Realization of a User-friendly Access toNetworked Information Retrieval Systems

Hermann Helbig and Carsten GnSrlich and Dirk MenkeFernUniversit~it Hagen

LG Praktische Informatik VII/Kfinstliche Intelligenz58084 Hagen

hermann.helbiglcarsten.gnoerlichldirk.menke@fernuni-hagen.deh t tp: / / voss.fernuni-hagen.de / gebiete / pi 7 /papers / dag.ps(.gz

Abstract

The paper describes two methods for realizinga user-friendly access to distributed informa-tion resources. The first method (Method I) based on a ibrm-driven dialogue, which is usedin the project named "MEDOC". It aims at anexperienced user who is familiar with attributevalue structures of the data base schemes oftypical information retrieval systems (IRS) andwho knows the definition of boolean operators.The second method (Method II) applied in thesystem LINAS is from the very beginning ori-ented towards natural language communica-tion between end-users and IRSs. Both meth-ods can be used in an interface between theuser and an information brokering system help-ing him/her to find an appropriate informationprovider for his/her demands in networked in-formation systems. Method I gives the user acertain guidance in formulating his/her queriesbut has a restricted expressive power. It almostnever supports the user in automatically find-ing more complicated descriptional elements,as for instance classificators of a standardizedclassificational system. Method II, on the otherhand, is devoted to the "naive user" havingno experience with information retrieval tech-niques. Allowing for an unrestricted naturallanguage input, it is distinguished by a greaterexpressive power and gives valuable supportin automatically finding descriptors and clas-sificational categories used in the descriptionof documents. In comparison with Method I,there is less guidance in formulating the user’sdemands.

IntroductionIn general, the design of a data base retrievalinterface has to follow the same design goalsas any other modern, interactive software tool.The system should be easy to learn and shouldcommunicate with the user in a comprehensibleway.Another point to take into consideration is thata user’s preference with regard to the interfacemay change depending on his skills already ac-quired with the retrieval system. Just like agood text editor supplies a menu system for

novice users as well as an alternative system ofkeyboard sequences for the more experienced,the retrieval system should not only concentrateon maximum userfriendliness for beginners, butalso provide a user configurable expert mode forprofessional users.In contrast to a typical ’stand alone’ applica-tion like a word processor, it is also remarkablethat in general not all components of the re-trieval system run locally on the user’s machine.Typically, only the interface runs on the user’sown machine, while the data base is hosted ona dedicated data base server connected througha network. Ideally, the implementation detailsof the underlying data base and network systemare totally hidden from the end-user by creatinga uniform "look and feel" at the user interfacelevel. Therefore, the transformation from theuser’s query to the target system’s representa-tion has to be a process completely behind-the-scenes.However, presenting a uniform user interfacedoes not mean that also in a technical sensethere will be only one specific form of the userinterface -- it is very likely that users wish toconnect to the data base using a wide varietyof hard- and software components like Xll- andWindows-based platforms. Even non-graphicalterminals have to be put into consideration - apublic library might opt for VT100 terminalsas a cost-effective solution, while a natural lan-guage retrieval system could even be queriedthrough a normal telephone line using acousticlanguage recognition for input and speech syn-thesis for output.In the following we shall concentrate on theneeds of an end-user of an information retrievalsystem (IRS) with regard to the communicativeproperties of the system.

User requirements and twomethods of their fulfilment

The long-term aim in the development of an IRSis to provide an user-friendly access to the sys-tem not only for specialists but rather for the so-

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From: AAAI Technical Report SS-97-02. Compilation copyright © 1997, AAAI (www.aaai.org). All rights reserved.

called "naive users" having no special computereducation. Especially in the work with world-wide distributed IRS, it should be hidden fromthe user which information source finally ans-wers his/her informational demands. There aretwo principal approaches in realizing this aimwhich shall be explained and compared in thispaper.On the one hand it, is possible to equip tradi-tional access systems (especially already exist-ing network browsers) with more and more com-fortable and intelligent, user interfaces (MethodI). On tile other hand we can place the nat-ural language as a communicational means atthe disposal of the user (Method II). Both ap-i)roaches have to meet the following require-ments:

