requre mnutes or even hours to solve these problems, and yet, they are able to perform the nference needed for natural language understandng almost mmedately. It s not unreasonable to suppose that natural language nference s shallower (.e., requres fewer steps) than that requred for mathematcal theorem provng. II THE SEMANTIC NET To understand how the nference method works, t s mportant to understand how the semantc net represents and organzes knowledge. The representatonal scheme s essentally that as descrbed n Schubert (1976), ncorporatng changes descrbed n Schubert et al. (1979) and Covngton (1980). The syntax of the net provdes for the representaton of formulae n hgher-order modal logc,* wth constants, functons, exstentally and unversally quantfed varables, and the usual truth functon connectves (negaton, mplcaton, dsjuncton and conjuncton). * Although the net s able to represent and organze modal propostons, the current nference method s restrcted to the frst order predcate calculus. 334 / SCIENCE

2 specalzaton generalzaton part appearance odour form colourng translucency texture generalzaton: [ WOLF1 WOLF] I WOLF1 --I feedng: [ WOLF1 EAT GRANDMA] [ WOLF1 EAT LRRH] behavor tactlequalty - texture hardness reslence communcaton: [ WOLF1 TALK- WITH LRRH] mental- qualty -l emotonal-dsposton ntellectual-dsposton generalzaton: ~[z WOLF] V [zz MAMMAL] behavor communcaton WOLF- specalzaton: [ WOLF1 WOLF] colourng: -I[Z WOLF] V [z GREY] appearance shape:... functon (use) Fgure 2. Topc access skeletons (TASS) knshp control membershp ownershp Fgure 1. Topc herarchy (TH) Wthn the man net and each subnet, a dctonary provdes drect (hashed) access to named concepts. Facts about these concepts are then organzed usng a topcal herarchy (TH). Usng the TH, t becomes possble to drectly access clauses whch topcally pertan to a concept and to gnore all the rest (whch can potentally be a large number of clauses). The structure of the TH also defnes relatonshps among topcs, so t s possble to broaden an access to sub-topcs or super-topcs of a gven topc. Fgure 1 llustrates a smplfed topcal herarchy for concepts whch are physcal objects. It s not lkely that we know somethng about all topcs for all concepts, and to duplcate the entre TH for every concept would waste storage space and ncrease traversal tme across empty topcs. To solve ths problem, topc access skeletons (TAS) are used. Each TAS s a mnmal herarchy based on the complete TH, and only ncludes topcs about whch there s some knowledge, or topcs whch are needed to preserve the structure of the herarchy. Fgure 2 llustrates smple TAS s for the specfc concept WOLF1 and for the generc concept WOLF. Usng predefned topcal ndcators for predcates, a classfcaton algorthm automatcally assgns to each asserted clause partcular (concept, topc) pars. For example, the predcate EAT ndcates the topc feedng wth respect to ts frst argument and the topc consumpton wth respect to ts second argument. Hence, the clause [ WOLF1 EAT GRANDMA]* s assgned the pars ( WOLF1 feedng) and (GRANDMA, consumpton), and s ndexed accordngly n the TAS s of the concepts WOLF1 and GRANDMA. Smlarly, the predcate GREY ndcates the topc colourng, so the clause ~[z WOLF] v [z GREY] s assgned the par ( WOLF, colourng), and the clause s ndexed under the colourng topc n the TAS for the concept WOLF. Subsequent queres about the colourng of wolves would be able to drectly access ths clause. Queres about the appearance of wolves would also be able to quckly get to the clause, because colourng s a sub-topc of appearance. The specal topc major mplcaton s used to classfy fundamental propertes of predcates that characterze ther meanng.** For example, a major mplcaton of the predcate IN mght be that f A s n B, then A s smaller that B, and that B s a contaner or enclosure of some sort. Smlarly, an excluson topc s used to classfy clauses whch explctly defne such a relatonshp (e.g., + CREATURE] v + PLANT]). For the man net and all of ts subnets, there s also a herarchcal organzaton of the concepts wthn each net. Concepts are organzed usng a structure called a concept herarchy (CH), whch s essentally a type herarchy for physcal objects. It s used n two ways: (1) for quck assocatve access to groups of concepts wth the same type (just as the TH provdes quck access to clauses about the same topc) ; and (2) to gude a property nhertance mechansm n ts search for generalzatons or specalzatons of a gven concept. Fgure 3 presents a smplfed CH for physcal objects. To repeat the CH for every subnet s also waste of resources, so a concept access skeleton (CAS) s mantaned for each sub- * To mprove readablty, the syntax places the frst argument of a lteral before the predcate. ** Major mplcatons were lngustcally motvated n Schubert et al. (1979). They are related to Schank s ACT-based nferences, as well as to termnologcal facts n systems lke KRYPTON and KL-ONE. KNOWLEDGE REPRESENTATION! 335

