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Ontological Engineering for B2B E-Commerce Leo Obrst Robert E. Wray Howard Liu The MITRE Corp. 7515 Colshire Drive McLean, VA 22102 [email protected] [email protected] P. 0. Box 22285 Sacramento, CA 95831 [email protected] Abstract - In this paper we discuss the nature of our overall enterprise to create ontologies in the product and service knowledge space for Business-to-Business (B2B) electronic commerce. We describe one crucial problem: the mapping problem, i.e., mapping among ontologies, taxonomies, and classification systems, some of which are more semantically sound and coherent than others. This problem we consider to be in need of a sustained research program if tenable solutions are to be found, since the lack of a solution will preclude widespread adoption of ontologies by the commercial world. Finally, we summarize the general issues we faced and indicate prospective future research. Categories 8 Descriptors - 1.2.4 [Artificial Intelligence]: Knowledge Representation Formalisms and Methods - Predicate logic, Representation languages, Representations (procedural and rule-based), Semantic networks. General Terms - Algorithms, Design, Economics, Experimentation, Languages, Management, Performance, Reliability, Standardization, Theory. Keywords - Ontological engineering, ontology management, ontology mapping, ontology merging, business-to-business electronic commerce, B2B e-commerce. 1. Introduction In this paper we discuss the nature of our overall enterprise as ontologists and domain experts working to create ontologies in the product and service knowledge space for B2B electronic commerce that included domains (lower ontology), a generic upper ontology adapted from the Cyc upper model, (http://www.cyc.com/tech.html) and shared middle ontologies. We describe one crucial problem: mapping, i.e., mapping among ontologies, taxonomies, and classification systems, some of which are more semantically sound and coherent than others. We consider this problem to be in need of a sustained research program, since the lack of a solution will preclude widespread adoption of ontologies by the commercial world. The structure of this paper is the following. In Section 2 we describe the B2B enterprise and the use of ontologies to facilitate this enterprise. In Section 3 we highlight the mapping problem. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. FOIS'OZ, October 17-19,2001, Ogunquit, Maine, USA. Copyright 2001 ACM I-58113-377-4/01/0010...$5.00. 117

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Page 1: Ontological Engineering for B2B E-Commercearmyunis/knowledge-managment/papers...Ontological Engineering for B2B E-Commerce ... The Nature of the B2B Enterprise B2B electronic commerce

Ontological Engineering for B2B E-Commerce

Leo Obrst Robert E. Wray Howard LiuThe MITRE Corp.

7515 Colshire DriveMcLean, VA 22102

[email protected]

[email protected] P. 0. Box 22285Sacramento, CA [email protected]

Abstract - In this paper we discuss the nature of our overall enterprise to create ontologies inthe product and service knowledge space for Business-to-Business (B2B) electronic commerce.We descr ibe one crucial problem: the mapping problem, i .e . , mapping among ontologies ,taxonomies, and classification systems, some of which are more semantically sound and coherentthan others. This problem we consider to be in need of a sustained research program if tenableso lu t ions a re to be found , s ince the lack of a so lu t ion wi l l p rec lude widespread adopt ion ofontologies by the commercial world. Final ly, we summarize the general issues we faced andindicate prospective future research.

Categories 8 Descriptors - 1.2.4 [Artificial Intelligence]: Knowledge RepresentationFormalisms and Methods - Predicate logic, Representation languages, Representations(procedural and rule-based), Semantic networks.

General Terms - Algorithms, Design, Economics, Experimentation, Languages,Management, Performance, Reliability, Standardization, Theory.

Keywords -Ontological engineering, ontology management, ontology mapping, ontologymerging, business-to-business electronic commerce, B2B e-commerce.

1. IntroductionIn this paper we discuss the nature of our overall enterprise as ontologists and domain expertsworking to c rea te onto logies in the product and serv ice knowledge space for B2B e lec t ron iccommerce that included domains ( lower ontology) , a gener ic upper ontology adapted from theCyc upper model, (http://www.cyc.com/tech.html) and shared middle ontologies . We descr ibeone crucial problem: mapping, i .e . , mapping among ontologies , taxonomies, and classif icat ionsystems, some of which are more semantically sound and coherent than others. We consider thisproblem to be in need of a sustained research program, since the lack of a solution will precludewidespread adoption of ontologies by the commercial world.

