A knowledge base system is  a database system with logical,  temporal and topological structures together with operations on these structures. vVe provide the necessary mathematical concepts for modeling such a system. These are parametrized hierarchical relations, logic functions, hierarchies of variables with their hierarchical control operators, and neighborhood/similarity structures.  These concepts are then applied to define a model of a knowledge module. By composition of knowledge modules we obtain the knowledge system model

Albrecht, R. F.

Németh, G.

English

Periodica Polytechnica (Budapest University of Technology and Economics)

PERIODICA POLYTECHNICA SER. EL. ENG. VOL. 42, NO. 1, PP. 147-154 (1998) A GENERIC MODEL FOR KNOWLEDGE BASES R.F. ALBRECHT x and Gibor NEMETH xX • University of Innsbruck Austria •• Technical University of Budapest H-1521 Budapest, Hungary Received: October 30, 1997 Abstract A knowledge base system is a database system with logical, temporal and topological structures together with operations on these structures. vVe provide the necessary math-ematical concepts for modeling such a system. These are parametrized hierarchical rela-tions, logic functions, hierarchies of variables with their hierarchical control operators, and neighbor!1oodjsimilarity structures. These concepts are then applied to define a model of a knowledge module. By composition of knO\vledge modules we obtain the knowledge system model. Keywords: knowledge bases, system theory. Introd uction In our model a knowledge base system KBS consists of the follmving com-ponents: S a set of primitive objects, 5(S) a hierarchy of relations S, all parametrized (referenced) by indices of hierarchically structured index sets, F(S) an explicitly given part S(S), the 'facts', D(S) = def 5(S) \F(S) the implicitly given part of 5(S), obtainable by com-posite applications of functions of R, a set of (inference, deduction) 'rules', the application of which is in general subject to constraints. conditions. grammatical rules, collected in a set, r (R) the grammar of R. The representation of KB5 is facilitated by use of variables on sets of components on all hierarchical levels. Assignments 'to variables and reciprocal, reassignments to substitutable components are performed by C a hierarchy of control functions and their reciprocals, \vhereby a control function val : P X {var x} -+ X is associated with each variable var x, and where X = {x[P] I pEP} is the variability domain ('type') of var x and P is a set of control parameters p. An assignment to var x is then expressed by val (p, var x) = x[P], usually written varx: (p)x[P]. Domains of variables can contain variables of lower hierarchical level 148 R. F. ALBRECHT and G. NEMETH and variables can defined on sets of lower level control parameters of variables [A1BRECHT 1995, 1996, 1997]. To operate on the components of KBS, a set of operations OP has to be given (e.g. selectors like subset forming, projections, cuts, selection of su bstructures by properties, constructors like set forming, set prod ucts, set union, set intersection, concatenation of relations, and transformations of objects and indices, counting cardinalities [A1-BRECHT 1995, 1996, 1997]. Again a grammar f(OP) for the application of operations of OP may be given. To express structural properties of KBS, \Ye need P a set of predicates, e.g. generalized quantors, 'is part of' property, etc. Given a partially or linearly ordered logical or physical model time (T, <) [A1BRECHT 1995, 1996], all components of KBS can be indexed by time points t E T and processes (KBSt)tE[iCT \Yith varying states KBS[t] at time points t E U ~ T can be considered. Temporal prop-erties