¯ Simplicity of operation (no special training,ergonomic design of the user interface)

¯ R.ealization of a largely natural dialogue (e.g.possibility of paraphrasing)

¯ Warranting of an adequate expressiveness ofthe query language (logical properties, per-missibility of vague terms)

¯ Allowance of references (from question toquestion, from question to foregoing answers)

¯ Multilinguality (independence of the languageof the information source)

¯ R.obustness and cooperativity (error correc-tion, dealing with acoustic similarity; observ-ing the correspondence between query typeand answer)

¯ Screening off the teclmical details (indepen-dence of the user from the knowledge aboutthe system).

The first way to meet these requirements(Method I) is followed in the System MEDOC(4). In this case the user is guided by a form-driven dialogue where the initiative stays withthe system.The user will be provided with a search maskshowing a fixed set of predefined attributesand a choice of logical operators to connectthe attribute-value combinations entered by theuser. The advantage of this approach consistsin tile explicit guidance of the user through thesystem while on tile other hand the predefinedmasks impose a certain inflexibility upon theuser. Each time he/she wants to switch to an-other domain of discourse the form shown andthe attribute set have to be changed. Within in-formationally similar domains of discourse, thesystem can be equipped with a standardized setof attributes (cf. (5)) but an entirely free logue using this method is not realizable. An-other problem arises from the need of establish-ing references between a question and foregoingquestions or answers. This possibility is rather

"Which h~mks Shorlliffc alxlut ncdical have hccnby CXpCrlSyslClllSpublished by Addison-Wesley sin~ 1090’!"

NL.proces.ving/

(Metlm~

((TYPE WH) (FOCUS G01) (REF NIL)

((SUB COl b~,k) (ORIG GOI Shor|liflk)

(MCONT G01 (*PROPA medical XPS))

(SUBS G(}2 ~blish) (OBJ G02

(AGT GO2 Addison-Wesley)

(STRT GO2 1990))

~Monn-d~iven dialogl.,elhod I)

((TYPE = I’~mk) AND (AUTHOR = Shorlliffc)AND

((SUBJECT = (Medicine. XPS))OR

(CLASSIFICATION = 1.2.1.5))AND

(PUBL = Atklison-Weslcy}AND

(DATE >= 1990))

f ALL lSELECr~ TITLE. YEARJ F~QM LITERATURE WtlERE

((DOCTYPE = BOOK)

(AUTHOR = "SHORTL.IFFE") AND

((DESCR ~ (MEDICINE; XPS)) OR (CLASS = 1.2.1.5))

(PUBL = ADDISON*WESLEY) AND

(YEAR >= 1~)0))

Figure 1: Comparison of different access meth-ods

restricted in using a form-driven dialogue. Byassigning unique indices to the answers of thesystem it is possible at least to establish ref-erences from questions to former answers (thistechnique, for instance, is applied in the IRSAIDOS (1)). To guarantee a certain multilin-guality with Method I, one has to embed ele-ments of linguistic data processing and multi-lingual thesauri into this surrounding which arescarcely available for most domains of discourseand which, after all, don’t fit very well into thismethod.The second way to fulfil the above mentionedrequirements (Method II) is oriented from thevery beginning towards the natural language asthe only means of communication a priori well-known to all users (this fact is especially im-portant for "naive users"). Method II is incor-porated in the system LINAS (3). Here, theadvantage of universality and great expressive-ness must be contrasted with the comparativelyhuge costs for the preparation of the necessarybackground knowledge (building of large com-puter lexicons and comprehensive grammarssuitable for automatic natural language process-ing). From the strategic point of view this is themost promising way to open a user-friendly ac-cess to the heterogeneous scene of world-widedistributed information sources for naive users,especially if we think of the acoustic channel of

63

I You {He/She/They}

t t x: <Deictic : Prenounp ,.