The structure of this paper is the following. In Section 2 we describe the B2B enterprise and theuse of ontologies to facilitate this enterprise. In Section 3 we highlight the mapping problem.

Permission to make digital or hard copies of all or part of this work for personal or classroom use isgranted without fee provided that copies are not made or distributed for profit or commercialadvantage and that copies bear this notice and the full citation on the first page. To copy otherwise, orrepublish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.FOIS'OZ, October 17-19,2001, Ogunquit, Maine, USA.Copyright 2001 ACM I-58113-377-4/01/0010...$5.00.

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Finally, in Section 4 we summarize the general issues we faced and indicate prospective futureresearch.

2. The Nature of the B2B EnterpriseB2B electronic commerce is everything that land commerce is, plus more: automated support forinformat ion and t ransact ion f low and for ver t ical and hor izontal commercia l in teroperabi l i ty .B2B electronic commerce includes the following: multiple marketplace platforms on the Internetthat suppor t mul t ip le t rading models (auct ions , reverse auct ions , exchanges , Request-For-Proposal/Request-For-Quote (RFPRFQ), bookstores, trading hubs, etc.) for and by commercialorganizations, providing rich information content on products and services for both buyers andsel lers (ca ta logs , product guides , market and domain edi tor ia l content , news, adver t i s ing) andsupport for buying nd selling, financing, privacy/security, payment processing, ordermanagement, protiling/personalization, product configuration, planning/scheduling andforecast ing, product l i fe cycle and inventory management , business processes , workflow, andrules, logistics, distribution, and delivery.

B2B e-commerce needs ontologies. First, there is an informational need: because the ontologyis a structured conceptual model of the e-commerce vert ical domain (and sometimes, quasi-hor izonta l domains too) , i t suppor ts parameter /proper ty-based search and naviga t ion us ingproduct and service knowledge by prospective buyers to discover what to buy, andsubsequently to determine pricing and availability. In this case, the relatively static knowledgeof the ontology maps to the relatively dynamic data of the vendors. Furthermore, an ontologycan model not only commodit ies , but a lso agents , i .e . , buyers and sel lers , both human andartificial. By employing user role knowledge (sometimes called user profiling orpersonal iza t ion) to ass i s t the search process , quer ies can be cus tomized to a user ’s knownfunctions and interests, possibly based on previous interaction with that user.

E-commerce also needs ontologies for t r a n s a c t i o n a l purposes : knowledge of a company’sorganiza t ional s t ruc ture , workf low, processes , and products / serv ices can be used to ac tua l lyassist in buying and selling directly.

The ontologies were built to support the representation requirements, as opposed to thepresen ta t ion requirements of the intended applicat ions, al l of which presumed some form ofclassification of products and services. Classification systems are typically ad hoc, inconsistent,and not integrated, with little association between classification systems. Representation was theunderlying structure and codification of the product and service knowledge space to be suppliedby the even tua l ly deve loped on to log ies . This represen ta t ion would be semant ica l ly sound ,consistent (though obviously always incomplete because additional refinements could always bemade) , control led, modular , reusable , and provide some support for appl icat ion presentat ionneeds. Presentation was largely the responsibility of the application, though the ontologies wouldoffer some support . I t i s in the mapping among ontologies and c lass i f icat ion systems, and themapping among representa t ion categor ies and those presenta t ion categor ies preferred byparticular applications, that technical difficulties lie. This mapping problem we address in thenext section.

3. Mapping Ontologies to Ontologies, Taxonomies and ApplicationsIn th i s sec t ion we d i scuss in de ta i l one c ruc ia l i s sue we faced in deve lop ing on to log ies tosuppor t B2B e-commerce: the problem of mapping reference ontologies with well-definedsemant ics to o ther ontologies , taxonomies , and s tandards-based c lass i f ica t ion sys tems that areless semantically sound and coherent.