can be adjoined to P. Finally, on each hierarchicalleve/' sets of objects, rules and parameters of variables can be topologized, mostly by introducing a uniform topological structure. Topological properties can be adjoined to P, for example general distance or similarity measures [A1BRECHT, 1997]. In engineering science topological structures are used under the name 'fuzzy'. Knowledge Representation Knowledge we have in mind in form of memorized perceptions, concepts, be-havioral processes, intellectual processes. is physically represented by struc-tured physical objects in space-time dimension \Yhich we are able to 'in-terpret'. Mathematically, these objects are abstracted and represented by normed symbols in mathematical space, subject to mathematical operations (aggregations, partitionings, su bstit u tions, combinations, referencing, etc.). The time dimension is mapped onto orderings in space. \~'e use parametrized sets (families. relations) and operations on these for the mathematical de-scription. The Hierarchy of Parametrized Relations Let there be given a set 5, 5 =f. 0, of elements s. considered primitive with respect to the hierarchy. 5 is isomorphic with the family (s) sES (canonical indexing). For a given index set /(1), /(l) =f. 0, let there be 5{l) ~ 5 U UJC1(1) 5 J , 5(1) =f. 0. An element (Si)iEJ of 5{l) is a family or relation on level 1 \\'ith I-dimensional index set J, with ind J -+ 5, i -+ Sri], Si = def (i, S[i]). A GENERIC MODEL FOR KNOWLEDGE BASES 149 For a given index set 1(2), 1(2) i= 0, let there be 5(2) ~ 5(1) U UJCI(2) x x (5(1)) J, 5(2) i= 0. An element of 5(2)\5(1) i= 0 is a family of fami-lies (( Si(2) i(1)) (i(2) ,i(l) )EJ(1) [i(2)]) i(2) EJ(2) , or a relation on level 2 'with two 1-dimensional index sets J(l) ~ 1(1), J(2) ~ 1(2), whereby we use the nota-tional conventions J(1)[i(2)] = def {i(2)} x J(l), J(2)[i(1)] = defJ(1) x {i(2)}, J(2,1) = {(i(2), i(1)) I i(2) E J(2) 1\ i(1) E J(1)[i(2)]} = {(i(2), i(l)) I i(1) E J(1) 1\ i(2) E J(2)[i(1)]} ~ 1(2) x 1(1). Then the position of the indices ex-presses their membership in the hierarchy of index sets. Concatenation of the family of families yields K((si(2)i(1) )(i(2),i(1))EJ(1)[i(2)1)i(2)EJ(2) = (si(2)i(1) )(i(2),i(1))EJ(2,1), a family with 2-dimensional index set. On the other hand, (cut ({i(2)}) (Si(2),i(I))(i(2),i(1))EJ(2,1)));(2)EJ(2) = ((Si(2)i(1))(i(2),i(1))EJ(I)[i(2)])i(2)EJ(2) , and for the transposed case, (cut ({i(1)}) (si(2)i(1) )U(2),i(1))EJ(2,1) ))i(1)O(1) = ((si(2)i(!))j(2),i(1))EJ(2)[i(1)j)i(I)EJ(1)' Applying induction with respect to n E N we have for given 5(n), 1(n+1), 1(n+1) i= 0, 5(n+1) ~ 5(n) U UJcI(n+l) (5(n))J in general on hierarchi-callevel n+ 1. An element of highest hi~rarchicallevel of 5(n+l) is then of the form ( ... ( (si( n+ I) ... i(1) L(n+ I) ... i(1) EJ( I) [i( n+ I) , ... i(2)]) i( n+ I) ... i(2) EJ(2) [i(n+ I) , ... i(3)]) ... . . . );(n+l) EJn+(I), or if concatenated (Si(n+l) ... i(1) ) i(n+l) ... i(1) EJ(n+I,.I) with J(n+1, ... 1) ~ J(n+1) x ... :; J(1). The structural complexity of the objects is mirrored in the structural complexity of the indices after concatenation. EXAMPLE 1 Construction of valuated objects, especially logics [see AL-BRECHT, 1997]: We assume 5 = A. U y' is a partition, 1(1) = ({l. 2}, <), 5(1) ~ A. x y' with elements (a, v), the indices suppressed, 1(2) a finite set, 5(2) ~ TIJCI(2) (5(1))J \\'ith elements (ai, Vi)iEJ, 1(3) = ({I, 2}, <). 