3 We wll elaborate on the last pont frst, and then dscuss (1) and ( 2) under the headng Resoluton control. A. Generalzed resoluton and evaluaton thng - thng lvngthng - person creature anmal -I rock nanmate mountan natural lake object.. artfact clothng buldng. I * Fgure 3. Concept herarchy (CH) grl: [LRRH GIRL] wolf: [ WOLF1 WOLF] cottage: hood: 1 [c COTTAGE] [d HOOD] Fgure 4. Concept access skeleton (CAS) woman man mcrobe bug largeranmal net. Just as a TAS s a mnmal herarchy based on the TH, a CAS s a mnmal herarchy based on the CH. Fgure 4 presents a CAS whch assocatvely organzes some of the constant concepts n the story of Lttle Red Rdng Hood* (LRRH). The classfcaton algorthm automatcally places clauses whch are assgned a (concept, generalzaton) par nto a CAS whenever concept s an nstance. III THE INFERENCE METHOD As mentoned above, we were led to adopt clause form as the most convenent logcal form for the purposes of automatc topcal classfcaton. It was therefore natural to choose resoluton as our basc nference method. Our deductve algorthm employs certan famlar strateges, such as set-of-support and preference for smple resolvents. What makes the algorthm unque, however, are the followng three features. (1) The set of potental nference steps consdered at any one tme s severely lmted by use of both the concept herarchy and the topc herarchy (as reflected n each concept s TAS); (2) there s an ongong decson-makng process whch trades off nference steps aganst retreval steps; and (3) resoluton s generalzed, permttng use of specal nference methods for specfc domans. * Most examples n ths paper are loosely based on ths story. Resolvng two clauses s usually done by resolvng on lterals from each clause whch have the same predcate but opposng sgns. For example, [.LRRH GIRL] resolves aganst l[lrrh GIRL]. G ven the exstence of a specal nference method whch quckly nfers relatonshps among type predcates, t s also possble to reduce long nference chans to a sngle resoluton step. For example, (LRRH GIRL] drectly resolves aganst ~LRRH CREATURE], wthout USng the ntermedary clauses ~[z GIRL] V [z CHILD]... ~[z PERSON] V [z CREATURE]. Or, gven a specal nference method for colour, t becomes possble to drectly resolve [ WOLF1 BROWN] aganst [ WOLF1 GREY] (Papalaskars & Schubert 1982, Schubert et al. 1983, Brachman et al. 1983, Stckel 1983, Vlan 1985). Ths method of generalzed resolvng can smlarly be used for factorng and subsumpton (Stckel 1985, Schubert et al. 1986). A crucal advantage of our topcal retreval mechansm s that t allows canddates for generalzed resolvng to be found as effcently as canddates for ordnary resolvng, on the bass of ther classfcaton under the same topc (e.g., [ WOLF1 GREY] and [ WOLF1 BROWN] are both classfed as colourng propostons for WOLFl). Evaluaton s another means by whch the resoluton process can be consderably shortened. Every clause from the orgnal queston or generated durng the proof goes through an evaluatve attempt, to try to acheve an mmedate proof (f the clause s false), or to remove the clause from consderaton (f t s true). If the clause cannot be evaluated, each of ts lterals s tred, to try to prove the whole clause true (f the lteral s true), or to remove t from the clause (f t s false). The smplest evaluatve method s to match a clause or lteral aganst prevously asserted clauses (the normal form ensures that the nput form of a formulae has no affect on the matchng process). Generalzed resoluton and evaluaton can be used for any class of predcates for whch there exsts a specal nference method. Currently, the nference algorthm uses specal nference methods for types and colours; however, methods for tme and part-of relatonshps have been developed as well (Schubert 1979, Papalaskars & Schubert 1982, Schubert et al. 1983, Schubert et al. 1986, Taugher 1983). B. Resoluton control A great many resoluton control strateges have appeared n the lterature, but none