: asks shows I dclivcrs

-- mediate

Figure 2: Typical information stored in the di-alogue model

communication which is immanently connectedwith natural language (Method I is excluded perse if we want to realize an access to the Internetvia telephone). With Method II, we have to em-ploy the full range of NL processing techniquesstarting with morpho-syntactic analysis of theuser query, including its semantic interpreta-tion, and ending with a semantic representationof the question (cf. left side of fig. 1). Becauseof this algorithmic effort, the dialogical proper-ties of tile natural language interface (e.g. al-lowance of references and elliptical expressions)and the possibility of using paraphrases resultahnost automatically.

In comparison with that, the costs connectedwith Method I are not so high, because the in-put of the user can be more rigidly guided bythe form interface according to the syntacticpatterns of the formal retrieval language of thetypical target system (cf. sect. and right sideof fig. 1). While the natural language approachwithout doubt has the greater expressivity, theform-driven dialogue possibly shows the betteradequacy with regard to tile correspondence be-tween tile expressiveness of the query languageoffered to the user and the retrieval languageof typical target systems (cf. lower part of fig.1). Allowing for a natural language communica-tion, it is sometimes difficult to prevent the userfrom asking meta-questions like What informa-tion do you have at all? or Why can’t you find

anything?. On the other side, it is not so easywith intelligent broker systems having a certainamount of meta-knowledge to include specialmeans of expressivity into the form of Method Icorresponding just to this meta-knowledge (seesect. ).In the following we shall assume as a gen-eral scenario that an information mediating sys-tem (IMS) stands between user and informationprovider screening the user from the informationsource which satisfies the user’s demands.The user interfaces are running an a userclient where also the backgound knowledge forthe natural language processing (the so-calledcomputer lexicon) is stored. The informationsources (data bases) can be located at any placeachievable through the Internet and allowing ac-cess to the user via standardized retrieval lan-guages.

The role of background knowledgeTo translate the user queries into expres-sions understandable by whatever informationprovider reachable through a given network, theIMS has to have some background knowledgeor meta-knowledge about the information re-sources:

]Thesaurus[AI SYN ~ Artificial

Intelligence

Inference q ¯ ̄ ̄

1 Ts° XPS SYN _ Expert. - System

[Information DomainIArtificial Intelligence

[ Data Base Schema [

[Classification]1 Compu~tg Methodologlu Literature

i0Books Articles Leaflets

1.1 Algebraic Mauipu]ation

1.2 Artificial lntelligence Attributes [1.2.0 General Originator

1.2.1 Applications/Expert Systems1.2.1.1 Cartography

........~:~rsL2.1.2 Games1,2.1.3 Industrial Automation Author Publisher Editor Conference

1.2.1.5 Medicine and Science s ua su sUB

.~.eyer IJCA]

Figure 3: Background knowledge characterizingthe information content

1. Dialogue situation/dialogue model. At first,the IMS must have some general understand-

64

menu drive .......... ] ...... [’:: ki~@i:~lii~query T text string / ....................................................

!lngui~ti~ ~i~(i

~" ~1" gc%n~:na;i3;::" e x P r

Figure 4: The general process of transforming aquery

ing of the interrelations between the mainparticipants of the information exchange: theuser, the IMS itself, the provider and theirrespective instances as well as the objects ex-changed by them (information, money in caseof paid services etc.). The principal structureof such a dialogue model using the representa-tional means of semantic networks is shown infig. 2. An IMS with a natural language inter-face (NLI) must also have some special knowl-edge about the meaning of deictic pronouns (I- the user, you - the IMS, he - the provider)and of special verbs like "show", "know" etc.establishing the rhetoric context in a naturallanguage dialogue.

Also information about special providers be-longs to the dialogue model (" What type ofinformation can they deliver?", " What inJor-mation domain do they cover?"). The latterpart of the model is only sketched in fig. 2.

2. Meta-knowledge about the content of the infor-mation sources. The most important knowl-edge part needed for the transformation ofthe user queries into expressions of the re-trieval language of the provider DBMS is thedata base schema defining the structure ofthe stored information (cf. right side in fig.3) and the syntax of the retrieval languageitself. Together with taxonomic knowledge

about attributes and values (lower right sideof fig. 3) it is the main source for the trans-formation of the user query (see sect. ).