5(3) ~ 5(2) x V \~ith elements ((ai, Vi)iEJ, v), whereby v = YcardJ((L'[ilLEJ), Ycard J : v card J -7 V, which is in particular a logic function for V a lattice. In this example TI, x, ycard J are elements of OP, the applications of TI, x are restricted, the restrictions are elements of [(OP). Rules At logical time t let there be given a part D ~ F. A rule fER is then a surjective function f : D -7 VV with d r-+ U' = f(d). If the grammar [(OP) admits for F and w a concatenation, then at logical time t', t < t', D' = def K((F,w),£(C),C) (for concatenations I refer to [ALBRECHT, 199.5]). If i(w) is the (composite) index of w, then w = def pr(i(w))D'. Rule applications can be composed. \Ve distinguish rule applications from operations op E OP. However, both can be combined, 150 R. F" ALBRECHT and G" NEMETH EXAMPLE 2 (,formal languages'): I( = {1,2,3,4}, D = {(aikhEK , i E 1/\1\ i E 1(a[i]3 = a3/\ 1\ k E K(a[ik] E A))}, A a given set, 'production rule' !(a3) = (b31 b32 ), K(pr( {il, i2, i4} )D, {(b31 , b32 )} = {(ail, ai2, b31 , b32 , ai4)} (concatenation with replacement, a3 ::= (b31 , b32 ), in context 1\ i E (a[il], a[i2],a[i4) EA)). EXA!\!PLE 3 Creasoning' inference rules): K = {l. 2, 3, 4}, V = {'t', '!'}, D = {((ak,vkhEK, v = Y4((V[k]hEK) 'v E V /\ (v[kJhEK E V4}, inference rule! : D -+ {((b1, WdIE{I,2}, W =W2((W[1])IE{I,2}) , wE V /\ (W[I])IE{I,2} E V2}, Y4, ?j;2 logic functions, such that ((ak' Vk)kEK, Y4((V[k]hEK) = 't'I'!,) f-7 ((b 1, WdIE{I,2j,li'2((W[I])IE{I,2}) = 't'I'I'). An example in usual notation is: 'if' ((a[I]/\ a[2]/\ a[3]) V a[4]) 'then' (b[l] V b[2]) 'else' -,(b[1] V b[2])' Utilisation of Variables We can represent a KBS by a hierarchy of variables and their control func-tions/operators: Starting on 'top'. we consider var KBS = (var S, var S (varS), varF(varS), yarD (varF), varR (varF), varf (varR), varOP, varf (var OP), var P, var (T, <)). All variables var X range on given domains X parametrized by p[x] and have control functions val: p[x] -+ .X:- with control parameters P[x] E p[x]· The assignment steps in logical time are: var S := S f::. 0, selection of the primitive objects: var 1 := 1 f::. 0, selection of the primitive indices: var OP := OP. var f(\'ar OP) := f(OP). selection of admitted structors for 5: var P := P. selection of structural predicates: for bottom up construction of the hierarchy F up to F(S): v,u F(O) : pO\\' 5\0. selection of \"ar F(O) := F(O): F(O) = 0: var S: N \'cH S:= .Y. for 71 0.1. 2.,. ,S - 1: \',H j{,,+I) : Po\\" [\0, selection of var [(n+l) := 1(,,+1): \'ar F(7, ,-I) : pow UJcvar /(n+i) (F(n))J\0. selection of var F(n+l) := F(n+I); \'ar F(n + 1) F(71) U F{71+I): varR(F(S)) := R(F(S)), varr(H(F(S))) := R(F(.Y)). selection of admitted rules. This rcsldts in var KBS := 1\:BS. \Ve suppressed the assignment pa-rameters, Assignments to composite variables can be performed in partial :-;teps [see e.g. ALBRECHT 1997J. Analogously, assignments to time variables and topological structure variables can be made. The deduction steps in logical time are: A GENERIC MODEL FOR KNOWLEDGE B.4SES 151 var f(var D) : R with f(R). Selection of f : var f := f, follows var D := D, varlV := f(D). Selection of an argument: vard: D, vard:= d, evaluation of var w := w = f(d). Decision on operation on (F, w) : var op(F, w) : OP with f(OP), var op(F, w) := op(F, w). So far we made the assumption, that 'someone' made the selection of all the control parameters P[x) involved, whereby the sets p[x) of control parameters are in hierarchical dependence. Parametrizing these sets by higher order parameters Q[x), we can define variables on the lower order parameter sets p[x) : var P[x) : {P[q.x) I P[q.x) E p[x) A q E Q[x)} and control functions val : Q[x) x {var P[x)} -7 p[x)' These higher order control functions can depend on the results of previous 100ver order assignments (,feedback') and on currently given external parameters ('goals') and are assumed to represent 'higher intelligence' for forthcoming decisions. The hierarchy of