of them has been completely successful n contanng the usual combnatoral exploson of generated clauses and the ensung dffculty n fndng the rght ones to resolve wth. Nevertheless, resoluton proved to be well-suted to our purposes, for two reasons: (1) Proofs requred for natural language understandng and ordnary queston-answerng are generally short (at least when specal shortcut methods are avalable) ; and (2) Th e needle-n-a-haystack problem s solved n our system by the access organzaton we have descrbed. The examnaton of most resoluton proofs that have gone astray soon reveals a large number of resolutons where the unfcaton process made some substtuton that dd not seem to be semantcally vald. For example, t s syntactcally possble to resolve jz WOLF] v [Z GREY] aganst l[lrrh GREY]. However, we ntutvely realze that the frst clause (knowledge about wolves) can not really be appled to LRRH, and that ths resoluton s frutless. To express t another way: the unversal varable z s typed to represent WOLF, and should not by substtuted by a concept whch s of type GIRL. 336 / SCIENCE

4 Brefly, our algorthm avods frutless nferences by restrctng ts search for potental resolvng canddates aganst a gven clauses to clauses connected to t by a path (of length 0 or more) n the concept herarchy, and classfed under the same topc. The confnement of resoluton to paths n the concept herarchy s comparable to approaches based on sortal logcs (e.g., McSkmn & Mnker 1979, Walther 1983). However, our method does not requre explct typng of predcates and sort checks durng unfcaton. The topcal confnement of resoluton readly pcks out clause pars contanng resolvable lterals, ether n the ordnary sense or n the generalzed sense. The nference algorthm mantans an agenda of potental actons. Each acton s relevant to a sngle clause, and s ether a possble resoluton that can be performed wth that clause, or a retreval acton whch mght lead to possble resoluton actons. Retreval actons are based drectly on the classfcaton of a clause, and are specfc to the same knd of (concept, topc) pars that the classfcaton procedure derves for asserted clauses. Sx knds of retreval actons can appear on the agenda: (1) (2) (3) (4) (5) (6) clause f-+ (cone, topc, super) s the notaton for the acton whch would retreve all clauses stored at concept cone and ts superconcepts, under topc topc, and form all potental resolutons between clause and the retreved clauses, placng them on the agenda. clause t+ (cone, topc, sub) s smlar to (l), but uses the subconcepts of cone only, excludng nstances. clause +-+ (cone, topc, nst) s also smlar to (l), but uses nstances of cone only. clause et (cone, major-mp) denotes the acton whch would retreve all major-mplcatons of concept cone, and form all potental resolutons between clause and the retreved clauses. clause et (cone, ezcl) s smlar to (4), but uses excluson propostons of concept cont. clause +-+ (cone, nst) denotes the acton whch would retreve all clauses specfyng nstances of cone and form potental resolutons between clause and these clauses. The CH s used to quckly determne the super or subconcepts of a gven concept, and the CAS s used to quckly fnd nstances of a gven concept. The agenda s ordered by the estmated cost of the actons, and the nference method always chooses to do the acton wth the least cost frst. The cost of a possble resoluton s hgh to the extent that the resolvent s expected to be complex (effectvely mplementng a least complex resolvent preference strategy). The cost of a possble retreval, clause +-+ (cone,...). s hgh to the extent that clause s complex, and the expected number of clauses to be retreved s hgh. Each clause c whch s to be consdered for possble ton s loaded nto the network, as follows: 1. Smplfy ( evaluate) c, f possble. s false, report a dsproof. 2. Classfy c for nserton and, f not as f t were an asserted clause). refuta- If c s true, dscard t; f c yet present, 3. If c was classfed twce w.r.t the same (cone to factor t; f successful, load the factor(s). 4. Generate the followng possble ng them on the agenda: retrevals nsert t (.e.,, topc) par, try relevant to c, plac- (a) f c was classfed under ( cone, topc) generate the retreval c -+ (cone, topc, super) > and, f coflc s not a constant, P4 (4 the addtonal retrevals c tt (cone, topc, sub) and c t) (cone, top, nst). f c contans a postve predcate P then generate the retrevals c t) (P, major-mp) and c +-+ (P, ezcl). f c contans a type predcate P wth varable argument and c was not classfed under any (cone, topc) par, generate c t+ (P, nst). The complete nference algorthm can now be descrbed: 1. Load the clauses to be refuted. Ths mght yeld an mmedate dsproof, but more lkely t wll put a set of potental retrevals on the agenda. 