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Ideal ly , ontologies provide a semant ic infras t ructure that can be used for a l l appl icat ions . Toprovide this semantic framework across the appl icat ions/data of an e-commerce business( independent ly developed and wi thout commitment to ontologica l inf ras t ruc ture) , ex is t ing andplanned information resources must be connected to the ontology framework. We term thisconnect ion a “mapping.” A mapping is a many- to-many re la t ionship between source data andan ontology. Sources for mapping could be another ontology, some standard taxonomy, OT anapplication’s data structures.

. ..~‘. “....~ 1.. ‘. ~.

” 1 ,_, ‘, ._ . , ,_.: . ..‘ i- _. ., , .’

Figure 1. A Simple, Informal Application Taxonomy (left) Mapped to an Ontology(right)

Figure I illustrates a mapping. Solid lines indicate a well-defined subclass relation in theontology (right); in the taxonomy (left) the solid line represents a parent-child relation, with ill-defined semant ics . The heavy, dotted lines without arrows represent other ontological relations.The thin, double-armwed l ines dep ic t a mapping be tween nodes in the on to logy and da tastructures in the application taxonomy.

On the left, an e-commerce application uses a taxonomy with ill-defined semantics to representsome information. For example, node Z could represent some indus t r ia l p rocess . Node Y cou ldrepresent products resulting from this process, X the equipment used in the process, and W theemployees involved in the process . The re la t ion be tween nodes i s an undef ined parent -chi ldre la t ion wi th no inher i t ance ; the in format ion i s used in the app l ica t ion to group these re la tedconcepts together. On the right, an ontology represents much of the same information, but with awell-defined semantics for each relation. For example, nodes B and C could represent productsresulting fmm the industrial process A. The relation between A and these nodes is not subclass;it might be “generated-&om,” assuming the semantics of this relation was defined. B e c a u s e Band C are products, they are subclasses of a more general pmdnct node iJVQ, perhaps in a middleor upper ontology. Unlike the application taxonomy, a different, well-defined relation relates Ato D, and so on.

One criticism of this scenario is that the application taxonomy is deficient. In other words, whatis needed is not a mapping, but a clearly defined semantics in the application taxonomy. W i t hsuch a def ini t ion, some automated merging process could (conceivably) merge the nodes.However, practically, such merging was not possible.

Mappings pmvide an intermediate solution to this problem. Once Z is mapped to the ontology,other applications can recognize that Z is similar to A. This example is only one of severaldifferent kinds of mapping we encountered The following sections explore three different kindsof mappings: taxonomic-standard-to-ontology, application-to-ontoloW, and ontology-to-ontology. We consider the different Qes of mapping separately because the more well defined

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the semantics of the source to be mapped to the ontology, the more straightfonvard the mappingprocess and the more semantically rich and powerful the resulting mapping.

3.1 Taxonomic standard to ontology mappingAn e-commerce company will USC open standards whenever possible simply to facilitatein terac t ion wi th o ther companies . A case in point is our use of the UNSPSC. The UNSPSChierarchy includes some I 1 ,000 codes represent ing a taxonomic shuctwe g iven by “segment ,”“family, ” “class ,” and “commodity .” For example , Segment 32 is “Electronic Components andSupplies,” Family 3210 is “Printed circuits and integrated circuits and microa.ssemblies.”

When we began, no attributes were associated with nodes in the UNSPSC, and we had to developattributes ourselves. Furthem,ore, we needed substructure below the lowest level of the UNSPSC(commodity level). Because the UNSPSC was originally developed, not from a buyer or seller’sperspect ive , but to permit f inancia l rollup for account ing purposes , the taxonomy was n o tadequately defined semantically for our purposes. We did, however, want to map to the standard,to suppor t companies tha t adhered to i t . To sa t is fy our more complex needs, we developedNebenstmktur (shadow shucture) that linked the mope semantically well-defined ontology(developed Tom an indus t ry suppl ie r and buyer perspec t ive) to the UNSPSC (Figure 2) . Weprov ided these l inks by over load ing subc lass inher i t ance , thus a l lowing the inspec t ion o rextraction of a purely UNSPSC-based taxonomy with attached attributes and substructure.

kigure 2. UNSPSC Mapped to Electronics Domain Ontology

3.2 Application to ontology mappingAny appl ica t ion data may need to be mapped to an ontology. Before consider ing how toapproach such mappings, the goals for creating the mappings and their use should be identified.