higher order control functions can be extended. More details are given in [ALBREcHT, 1997]. Binary Knowledge Modules As a particular but important case we consider knO\vledge represented by valuated binary relations. For example, if Y = f(x), (y, x) is a pair, if (y, .r) is a proposition ( object Y has property x), it can be val uated for exam pIe by v E V = {'t', 'f'} to give ((y, x), v). This includes of course composite objects (relations) y and composite properties (relations) x and arbitrary sets V with any structures. Deterministic Case with Discrete Topology Given a non-empty set Y of elements y named 'objects' and a non-empty set X of elements x named 'properties', bijective parametrizations ind : J H Y, ind': I H X, and a relation R = (Yj,Xi)(j.i)EU, [; <;;; J x I, with pr1U J, pr2U = I. As well we could have named X the set of objects and Y the set of properties. If V is a non-empty set and y : R -7 V is a valuation, then R can be represented by ~VJ = def(vji)(j,i)EU with Vji =defY((Yj. Xi)). We consider /\j E J(cut ({j}) Ai = (Vj;)iEI[JJ)' Ai E I(cut {i}) M = (Vji)jO[,]), which define IU), JU)' and we assume Aj,j' E J(j =j:. j':::::} (Vji)iEl[JJ =j:. (L'j1iLEI[JljL Ai,i' E l(i =j:. i':::::} (Vji)jEJ[iJ =j:. (Uji')jEJ["J)' We name ((Yj,X;),Vji)jiE[i a 'knowledge module' 101. Let there be given TJ: V x V -7 B = ({'f. 'f'}, n, \vithTJ(diag V x V) = {'t'}. TJ(V x V\diag V x \l) = {'f'}. For card I( < card [T we consider ycard J{ E (Beard J{ -7 B). For j <;;; I and j <;;; J we define J[l] = 152 R. F. ALBRECHT and G. NEMETH defnEiJ[i] = {j 1 j E J /\ J ~ IU]}' I[J] = defnjEjIU] = {i 1 i E 1/\ J ~ J(i]}, and forv[jij E V,,8 E B, Y((Vi)iEY'Ycardy"B) = def{Yj Ij E J[J]/\ YcardY((17(V[ij, V[jij))iEi) = ,B}, X((Vj)jEj, Ycardj,.6) = def {Xi 1 i E I[J] /\ Ycardj((ry(vU), V[jij))jEj) = ,B} . We have Y((V;)iEi' YcardY' 't') = <pcardy((Y(Vi, 't'))iEi) , Y(Vi, 't') = defY((v)iLE{i},ry(V[i]:V[jij) = 't') , X((Vj)jEj, Ycardj, 't') = <Pcardj((X(Vj, 't'))jEJ) , XCVj, 't') = defX((v)j)jE{j}, ry(vUj, V(ji)) = 't') , <P card j, <P card j the set functions corresponding to the boolean functions YcardY' Ycardj, respectively. Further, is a filter base for J -+ IUj) , /\i E I(Xi = def{X((vUji)jEj, ncardj, 't') 1 J E (pO\\' J[ij)\0}, is a filter base for J -+ J[iJ) . This expresses the 'inheritance' principle: the larger the set of common properties/objects, the smaller the set of objects/properties possessing these properties/objects. If /\j E J(lim Yj = {Yj}) and /\i E I(limXi = {Xi}). then we say (L'ji)iEI[J] and (Vji)jEJ[,] 'characterize' Yj and ~'i, respectively. Under this assumption, there may exist 'coarser' filter bases Y7 and Xi also converging to {Yj} and {.I'd. respectively [see e.g. ALBRECHT 1994]. It can be of practical importance to find such )-7 and Xi of maximal coarseness (minimal characterizations). For all IUt being characteriiations and for a given {Yj 1 j E J} we have = u n Y(VUij, 't') , jE] iEI'Uj and an analogue result for the transposed equation. Considering the dual is an ideal base for J -+ IUj) , A GENERIC MODEL FOR KNOWLEDGE BASES 153 /l.i E I(Xi = def{X((vU]i)jEj, Ucardj, 't') I j E (powJ(i])\0}, is an ideal base for j -+ J[i]) , we have the dual to the inheritance principle: the larger the set of alternative properties/objects, the larger the set of objects/properties possessing these properties/ objects. 'Queries' to the knowledge module KM are then formulated by appli-cations of operations of OP. For example: 1. given: 0 C K ~ U. (f'ji)jiEK, 0 C \I"Ui] ~ VT; find: cut ((Vji)jiEK)M, find card cut ((Vj;)jiEK)AI, find { v j'i' I v j' i' E l\1I /I. VL'ji E cut (( Vji) jiEK) AI (V[j'i'] = V[ji])} . 2. given: /I.(j,i) E K(\-i[jij ~ V"), YcardK, /3, find: /I.