2. Carry out the potental acton wth the least cost. If t was a retreval, ths results n a set of potental resolutons beng placed on the agenda. If the acton was a resoluton wth resolvent c, then load c. If c evaluates to false when t s loaded, or f c s the null clause, then report a dsproof. 3. If some predefned resource lmt has been return unknown, else repeat from Step 2. exceeded, then The method also concurrently searches for a proof, usng the dual of the orgnal queston. Note that a set-of-support strategy s used, as only clauses from the orgnal set to be refuted, or one of ther descendants, s ever consdered for a resoluton acton, IV EXAMPLES 1. To answer the queston Is there a creature (n the story of LRRH)?, the clause to be refuted for a yes answer s ~[z CREATURE] (called c for brevty). Loadng ths clause generates the retreval c t--t (CREATURE, nst). Usng the CAS, the clauses [LRRH GIRL], [ WOLF1 WOLF],... are retreved, any of whch gves an mmedate null resolvent by generalzed resoluton. 2. To answer the queston Is the Wolf grey?, the clause to be refuted for a yes answer s l[ WOLF1 GREY] (=c). Loadng ths clause generates the retreval c t--t ( WOLF1, colourng, super). Usng the CH to get from WOLF1 to the generc WOLF concept, the clause ~[z WOLF] V [Z GREY] s retreved. Resolvng aganst the orgnal clause yelds -[ WOLF1 WOLF], whch mmedately evaluates to false when t s loaded. 3. To answer the queston Are all creatures pnk? the clause to be refuted for a no answer s ~[z CREATURE] V ~;,~y (= 4 * Loadng ths clause generates the ret) (CREATURE, colourng, super) and c f--t (CREATURE, colourng, sub). The sub retreval yelds --I[z WOLF] v [z GRE Y] (=c ). Usng generalzed resoluton (for colours) yelds ~[z CREATURE] v ~[z WOLF]. Generalzed factorng on ths clause yelds l[z WOLF] (= c ). A retreval for ths clause s c ++ ( WOLF, nst). The CAS s used to fnd the clause [ WOLF1 WOLF], whch resolves aganst c to complete the dsproof. 4. To answer the queston Does the Wolf lve n LRRH s basket? (cf. Fahlman s Clyde-n-the-teacup problem), the clause to be refuted s [ WOLF1 LIVE-IN B.4SKETl] (=c). One of the retrevals generated for ths clause s c t+ (LIVE-IN, major-mp). A major mplcaton of LIVE-IN ndcates that the WOLF1 would have to be smaller than BASKET1 to lve n t.* Further nference s then needed to Alternatvely, a major mplcaton of LIVE-IN could merely ndcate that f z lves n y, then z s n y (at some tme). The knowledge that z s smaller than y would then be retreved from a major mplcaton for IN. KNOWLEDGE REPRESENTATION 55-

5 establsh that the WOLF1 s not smaller than the basket, and therefore cannot lve n t. The best way of establshng the relatve szes of WOLF1 and BASKET1 would be va a specal nference method for relatonshps among physcal objects, but the current mplementaton does not nclude such a method and nstead resorts to explctly assertng these relatonshps n the knowledge base. The above questons were chosen to llustrate techncal ponts. More natural questons are also easly handled, such as Dd an anmal eat someone?,, and Is there anythng n LRRH s basket that she lkes to eat?. V DISCUSSION AND FUTURE WORK An mplementaton of the nference method, wrtten n Berkeley PASCAL, was able to answer a test set of 40 questons n about 15 seconds CPU tme on a VAX 11/780, usng a knowledge base of over 200 clauses (general and specfc knowledge about the story of LRRH). Doublng the sze of the knowledge base had no effect on the queston-answerng tme. The successful mplementaton of the method vndcates the net organzaton developed earler, showng that t provdes quck selectve access to the knowledge needed for smple questonanswerng. Furthermore, the organzatonal structure proved useful n gudng and constranng deducton steps. Proofs are confned to vertcal paths through the concept herarchy, and are topcally focused, and as a result are very drect, avodng meanngless,, deductons, regardless of the amount of knowledge stored. Thus, we have made sgnfcant progress towards solvng the symbol-mappng problem. Recent knowledge representaton systems somewhat smlar n am to ours nclude KRYPTON (Brachman et al., 1983)) KL- TWO (Vlan, 1985) and HORNE (Allen et al., 1984). Lke ECOSYSTEM, these systems are ntended to provde a domanndependent logcal representaton, and general and specal nference methods (such as taxonomc methods) applcable to a varety of domans. However, ECOSYSTEM s concept-centred, topcally focused retreval mechansm, and ts use n gudng deducton, appear to be unque. Further, rather than provdng alternatve nference tools, such as forward and backward channg, we have tred to provde a sngle, effcent algorthm for deductve queston-answerng. Also, our overall phlosophy has been to provde a perfectly general representaton and nference mechansm, whch we then seek to accelerate by specal methods, as opposed to provdng an ntally restrctve.representaton and nference mechansm, to be subsequently extended by specal nference methods. Numerous extensons to ECOSYTEM are planned. These nclude extensons to handle temporal nformaton (the temporal system s nearly operatonal), equalty, arthmetc, sets, modaltes (ncludng causaton), genercs (usng the approach of Pelleter & Schubert, 1984)) and specal methods for nave physcs. Work on wh-queston-answerng and on the natural language front end are also under way (Schubert 1984, Schubert & Watanabe 1986). REFERENCES Allen, J. F., Gulano, M., and Frsch A. M. (1984). The HORNE Reasonng System, TR 126, Computer Scence Department, Unversty of Rochester, Rochester NY. Brachman, R. J., Fkes, R. E., and Levesque, H. J. (1983). Krypton: a functonal approach to knowledge representaton, Computer 16, Covngton, A. R. (1980). Organzaton and Representaton of Knowledge, M.Sc. Thess, Unversty of Alberta. Covngton, A. R., and Schubert, L. K. (1980). Organzaton of modally embedded propostons and of dependent concepts, Proc. of the 3rd Natonal Conference of the CSCSI/SCEIO, Vctora, BC, May, Fahlman, S. (1975). A System for Representng and Usng Real World Knowledge, AI Lab Memo 331, MIT, Cambrdge, Massachusetts. Lehnert, W. G, Dyer, M. G., Johnson, P. N., Yang, C. J., and Harley, S. (1983). BORIS - an experment n n-depth understandng of narratves, Artfcal Intellgence 20, 15-62, MIT press. McDermott, D. (1975). Symbol-mappng: a techncal problem n PLANNER-lke systems, SIGART Newsletter 51, (Aprl), p.4. McSkmn, J. R., and Mnker, J. (1979). A predcate calculus based semantc network for deductve searchng, n Assocatve Networks, N. V. Fndler (ed.), Academc Press, Papalaskars, M. A. (1982). Specal Purpose Inference Methods M.Sc. Thess, Unversty of Alberta. Papalaskars, M. A. and Schubert, L. K. (1982). Inference, ncompatble predcates and colours. Proc. of the 4th Natonal Conference of the CSCSI/SCEIO, Saskatoon, Sask., Pelleter F. J., and Schubert L. K. (1984). Two theorems for computng the logcal form of mass expressons, Proc. COLING-84, July, Stanford, Calforna, Schubert, L. K. (1976). Extendng the expressve power of semantc networks, Artfcal Intellgence 7, Schubert, L. K. (1979). Problems wth parts. IJCAI-79, Tokyo, Japan, August, Schubert, L. K., Goebel, R. G., and Cercone, N. J., (1979). The structure and organzaton of a semantc net for comprehenson and nference, n Assocatve Networks, N. V. Fndler (ed.), Academc Press, Schubert, L. K., Papalaskars, M. A., and Taugher, J. (1983). Determnng type, part, color, and tme relatonshps, Computer (USA) 16, 10, Schubert L. K. (1984). On parsng preferences, Proc. COLING- 84, July, Stanford, Calforna, Schubert L. K., and Watanabe, L. (1986). What s n an answer: a theoretcal perspectve on deductve queston-answerng, Proc. of the 6th Canadan Conf. on AI (AI-86)) May, Montreal, Canada, Schubert, L. K., Papalaskars, M. A., and Taugher, J. ( to appear). Acceleratng deductve nference: specal methods for taxonomes, colours and tmes, n Knowledge Representaton, Cercone, N., and McCalla, G. (eds.), Sprnger- Verlag, New York. Stckel, M. E. (1983). Th eor y resoluton: buldng n nonequatonal theores, Proc. AAAI-83, Washngton, D.C., August, Stckel, M. E. (1985). A u t omated deducton by theory resoluton, Proc. IJCAI-85, Los Angeles, Calforna, August, Taugher, J. E. (1983). An Ef cent Representaton for Tme Informaton, M.Sc. Thess, Unversty of Alberta. Vlan, M. (1985). The restrcted language archtecture of a hybrd representaton system, Proc. IJCAI-85, Los Angeles, Calforna, August, Walther, C. (1983). A many-sorted calculus based on resoluton and paramodulaton, Proc. IJCAI-89, Karlsruhe, West Germany, August, / SCIENCE