3. Thesaurus and classification. There are twocomponents of the stock of backgroundknowledge which can be characterized as con-ceptual ordering systems implicitly or explic-itly used in many IRS (left side in fig. 3).The first one, the so called thesaurus, con-tains the system of concepts describing theinformation domain (or the field of discourse).It is an alphabetically ordered set of conceptswhich are interrelated by certain lexical rela-tions (e.g. by SUB - conceptual subordina-tion, SYN - synonymy, ASSOC - association,PARS - part/whole relation and others). Theentries of the thesaurus are the potential key-words usable in the description of documentsin the data base (cf. attribute DESCR inthe data base schema of fig. 3). The sec-ond component often found in bibliographicinformation systems is the classification sys-tem. It comprises a hierarchically organizedsystem of non-lexicalized concepts associatedwith conveniently chosen rubricators defininga so-called decimal classification of informa-tional subfields. One typical example -- apart of the so-called CR-Classification (6) is shown in the lower left side in figure 3. Theentries of the classification system can be usedadditionally or alternatively to the descriptors(attribute DESCR) for the characterization the documents in the data base (cf. attributeCLASS in the data base schema of fig. 3).

4. Organizational and commercial information.For the sake of completeness we have to men-tion a fourth part of background knowledgewhich concerns the technical and economi-cal aspect of the communication with theprovider. This part comprises informationabout access rights, accounting, passwordsand so on which rule the physical access to aspecial data base as well as information aboutcosts and modalities of payment in the case ofcommunication with commercial data bases.This aspect will not be deepened in this pa-per.

Summarizing, we can state that the dialoguemodel (1) is needed to find the appropriate in-formation provider for a given user demand andto discern between dialogical or rhetoric phraseswithin the user queries and their informationalkernel proper. The meta-knowledge about thecontent of the target data bases (2) and theclassification systems (3) are important for thetransformation of the user query into expres-sions of the retrieval language of the providerDBMS. From this point of view, the transfor-mations Tr described in sect. realize a binaryfunction Tr : Q x M --+ R mapping queries

65

Figure 5: The html query page

Q of tile user by means of the meta-knowledgeM into the retrieval language R of the targetsystem. Finally, the organizational and com-mercial data (4) are necessary to manage thetransactions between end-user and informationprovider and thereby to survey the observationof financial, juridical, and technical agreements.

The internal representation of theuser query

This section deals with the problems connectedwith the "inner representation" of user demandsand their translation into the retrieval languageof typical target systems. According to the dif-ferent ways of formulating a user query we shalldivide our considerations into two parts. In thecase of natural language access, semantic net-work concepts are applied to represent formallythe content of the question. In the other casean internal retrieval language (IRL) is used for

this purpose.Nevertheless, there are some common featuresconcerning the general structure of the trans-formation procedures which are similar in bothcases. The main steps in the transformationprocess are illustrated in figure 4.

The representation of form-orientedqueries

A query specified by different terms in a formlike manner (shown in fig. 5) is translated intothe internal retrieval language IRL. This innerrepresentation follows the pattern of a possiblylarge class of target languages. Because of that,the transformation steps needed for Method Iare not so complicated compared with naturallanguage input.The IRL expressions are essentially constructedin terms of attribute-value-pairs defined bymeans of regular expressions shown in fig. 6.

66

<query>

<expression><rel><attribute>

< s_attr ibute ><n_attribute>

<value><string><number >

<vagueness><vag_value>

::= (<expression>)

I (<expression> [<and> <expression>]*)

I (<expression> [<or> <expression>I*)

::= [not] (<attribute> <tel> <value> [<vagueness>I).... I < I > I <= I >=::---- <s_attribute> I <n_attribute>

::= AUTHOR i TYPE i SUBJECT I DATE I PUBL i::= YEAR i MONTH I ...::= "<string>" I <number>::= [O-9a-zA-Z-_. ! ?a%]*::= [-]?[0-9]+[.]?[0-9]*::= [<vag_value>]?::= similar I optional I uncertain

Figure 6: Regular expressions used in the IRL

In this definition, an infix notation is used be-cause several target languages, as for instanceSQL, use it too. The language defined above al-lows to express the vagueness of special terms.If no vague terms are contained in a query, allterms have to be treated as reliable information.