1. Rep-exntafional adequacy A mapping solution should represent the mapping between thesource and on to logy comple te ly and cons i s ten t ly . Completeness impl ies that anyinformat ion in the appl ica t ion tha t could be quer ied by an externa l appl ica t ion has beenmapped. Consistency implies that the most appropriate node in the ontology is mapped tothe external s t ructure . In some cases , th is requirement wi l l mean creat ing new ontologynodes. Thus, there is a tension between completeness and consistency that must be resolvedby the methodology for mapping.

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2 . Heuristic adequacy. A mapping solution must be relatively easy to create and convenient toaccess . Because the mappings wi l l be dynamic (appl ica t ion da ta and onto logies wi l l bechanging over time), an individual mapping should not require significant computation oruser interaction.

3 . Represent once. A mapping solution should not introduce duplication in representation.4. Knowledge engineering cost/Supports automation. A mapping solution should not increase

the workload or day-to-day activities of ontologists and domain experts. Thus, the solutionshould provide support to automate mappings. Because not all mappings can be automated,an infrastructure for mapping should be developed that will allow non-domain/non-ontologyexperts to create mappings that cannot be done automatically.

Given these goa l s , how should the mappings be tween an app l ica t ion and an on to logy beimplemented? The following outlines three possibilities.

Mapping table. A simple way to establish a mapping would be to create a table (e.g., in adatabase) in which each row of the table indicates an association between some applicationdata s t ructure and the ontology. The table could suppor t many- to-many mappings byduplicating either an application structure or an ontology node in some rows. Completenesscan be easily determined by ensuring that each application structure in the table is mapped toat least one ontology node.Model of application. Figure 3 shows an example of building a model of the application inan on to logy . Al l on to logy- re levan t app l i ca t ion s t ruc tu res a re inc luded in the on to logymodel . A mapping relation is introduced that defines the association between an ontologynode and nodes in the application model. In this approach, there is an ontological conceptsuch as “claw hammer,” an appl icat ion-specif ic concept “hammer as used in carpenter’sca ta log XYZ,” and a mapping rela t ion that conveys the informat ion that the “hammer asused in Ca ta log XYZ” corresponds to the “c law hammer” node in the onto logy. Weconsidered using subclass to represent the relations in the application ontology because theapplication concepts in the application taxonomy (rather than in the ontology proper) aresubclasses in the context of the application. That is, “hammer” in the XZC Catalog might bea direct subclass of “tool” in the same catalog, even though in ontological space there ismuch more “distance” between these concepts.Application mapped within the ontology. Figure 4 illustrates another mapping solution. Inthis case, an application-specific mapping relation relates nodes in the ontology to their usein an application. The “claw hammer” node in the ontology is now not only related to othernodes in the domain ontology via domain re la t ions (e .g . , “c law hammer” is a subclass of“hammer”) but a lso re la ted to nodes v ia appl ica t ion re la t ions (“c law hammer i s a type ofproduct in carpenter catalog XYC”).

A complete analysis of these approaches is beyond the scope of this paper, and would includenotions of ontology mapping/merging and context. Although our efforts were interrupted beforewe could empirically explore these possibilities, it was clear that no single approach fully met allof our requirements . The mapping table solut ion was s imple to implement , but lacked powerfulconnections to the ontology that could potentially help automate the maintenance of mappings asthe onto logies and appl ica t ions changed. The increas ing power of the o ther two approachesplaced demands on the representation of the mappings: because the ontologies themselves werebeing changed, ontologists needed to be closely involved in the representation of the models andmappings, in addition to defining mapping relations.

Further, we lacked requirements that could tell us how powerful the resulting mappings needed tobe. For s imple inter l ingua use of the mappings, the mapping table appeared to be a suff ic ient

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solution. Tbe other approaches have higher m&al costs but perhaps could be mainta ined moreeas i ly and cer ta in ly would prov ide a more powerfol subs t ra te for reasoning, because both thestmctore of the application as well as its data is represented (explicitly in the model approach,implicitly in the internal approach). Our solution in the short-term was to pursue all of thesepossibilities to determine the actual consequences ofeach. We believe the mapping problem is asignificant challenge that requires additional research before a conclusive determination can bemade as to the best approach for a given source.