(j, i) E K /I. Vji E \-~i(R((Vji)jiEI{' YcardK, ,3), with R( (Vji) jiEK, ycard I{, 3) = def {Vji I (j, i) E K /I. Ycardj((7](V[jij, V[jij))jiEK)) =3}. 3. given: (Vi)iEl' Ycard1' 3, find (Yj)jEJ' = defY"((v')iE1, Ycard1, (3); given (Yj) j El': find (all) (Vi) iEl' Y card 1, ,3) such that (Yj ))0* = Y ((i\)iE1, Ycard l' .3) . 4. For 0 C j C I[jl let be (Yj')j'El* = Y((V[jji)iEl,ncardf 3). This defines deduction rules, implicitly given by AI: \vith r(j) = I[j]\l r = U r(j), r(i) = {j IJ E r /I. i E ru)}· jO' The conclusion can be repeated for (,Ti, (L'j;)jEl*(i)):EI*. Deterministic Case with General Topologies \Ve assume that (V :S, uu , ne) is a complete atomic boolean lattice. Then the families (Xi, U[j]iLEI[jj define functions f[j] : I[jj -+ F. The set extensions of the fU] are homomorphisms, i.e. for I' ~ I" ~ IU] holds fU] (I') :s fU] (I"). Then a filter/ideal base I = {I[jk] I k E K} on pow I[j] maps onto a filter/ideal base V = {f[j](I[jk]) I k E K} on pow V Filter/ideal 154 R. F. ALBRECHT and G. NEMETH bases can express neighbourhood/similarity relations between objects y and between properties x. To measure neighbourhood and similarity of values v t;: 1l we introduce a uniform topological structure on F by a filter base B = {D[q] Iq E Q} on pow (FxF) with l/xF E B, diag(1/x1/) <;:;; nqEQ D[q], and D~{ = D[q] and assume B is itself a complete lattice. Then for any pair Cv, v) E Il X V uniform, generalized, multivalued distances duCv, v) and dn(v, v) E B with du(iJ, v) C dn(v, v) can be introduced [see ALBRECHT, 1997J. \Ve set ry(v, v) = dn(v, v) and have B (or any isomorphic complete lattice) as generalization of B = ft', 'J'}, the latter corresponding to B = {V x 1/, diag(\l x V)}. This makes it possible to measure the distance of any (V··) "E C" - pr(K) M from a uiven (V')"E T." bv (0["] - def n(V[··] V[' .])) "E To" Jl )1 H - • 0 Jl Jl H v , Jl - ./ J'" Jl Jl I\. and to valuate the Vji : (Vji, ,Oji)jiEK. We then can appLy a logic function ycardK E <!>cardK = (BcardK -+ B) for a valuation ((vj;,3 ji )jiEK, 'PcardK (C3[ji]) jiEK) and can formally proceed as in the boolean case before. Knowledge Modules with Variables If the knmvledge module contains variables, e.g. ((Yj, Xi), VarVji), they express indeterminacy in the sense that the domain ('type') of the variable is known but the value to be assigned is not yet determined. This case has to be distinguished from elements not appearing in the mod ule, e.g. index pairs (j', if) E (.J x I) \ U. 'Queries' with variables to a mod ule with variables in general result in 'answers' \vith variables. Composition of Knowledge Modules A knowledge module can be seen as an input/output system and hence modules can be composed to a knowledge base system by feeding (part of) the answer of one module as (part of a) query to the same or another module. This composition is analogue to the composition of functional modules in computer architecture. References [1J ALBRECHT, R. F. (1994): Some Basic Concepts of Objectoriented Databases. System Science, Vo!. 20/1, vVroclaw, pp. 17-30. [2J ALBRECHT. R. F. (1995): On the Structure of Discrete Systems. Lect. ;Yotes in Comp. Se., Vo!. 1030, Comp. Aided Systems Theory, Springer, pp. 3-18. [3J ALBRECHT, R. F. (1996): The Structure of Discrete Systems, Trends in Theoretical lnformatics, (eds. R.F. Albrecht, H. Herre). Osterreichische Computer Gesellschaft, pp. 127-144. [4J ALBRECHT. R. F. (1997): Systems with Topological Structures. submitted to 1nt. Conf. on Computing AnticipatTy Systems, CASYS97. Liege. 

A GENERIC MODEL FOR KNOWLEDGE BASES

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A GENERIC MODEL FOR KNOWLEDGE BASES

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