The semantic structure of thenatural language query

When using the natural interface method, thetransformation of the user query is carried outin two distinct steps. Firstly, a linguistic pro-cessor analyzes the natural language expres-sion from a grammatical and semantic point ofview, thereby producing an intermediate seman-tic representation. Secondly, this representationis processed by a transformation module whichgenerates a query in the language of the under-lying data base system (which will be SQL inour example).The semantic representation language describesthe relations between the concepts contained inthe natural language query. Each concept isrepresented as a node in a semantic net (SN)whose expressional means are taken from theMESNET paradigm described in (7). A fixedset of predefined relations and functions (repre-sented as edges in the SN) is used to describe theinterrelationships between two (or more) nodes.To give some idea of the semantic representationprinciples, we will discuss the meaning of thesemantic representation of the user query shownin figure 1.The overall structure of the semantic represen-tation can be seen as a bracketed structure con-taining relational and functional terms as subex-pressions. Nodes are either labelled by artifi-cially created symbols like GO1, GO2 or namedafter the corresponding concept, if such a con-cept/name mapping is unique.In figure 7, a summary of the relations andfunctions used throughout the example isgiven. Functions are discerned from relations by

putting an asterisk ("*") in front of their name.In the semantic net, a relation (REL a b) will represented as an edge labelled "REL" betweentwo nodes "a" and "b".

Transformation

The transformations of the user queries have tobe differentiated according to the two principalmethods:

Method I: using a form with different fields asquestion pattern;

Method II: allowing for natural language inputfor the user query.

To illustrate the process of transforming queries,the following example sentence will be used:

Which books by Shortliffe about medicalexpert systems have been published byAddison-Wesley since 1990?

Having started his/her WWW client, the useressentially receives the page shown in fig. 5,but with empty input fields.Employing Method I, first the attributes have tobe chosen from a pull down menu showing up onthe screen after pushing the ’attribute’ button.Then the user has to enter the attribute valuesinto the corresponding value fields. Figure 5shows a form which is already filled out (the en-tries in the fields above and below the horizontalline have to be understood as alternatives).In Method II, the query is accepted in naturallanguage, which can be typed into the corre-sponding input field above the horizontal line.

Form-oriented query

In the case of a form-oriented input, it is neces-sary for the user to structure his query intellec-tually and to enter the appropriate terms intothe masks defined by the menu. Thus, a part ofthe transformation process will be passed to theuser.

67

(TYPE t)(REF val)(FOCUS n)

specification of the question typereference to an antecedent nodenode n is the primary target of the query

(AGT h a)(MCONT s i)(OBJ s b)

(OIl.IG ol o2)(STILT s t)(SUB ol 02)(SUBS sl s2)

node a refers to an agent who is performing some action h.node i refers to the mental content of a cognitive situation s.b refers to an object which is involved in a situation s,but is not physically affected by s.o2 describes the origin of ol.the situation or action s starts at the time given by node t.object ol is subordinated to object o2.sl is subordinated to s2,with sl, s2 describing situations or actions.

(*PROPA p o) = construction of a new node c by modificationof an object o with an associative property p

Figure 7: An excerpt from the relations and functions provided in the semantic representation lan-guage.

In our example we especially asked for books,a value which the user can specify but whichperhaps will not occur in the data base. In theworst case, if there is no additional backgroundinformation, the user gets no information at all.In the other case, if the transformation proce-dure has access to a predefined concept hierar-chy where the concept "book" is subordinatedto the concept "literature", and the superor-dinated value "literature" is integrated in thequery, the user will get too much information.After completing the specification of the queryby means of a form, the query has to be trans-lated into the unified internal language IRL. Theuse of this intermediate language is especiallyadvantageous when the IMS is supported by anassembly of brokers mediating tile communica-tion between user and information provider (asit is the case with the MEDOC system (4)). this case, the ItlL not only defines the interfacebetween user and provider but it is also used asmeans of communication between different bro-kers.The translation of the query can be supportedby means of CGI scripts, where every input fieldis evaluated immediately after entering a newitem into that field. The information specifiedwill be integrated successively into the inner rep-resentation. During this procedure, the logicaloperators have to be taken into account. Theyare inserted into the formal IRL expression ac-cording to the syntax given in section . Afterevaluating the input form, the query will havetim following structure1:

1The specification of the classification attributeCLASS will be omitted here for the sake of brevity

( (TYPE = "book") (AUTHOR = "Shortliffe") AND(PUBL = "Addison-Wesley") AND(SUBJECT = "medical expert system") AND(DATE >= 1990)

To bridge the gap between IRL and the retrievallanguage of a target system, the IMS has totake into consideration the synonymy betweenthe attribute names of both languages. Let’sassume the user wants to express that the manwho wrote a book is named "Shortliffe". If theform has an input field named "author" andthe target data base contains only attributeslike "writer" or just an abbreviation like "auth",there has to be a mapping between the form at-tribute (e.g. "author") and the target attribute(e.g. "auth") which can be used by the trans-formation process. For that purpose, we canuse the information contained in a thesaurus.In the case of abbreviations, the transformationprocess has to be supported by translation rules.Other difficulties are connected with concep-tual hierarchies of attribute or value concepts(cf. fig. 3). If, for instance, the query asksfor Person = "Shortliffe" and the data basecontains the attributes "Author", "Editor" and"Publisher", subsumed by "Person" (cf. fig.3),the transformation has to build a disjunction ofthree expressions: (Author = "Shortliffe")V (Editor = "Shortliffe") V (Publisher= "Shortliffe").An analogous situation can be found in thecontext of values. For example, if somebodysearches for document type "literature" havingin the data base scheme only the three fixed val-ues "Books", "Articles", "Leaflets" which are

68

searchRequest = { smallSetUpperBound = 1 ... ;;some more SR/Z39.50 specific termsquery = { type-i = { attributeSet = 0ID 1.2 ..... RPN = { rpnop = {RPNI = { op = { attrTerm = { attributes = { relation = Equal

use = Name-publisher} term = { general = "Addison Wesley" } } } }

RPN2 = { op = { attrTerm = { attributes = { relation = greaterThan0rEqualuse = Date-publication } term = { numeric = 1990 } } } }

op = { and } } } ... } }

Figure 8: An excerpt from the Z39.50 query

subordinated to "Literature" (cf. fig. 3), thequery has to be transformed in the followingway:The original query in IRL:

( (TYPE = "literature") AND(AUTHOR = "Shortliffe") AND(PUBL = "Addison-Wesley") AND(SUBJECT = "medical expert system")

AND (DATE >= 1990)

The transformed query:

( ((TYPE = "book") (TYPE = "article") OR(TYPE = "leaflet"))

(AUTHOR = "Shortliffe") AND(PUBL = "Addison-Wesley") AND(SUBJECT = "medical expert system")AND (DATE >= 1990)

After solving the attribute transformation prob-lem, the query has to be translated into the tar-get language.In the last section, we have concentrated onSQL as a target language, in the following wewant to discuss the translation of the user queryinto the Z39.50 protocol, which is a standardclient/server protocol for information retrievalsystems (8). In contrast to SQL, there is need for an extra hierarchy of attributes if oneuses the Z39.50 protocol. In the often appliedbib-1 attribute set associated with Z39.50, theattribute "Author-name" can have the follow-ing meanings: "person", "corporate author" oreven "conference name". In this case, a statictransformation table will be sufficient. For moredetails see (8).The Z39.50 is a session oriented network pro-tocol, where the query has to be sent to thedata base during an open session. Every mes-sage transferred within the session is called aPDU (Protocol Data Unit). A search querywithin the Z39.50 consists of several so-calledrpn (reversed polish notation expressions) con-nected by logical operations.Here, tile meta-symbol <RELATION> indi-cates one of the comparison operators admis-sible in Z39.50 which can be derived from theinner representation language IRL by means ofspecial translation rules: (RELATION("=") "equal", RELATION("<") := "lessThan",

RELATION(">=") "great erThan0rEqual" ).The "term" <VALUE_TYPE> = <VALUE>specifies the value of <ATTRIBUTE_N> to-gether with the value domain belonging to this

attribute. <ATTRIBUTE_N> characterizes aspecial attribute from depth N within a givenattribute hierarchy supported by the Z39.50protocol. After the transformation, the queryhas the structure shown in fig. 82.This query is written in the ASNI (ISO 8824),the Abstract Syntax Notation, a high-level data

structuring language belonging to the Z39.80protocol.