Figure 3. A Model of an Application in Figure 4. Mapping an Application inthe Ontology the Ontology

3.3 Context and mappingWe have used mappings between ontologies to solve some practical problems we encountered.One such problem is that of representing contexts.

A produc t on to logy of one industq may “over lap” wi th tha t of another industry in a BZB e-commerce marketplace. A c l a s s i n one on to logy may conceptua l ly occur in another as w e l l .Every such ontology in which the c lass occurs represents a d i f ferent context for the concept .Thus, we have the problem of express ing the re la t ionship between the dif ferent c lasses indifferent ontologies that actually represent the same concept in different contexts. Although weconsidered other solutions as well, one particular solution uses mappings between ontologies.

Consider the set of WordProcessors classes. A class that represents the concept Word Procasorsis found in Ontology I, Ontology 2 and Ontology 3 Thus, three classes in three ontologies allrepresent the same concept . Without loss of generality, assume that all three classes are labeled“Word Processors.” At first glance, this phenomenon suggests that one may need to eliminate theredundancy by removing a l l but one W o r d P r o c e s s o r s class . AtIer all , one may argue, severalclasses for a single concept defies the reusability principle of representing a concept only once.Upon closer examination, however, one discovers that the situation is not so simple. S u p p o s eone e l iminated a l l but one W o r d P r o c e s s o r s c las s , s ay in Onto logy 3 ‘llxn Onto logy I andOntoloW 2 would be missing a Word Procasors class. How can the idea that the concept @&Proce.ssors belongs in Ontology 1 and Ontology 2 be conveyed? How can one then represent anysubspecies of the concept Word Proce.wors in Ontology 1 and Ontology 2? The addition of anysuch assetiion on the Word Processors class in Ontology 3 would he irrelevant to Ontology 3and irrelevant to the class Word Pmce.wors as it relates to Ontology 3.

An approach using mapping between ontologies solves the context problem without eliminatingthe seemingly redundant cla.sses. A major advantage of this appmach i s tha t i t r equ i res nomodification to the ontology of each industry domain. This approach promotes modulari ty forthe ontologies in that they remain stand-alone for their respective industries. Another advantageto th i s approach i s tha t i t r e l i eves the domain expelt, who i s r e spons ib le fo r bu i ld ing andmaintaining a given indushy’s ontology, fmm having to deal with relationships of its classes toother ontologies: at least the tasks would be separate.

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The approach is defined as follows. First, we let ontologies remain as the domain experts havebuilt them. We then define mappings between relevant portions of two ontologies to express therelationship between the occurrences of the same concept in different contexts. By mapping froma given ontology to another ontology, we mean a function that takes way class in the sourceC’domain”) subontology of ontology A to classes in the target (“range”) subontology of ontologyB . Dy “subontology” we mean a subset of classes (with all their instances and relations) in agiven ontology that is closed under all relations, i.e. a subset that is itself an ontology (see Figure5).

Rmdhn, e.g. preserting“*,,brtasr-of? subclassof ,M 3rubctarr-or /N.

Figure 5. A Relations- (or rules-) Preserving Mapping f: M+N.

Moreover, such a mapping should preserve a defined set of relations and rules. For example, themapping can be defined to preserve directedpath (any two classes in its domain connected by adirected path to hvo clauses connected by a directed path thus preserving direction) or roofs (thedomain and range subontologies are t rees of classes with common roots and the mapping takesthe one to the other). Mappings express their semantics by this defined set of preserved relationsand rules. In par t icular , mappings tha t associa te a c lass in one contex t to i t s counte rpar t inanother def ine thei r se t appropr ia te ly . Under such a mapping the c lass W o r d Proce.ssors i nOntology 3 is mapped to its counterpart in Ontology I. Under another such mapping the classWordProcessors in Ontology 3 is mapped to its counterpart in Ontology 2.

