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<Paper uid="P79-1011">
  <Title>ON THE AUTOMATIC TRANSFORMATION OF CLASS MEMBERSHIP CRITERIA</Title>
  <Section position="3" start_page="0" end_page="45" type="metho">
    <SectionTitle>
2. INRXAC T MATCHING
</SectionTitle>
    <Paragraph position="0"> The research project presented in this paper was motivated by the need for determining automatically whether a set of facts comprising the description of a legal case satisfies the conditions expressed in a legs/ definition, and, if not, in what respects it fails to satisfy those conditions \[8\], \[9\], \[I0\], \[11\], \[13\]. The need to perform this task is central to a larger project whose purpose is the representation of the definitions of certain legal concepts, and of decisions based on those concepts.</Paragraph>
    <Paragraph position="1"> inexact matching arises in the legal/judlclal domain when a legal class must be assigned to the facts of the case at hand, but when an exact match cannot be roland between those facts and any of the definitions of possible legal classes. In that situation, a reasonable first-order approximation to the way real decisions are made may be to say that the class whose definition offers the &amp;quot;best&amp;quot; or &amp;quot; closest&amp;quot; match to the facts of the case at hand is the class that should be assigned to the facts in question. That is the approach taken in the current project.</Paragraph>
    <Paragraph position="2"> In addition to the application discussed here (the assignment of an instance of a knowledge structure to one of a set of classes), inexact matching and close relatives thereof are also found in several other domains within computational linguistics. Inexact matching to a knowledge structure may also come into play in updating a knowledge base, or in responding to queries over a knowledge base \[5\], \[6\]. In the domain of syntax, an inexact matching capability makes possible the correct interpretation of utterances that are not fully grammatical with respect to the grammar being used \[7\]. In the domains of speech understanding and character recognition, the ability to perform inexact matching makes it possible to disregard errors caused by such factors as noise or carelessness of the speaker or writer.</Paragraph>
    <Paragraph position="3"> When an inexact match of an instance has been identified, the first step is to attempt to deal with any criteria ~nich were not found to be satisfied in the instance, but were not found not to be satisfied either -- i.e., the unknowns. At that point, if an exact match still has not been achieved, two modes of action are possible: the modification of the instance whose characterization is being sought, or the modification of the criteria by means of which a characterization is found. The choice between these two responses (or of the way in which they are combined) appears to be a function of the domain and sometimes also of the particular item in question. In general, in the  lesallJudlcial domain, the facts of the case, once determined, are fixed (~nless new evidence is introduced), hut the criteria For assigning a legal characterization to those facts may be modified.</Paragraph>
  </Section>
  <Section position="4" start_page="45" end_page="46" type="metho">
    <SectionTitle>
3. I~Z~~E t~ ~ A p.mh+mtM~my
</SectionTitle>
    <Paragraph position="0"> Because of. the importance of inexact ~atchlnE in the legal/judlclal domain, it is desirable to utilize a matehir~ procedure that permits useful functions related to inexant matching to be performed conveniently. Such functions include a way of. easily determining all the respects in which attempted exact matches to a particular definition might fail , a wey of. easily determinln~ what chlln~es to a definition would be suf.f.icient For an exact match with a particular case to be permitted, and a wey of ensuring that a contemplated modif.lcation to a def.inition will not introduce inconsistencies.</Paragraph>
    <Paragraph position="1"> Two f.eatures of. a representational scheme that would appear to help in performin~ these functions conveniently are SPEC1) that the scheme permit a distinction to be made between those propositions that must be t~ be true of. any instance satlsfylng the def.lnltion and any other propositions that might also be true of. the instance, and SPEC2) that the scheme permit the former set of.</Paragraph>
    <Paragraph position="2"> propositions to be expressed in a simple, ulilf.led wey, so as to redune or even eliminate the need for inf.erencing and other processing activities when the ~ntlons outlined above are performed.</Paragraph>
    <Paragraph position="3"> By satlsfyi~ SPECl, we permit the propositions which are central to the matohiDg process to he distir~ulshed from any others; by satisfying SPEC2, we permit those propositions to be accessed and manipulated (e,go, for the inexact matching Functions listed above) in an efficient and straightforward manner. Thus, the Fulfillment of 3PECI and SPEC2 slgniflcantly strengthens our ability to perform Functions central to the inexact matching process.</Paragraph>
    <Paragraph position="4"> A representational scheme that meets these specifications has been designed, and an experimental implementation performed. The approach used is to precede the matching activity proper with a one-tlme preprocessing phase, duping Milch the definition is automatically transformed from the form in which it is originally expressed into a representational scheme which appears to be more suitable to the matching task at hand. The transformation algorithm makes use of a distlnntion between those components of the definition wl~ich must be Found to be true and those whose truth either may be inferred or else is irrelevant to the matching process. The transformation is performed by means of a process of ~ inmtRntlat~nn OF the deflnition -- the translation of the de/initlon f~'om a set of criteria for satisfying the definition into an exemplary instance of the concept itself. The transformed definition resulting fro m this process appears to meet the speclf.ications given above.</Paragraph>
