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    CHAPTER 1

    INTRODUCTION

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    CHAPTER 1 : INTRODUCTION

    1.1 Background

    On the last decades, the amount of web-based information available has increased

    dramatically. ow to !ather useful information from the web has become a

    challen!in! issue for users. Current web information !atherin! systems attem"t to

    satisfy user re#uirements by ca"turin! their information needs. $or this "ur"ose, user

    "rofiles are created for user bac%!round %nowled!e descri"tion.

    User "rofiles re"resent the conce"t models "ossessed by users when !atherin!

    web information. & conce"t model is im"licitly "ossessed by users and is !enerated

    from their bac%!round %nowled!e. 'hile this conce"t model cannot be "roven in

    laboratories, many web ontolo!ists have observed it in user behaviour. 'hen users

    read throu!h a document, they can easily determine whether or not it is of their

    interest or relevance to them, a (ud!ment that arises from their im"licit conce"t

    models. If a user)s conce"t model can be simulated, then a su"erior re"resentation of

    user "rofiles can be built.

    To simulate user conce"t models, ontolo!ies*a %nowled!e descri"tion and

    formali+ation model*are utili+ed in "ersonali+ed web information !atherin!. uch

    ontolo!ies are called ontolo!ical user "rofiles, or "ersonali+ed ontolo!ies. To

    re"resent user "rofiles, many researchers have attem"ted to discover user bac%!round

    %nowled!e throu!h !lobal or local analysis.

    lobal analysis uses eistin! !lobal %nowled!e bases for user bac%!round

    %nowled!e re"resentation. Commonly used %nowled!e bases include !eneric

    ontolo!ies /e.!., 'ordNet0, thesauruses /e.!., di!ital libraries0, and online %nowled!e

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    bases /e.!., online cate!ori+ations and 'i%i"edia0. The !lobal analysis techni#ues

    "roduce effective "erformance for user bac%!round %nowled!e etraction. owever,

    !lobal analysis is limited by the #uality of the used %nowled!e base. $or eam"le,

    'ordNet was re"orted as hel"ful in ca"turin! user interest in some areas but useless

    for others.

    1ocal analysis investi!ates user local information or observes user behaviour

    in user "rofiles. $or eam"le, 1i and 2hon! discovered taonomical "atterns from the

    users) local tet documents to learn ontolo!ies for user "rofiles. ome !rou"s learned

    "ersonali+ed ontolo!ies ada"tively from user)s browsin! history. &lternatively, e%ine

    and u+u%i analysed #uery lo!s to discover user bac%!round %nowled!e. In some

    wor%s, users were "rovided with a set of documents and as%ed for relevance feedbac%.

    User bac%!round %nowled!e was then discovered from this feedbac% for user "rofiles.

    owever, because local analysis techni#ues rely on data minin! or classification

    techni#ues for %nowled!e discovery, occasionally the discovered results contain noisy

    and uncertain information. &s a result, local analysis suffers from ineffectiveness at

    ca"turin! formal user %nowled!e.

    $rom this, we can hy"othesi+e that user bac%!round %nowled!e can be better

    discovered and re"resented if we can inte!rate !lobal and local analysis within a

    hybrid model. The %nowled!e formali+ed in a !lobal %nowled!ebase will constrain

    the bac%!round %nowled!e discovery from the user local information. uch a

    "ersonali+ed ontolo!y model should "roduce a su"erior re"resentation of user "rofiles

    for web information !atherin!.

    In this "ro(ect, an ontolo!y model to evaluate this hy"othesis is "ro"osed. This

    model simulates users) conce"t models by usin! "ersonali+ed ontolo!ies, and attem"ts

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    to im"rove web information !atherin! "erformance by usin! ontolo!ical user "rofiles.