Figure 6 shows a temporary implementat ion of path- and s lot- preserving mappings. Thisimplementation depends on semantically overloading the subclass-of relation between ontologies.At the time of this implementation, neither metaclasses nor rules were available in our ontologyeditor. Class labels follow a convention that would facilitate a subsequent transfomtation of thetemporay implementation into a more formal representation.

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!!IWdP_lm~f

,//

/ Ontology 3

\...Ontology 2

Ontology 1

Figure 6. An Implementation of a Relation-Preserving Mapping: an Example.

Moreover, preservation of rules and relations were to be enforced by human user policies untilthe ontology editor tool automated it: e.g., never link a descendant to an ancestor, always linkroot to root . Table 1 d isplays the beginning, in termedia te , and advanced implementa t ions ofFigure 5.

Table 1. An Implementation Plan for Figure 5 Using Ontology Editor

Version1 implementation: Overload subclass-of. User enforces relation-preserving propertiesaccording to documentation.

Version2 implementation (when meta-classes are available):Step 1. Define metaclass called “Dom( f)” by Dom( f) = { ITRoot, WordP } in Ontology 3.(This is M in diagram.) ( Note that “Dom( f )” is merely a label in this discussion. Nofunction Dom( ) need be defined.)

Step 2. Define metaclass called “Ran( f )” by Ran( f ) = { ITRootIm-f, WordPIm-f } inOntology 2. (This is N in diagram.)

Step 3. Define ontology called “View( Ontology 3 , Ontology 1 )” that uses both Ontology 3and Ontology 1. Then, create a slot called “f’ with domain Dom( f ) and range Ran( f ).

Define f by f = { (Ontology 3Root, Ontology 3Root Im-f), (WordP, WordPIm-f) }, i.e.attach the slot f to the domain class Dom( f ), and edit each instance of Dom( f ) by enteringthe target value horn Ran( f). For example, edit the instance Ontology 3Root of Dom( f) byentering the value Ontology 3Root-Im-f.

Version3 implementation (when rules are available):Step 4. Define rules .

For example, define Rule1 on f: If (X, Y) and (X, Z) are in f, then Y = Z. (I.e. the relation fis a function). For example, define Rule2 on f: If X * Y in A, then f(X) * f(Y) in B , where *denotes some binary relation on A and (by abuse of notation) the similarly defined binaryrelation on B.

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Defining a re la t ion-preserving funct ion between ontologies solves the problem of contexts . I tdoes not demand a g lobal perspec t ive of a l l on to logies in the knowledge base . Instead, thisapproach allows relationships to be expressed between local, i.e. context-based, classes. Thus,this constructivist bottom-up approach allows for gluing of ontologies wherever needed. Afterall, a class represents a concept in some given context. The global concept emerges as a result ofthe mappings . This approach in the spi r i t of [lo, 41 s teps away from the less maintainable andthus less scalable monolithic hierarchy.

4. Conclusions and Future ResearchWe have discussed in this paper the development of ontologies in the product and service spacefor B2B e-commerce. What we have termed the ontology mapping problem we consider to be inneed of a susta ined research program if tenable solut ions are to be found, s ince the lack of asolution will preclude widespread adoption of ontologies by the commercial world. It was thegreatest technical problem we faced. We believe this recapitulation of our experience developingan e-commerce application of ontological engineering to be of interest to the technicalcommunity because it highlights the difficult issues of adoption of ontological engineering by thecommercial world.

AcknowledgementsWe would l ike to express our apprecia t ion for the encouragement and suppor t we’ve receivedfrom many individuals , inc luding our col leagues Pat Cass idy, Lisa Colvin , Don McKay, MikeMolloy, Jon Pastor, Eric Peterson, Lori Wilson, Yao Zhu, Craig Schlenoff, Ugo Boggio, RandyClark, Frank Manshedi , Ashwin Par ikh, Kei th Thurs ton, Joe Whelan, Chr is ty Obrs t , and manyothers. We also note that the views expressed in this paper are those of the authors alone and donot ref lect the ofticial po l icy or pos i t ion of The MITRE Corporat ion or any other company orindividual . F inal ly , we wish to thank the anonymous reviewers for the i r cogent comments andsuggestions.

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