    <Paragraph position="5"> The input to the transformation process is a definition expressed in two parts: CCHPONENTI) a set of propositions eonslsting of relations between typed variables organized in frame form, and CCI4POMENT2) a set of' pattern-directed inference rules expressing constraints on how the propositions in CCHPONEMTI .my be Instantlated.</Paragraph>
    <Paragraph position="6"> 'rite propositions in COHPONENTI include propositions that must be found to be true of. any instance satisfying the</Paragraph>
    <Paragraph position="8"> definition, as well as other pr~positions that do not have this quality.</Paragraph>
    <Paragraph position="9"> The output from the trans{ormation process that is used for matching with an instance is a symbolically instantiated form of the definition called the KERNEL fo~ the definition. It consists solely of a set of propositions expressing relations between instances. These are precisely those propositions whose truth must be observed in any instance satisfying the definition. Constraints on instantiation (COMPONENT2 above) are reflected in the choice of values for the instances in these propositions. Thus the KERNEL structure has the properties set forth in SPECI and SPEC2 above, and its use during the matching process may consequently be expected to help in w~rking with inexact matches. For similar reasons, use of the KERNEL structure appears also to permit a significant improvement in efficiency of the overall matching process \[I0\], \[11\].</Paragraph>
    <Paragraph position="10"> The propositions input to the transformation process (i.e., COMPONENTI) are illustrated, for the definition of a kind of corporate reorganization called a BREORGANIZATION, in Figure I; the arcs represent relations, and the nodes represent the types of the instances between which the relations may ho\]d. Several of the pattern-directed inference rules input to the transformation process (COMPONENT2) for part of the same definition are illustrated in Figure 2. The KERNEL structure for that definition output by the transformation process is illustrated in Figure 3. The propositions shown there are the ones whose truth is necessary and sufficient for the definition to have been met. Bindings constraints between nodes are reflected in the labels of the nodes; the nodes in Figure 3 represent instances. Thus, the two components represented in Figures I and 2 are transformed, for the purposes of matching, into the structure represented in Figure 3, The transformation process is described in more detail in \[I0\] and \[11\]; \[10\] also contains an informal proof that the transformation algorithm will work correctly for all definitions in a well-defined syntactic class.</Paragraph>
    <Paragraph position="11"> ~. ~X~CUTIONOFTHEMATCHINOPR~CESS Once the transformation of a definition has been performed, it need never again be repeated (unless the definition itself should change), and the compiled KERNEL structure may be used directly whenever a set of</Paragraph>
    <Paragraph position="13"> Ffi_ u_re ~: A portion of COMPONENT2 or a sample definition.</Paragraph>
    <Paragraph position="14"> facts comprising a description of a legal c;Jse L~ presented-for comparison with the def(nit~n.</Paragraph>
    <Paragraph position="15"> In order to control possib\]e combinat~ric diffLcu\]+\[es, the KERNEL structure is decomposed tnt~ a se t ~r small networks, against each of which a\]\] substructures ~f the same type in the case description are tes+ed f~r a structural match (STAGEI). DMATCH \[15\], a functL~n written by D. Touretzky, performed structural ma+chLng in the experimental implementation. The hope LS the + &amp;quot;small networks&amp;quot; can be selected from the KERNEL in such a way that matching to any single small n~twork wi|\] involve a minimal degree of combinator\[c compiexEty.</Paragraph>
    <Paragraph position="16"> For an exact match, the substructures that survive STAGEI (and no others) are then combined in all p~ssibie valid ways into larger networks ~f s~me degree ~f increase in complexity. A structural match ~f each ~f these structures with the corresponding substructure ~f the KERNEL is then attempted, and bindings c~nstraints between formerly separate components of the new network are thereby tested. This process is repeated wLth surviving substructures until the structural match is conducted against the KERNEL structure itself. When +he criterion for matching at each stage Ls an exact match, as described above, the survivors of the final s~age ~f structural matching represent all and ~n\]y the subcases in the case description that meet the c~ndi+i~ns expressed in the definition.</Paragraph>
    <Paragraph position="17"> The execution of the marcher in the manner described above is illustrated in Figure 4. For this example, five instances of the type TRANS (TI, T2, T3, T4, TL), two instances of the type CONTROL (CI, C2), and ~wo instances of PROPERTY (06, 09) were used. The value of MAKEFULLLIST shows the survivors of STAGEI. The value of BGO shows the single valid instance of a BREORGANIZATION that can be created fr-m these components.</Paragraph>
    <Paragraph position="18"> An inexact matching capability, not currently implemented, would determine, when at any stage a match failed, I) why it had failed, and 2) how close it ned come to being an exact ms+oh.</Paragraph>
    <Paragraph position="19"> At the next stage, a combination of substructures would be submitted for consideration by the marcher only Lf it had met some criterion of proximity t~ an exact match -either on an absolute scale, or relative to the ~ther candidates for matching. When the final stage ~f the matching process had been completed, that candidate (or those candidates) that permitted the most nearly exact match could then be Selected.</Paragraph>
    <Paragraph position="20"> In order to perform the inexact matching function outlined in the preceding paragraph, an a\]g-rithm for computing distance from a exact match must be formulated. For the reasons given above, we anticipate that I) the transformation of definitions into the corresponding KERNEL structures will make that task easier, and that 2) once a distance algorit~ has been formulated, the use of the KERNEL structLLPe will contribute to performing the inexact matching f~/nction wlth efficiency and conceptual clarity.</Paragraph>
  </Section>
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