After completing the corresponding transforma-tions, the query has a numerically encoded form:

\270\020\237\037\007 ,

which can be sent to the Z39.50 server.

The natural language query

After entering the natural language query, thelinguistic processor translates it into the inter-mediate semantic representation; see figure 9 forthe semantic net corresponding to the examplequery used throughout this paper.

book Shortllffe XPS

SU/4

, ~ ,..~ medical

<focus> ~ ~----1~’’r

MCONT

\ ~ publish

~ SUBS

A21:~n~__~AGT ~’------m’- 1990

Figure 9: The semantic net representation ofthe example query

The transformation takes place after the linguis-tic processor has done its work.At this stage,

2Only a few typical parts of the query structureare described in the example because the whole ex-pression is too long to be printed here.

59

Rules:(Convention: Lower case symbols denote variables. All variables with the same symbol are instantiatedwith the same value throughout the rule set.)

rl: (q SUB BOOK) A (q ORIG a) --+ (a SUB AUTHOR) V (a SUB

r2: (q SUB

r3: (q SUB

r~: (a SUB

7’5: (a SUB

r6: (s SUB

r7: (p

7"8: (or9: (q

Facts:

BOOK) A (q MCONT s) --+ (s SUB SUBJECT)

BOOK) A (o OBJ q) A (o SUBS "publish") A (o --+ (p SUB PUBLISHER)

AUTHOR) --+ {SQL-Expression: "Author = a"}

EDITOR,) --+ {SQL-Expression: "Editor = a"}

SUBJECT) -+ {SQL-Expression: "Descr = s" V "Class = f(s)")

SUB PUBLISHER) -+ {SQL-Expression: "Publisher = p"}

SUBS "publish") A (o STRT t) --+ {SQL-Expression: "Year >= t’}

SUB BOOK) --+ {SQL-Expression: "Doctype = ’BOOK’"}

("Shortliffe" SUB AUTHOR), ("Myers" SUB AUTHOR),...

Figure 10: An excerpt from the transformation knowledge base

the basic information about the query is alreadymade explicit in the semantic net.For example, the linguistic preprocessor has al-ready determined on which object the query isfocussing. Also the interrelationships betweenthe components of the query have been trans-lated into the relational structure of the seman-tic net. The node labelled <focus> representsthe focus of the query.The transformation module’s task is to deter-mine the exact pragmatic interpretation of theSN using the background knowledge of fig. 10.At the beginning of this interpretation process,the edges directly connected to the focus nodeof the query are examined. Following the ’SUB’edge in the example from fig. 9, the transfor-mation process gets more detailed informationat>out the subject of the query, which is subor-dinated to the concept "book" in this case, andwhich results in (Doctype = Book) because rule r9.

It can be further deduced that the ’ORIG’ (ori-gin) edge must either point to an "author" orall "editor" (according to rule rl). If it known that "Shortliffe" qualifies as an author,an (Author = "Shortliffe") part of the SQLquery can be derived using rule rg; otherwise adisjoint term has to be generated according torules rl, r4, and r5.

Similar conclusions can be drawn for the’MCONT’ edge. Here the system finds outthat the mental content of a book is relatedto some subject information (rule r2). Asa consequence, applying rule r6 yields "De-

scr" or "Class" as a matching attribute for thesubject of the book. This leads to the ex-pression (Descr = "medical XPS") Y (Class= "I.1.2.1.5") in the resulting query. Thebuilt-in function f realizes a mapping fromthe verbal description "medical XPS" into therubricators of the CR-classification.

After having outlined the general idea of thetransformation process, we would like to pointout some additional difficulties which have to betaken into account when performing the trans-formation.For example, a problem likely to arise is thatthe user is paraphrasing some elements of hisquery, e.g. he might say "medical expert sys-tem" as well as "expert system for medicine".The semantic equivalence of such paraphrasesis automatically discovered during the semanticinterpretation by using so-called meaning postu-lates. In some cases, also different relations maybe selected from the linguistic preprocessor for acertain component, e.g. choosing "ORIGL Lon-don" instead of "ORIG London", because thereis possibly no background knowledge to decide ifLondon is a location (ORIGL - local source) a human being (ORIG - mental source). Suchambiguities are unavoidable since some relationsare difficult to distinguish automatically. Thiseffect can be covered by adding disjoint expres-sions (01%IGL q a) V (01%IG q a) to the query.

Altogether, the rule applications outlined abovegive the SQL expression (for the sample query)shown in figure II.Finally, it should be mentioned that the trans-

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SELECT * FROM BookWHERE Doctype = "Book"

AND Author = "Shortliffe"AND ((Descr = "medical XPS")

OR (Class = "I.i.2.1.5"))AND Publisher = "Addison-Wesley"AND Year >= 1990

Figure 11: SQL expression for the sample query.

formation is also assisted by a dialogue mem-ory, which contains the semantic structure of themost recent queries from the user. Informationfrom the dialogue memory is especially usefulwhen the user is entering incomplete or ellip-tical sentences like "And which be]ore 19907".If this sentence is following our initial example(" Which book by Shortliffe..."), then the systemcan fill in the missing attributes like "author --Shortliffe" etc. from the foregoing question inthe dialogue memory.

Present state and futuredevelopment

From the two methods described in the paper,natural language access to IRS and form-drivendialogue, the first (Method I) is followed in theproject MEDOC sponsored by the Federal Min-istry of Education and Research (BMBF). Oneimportant aim of thc MEDOC project is to pro-vide an intelligent brokering system to supportthe user in finding the needed information in aworld-wide distributed and networked system ofiuibrmation sources.The second way (Method II) is followedin the LINAS project (Literaturrecherche innatfirlicher Sprache, literature retrieval in nat-ural language) of the University Hagen. A firstprototype of the natural language interface ofthe system is already used in connection withthe target IRS AIDOS for managing and access-ing the AI literature of the local library held inthe AI lab.It is planned to merge these different approachesinto one system (i.e. into the above mentionedsystem LINAS) therewith combining the advan-tages of natural language communication withIRSs and the strategically important support foraccess to information systems distributed in in-ternational computer networks.

http ://is6-www. inf ormatik, uni-dortmund, de/projects/MEDOC/broker/intern/architecture, ps.

[3] Helbig, H.; Mertens, A.: "Der Ein-satz von Wortklassen fiir die automatischeSprachverarbeitung"; Informatik Berichte158; FernUniversitit Hagen; 1994, Tell 1;0berblick fiber das Gesamtsystem; chapter2.1, p 4 ft.

[4] Brfiggemann-Klein, A. et al.: "Entwick-lung und Erprobung oftener volltext-basierter Informtionssysteme fiir die Infor-matik"; 1995;http ://medoc. informatik, tu-muenchen, de/publications/ovid_l, ps.

[5] CNIDR: "The scientific and technical at,-tribute and element set";http ://stas. cnidr, org/STAS/.

[6] ACM: "Computing Classification Sys-tem"; (formerly Computing Reviews)http ://www. acm. org/class/.

[7] Helbig, H.; Schuh, M: "Knowledge repre-sentation with MESNET - A MultilayeredExtended Semantic Network"; InformatikBerichte 185; FernUniversit~t Hagen; 1996http ://voss. fernuni-hagen, de/

gebiete/piT/papers/ibmesnet, ps

[8] ANSI-NISO: "Information Retrieval: Ap-plication Ser-vice Definition and Protocol Specification";ftp ://ftp. loc. gov/pub/z3950/of f ical/part 1.ps -- part4.ps

References[1] Helbig, H. et al.: "Tim natural language

interface NLI-AIDOS"; in J.Newer Gener.Computer Syst. 3 (1990) 3; Akademie-Verlag, Berlin; p. 221-246.

[2] Fuhr, N.; GroBjohann, K.: "Broker Archi-tecturc"~ MeDOC internal report;

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