    The world %nowled!e and a user)s local instance re"ository /1IR0 are used in the

    "ro"osed model. 'orld %nowled!e is common sense %nowled!e ac#uired by "eo"le

    from e"erience and education3 an 1IR is a user)s "ersonal collection of information

    items. $rom a world %nowled!e base, we construct "ersonali+ed ontolo!ies by

    ado"tin! user feedbac% on interestin! %nowled!e. & multidimensional ontolo!y

    minin! method, "ecificity and 4haustivity, is also introduced in the "ro"osed model

    for analy+in! conce"ts s"ecified in ontolo!ies. The users) 1IRs are then used to

    discover bac%!round %nowled!e and to "o"ulate the "ersonali+ed ontolo!ies. The

    "ro"osed ontolo!y model is evaluated by com"arison a!ainst some benchmar%

    models throu!h e"eriments usin! a lar!e standard data set. The evaluation results

    show that the "ro"osed ontolo!y model is successful.

    1.2 Report Organiation

    Cha"ter 5 discusses the "roblem definition and "rovides the bac%!round %nowled!e.

    Cha"ter 6 contains 1iterature urvey.

    Cha"ter 7 discusses the desi!n of the system.

    Cha"ter 8 !ives the details of the technolo!ies used.

    Cha"ter 9 "rovides the im"lementation details.

    Cha"ter : contains e"erimental results.

    Cha"ter ; !ives the conclusion on the wor% carried out and future sco"e.

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    CHAPTER 2

    1IT4R&TUR4 UR

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    CHAPTER 2: !ITERATURE "UR#E$

    2.1 Onto%og& !earning

    lobal %nowled!e bases were used by many eistin! models to learn ontolo!ies for

    web information !atherin!. $or eam"le, auche et al. and ie!e et al. learned

    "ersonali+ed ontolo!ies from the O"en Directory >ro(ect to s"ecify users) "references

    and interests in web search. On the basis of the Dewey decimal classification, ?in! et

    al. develo"ed IntelliOnto to im"rove "erformance in distributed web information

    retrieval. 'i%i"edia was used by Downey et al. to hel" understand underlyin! user

    interests in #ueries. These wor%s effectively discovered user bac%!round %nowled!e3

    however, their "erformance was limited by the #uality of the !lobal %nowled!e bases.

    &imin! at learnin! "ersonali+ed ontolo!ies, many wor%s mined user

    bac%!round %nowled!e from user local information. 1i and 2hon! used "attern

    reco!nition and association rule minin! techni#ues to discover %nowled!e from user

    local documents for ontolo!y construction. Tran et al. translated %eyword #ueries to

    Descri"tion 1o!ics) con(unctive #ueries and used ontolo!ies to re"resent user

    bac%!round %nowled!e. 2hon! "ro"osed a domain ontolo!y learnin! a""roach that

    em"loyed various data minin! and natural-lan!ua!e understandin! techni#ues.

    Navi!li et al. develo"ed Onto1earn to discover semantic conce"ts and relations from

    web documents. 'eb content minin! techni#ues were used by @ian! and Tan to

    discover semantic %nowled!e from domain-s"ecific tet documents for ontolo!y

    learnin!. $inally, hehata et al. ca"tured user information needs at the sentence level

    rather than the document level, and re"resented user "rofiles by the Conce"tual

    Ontolo!ical ra"h. The use of data minin! techni#ues in these models leads to more

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    user bac%!round %nowled!e bein! discovered. owever, the %nowled!e discovered in

    these wor%s contained noise and uncertainties.

    &dditionally, ontolo!ies were used in many wor%s to im"rove the "erformance

    of %nowled!e discovery. Usin! a fu++y domain ontolo!y etraction al!orithm, a

    mechanism was develo"ed by 1au et al. in 6AAB to construct conce"t ma"s based on

    the "osts on online discussion forums. uest and &li used ontolo!ies to hel" data

    minin! in biolo!ical databases. @in et al. inte!rated data minin! and information

    retrieval techni#ues to further enhance %nowled!e discovery. Doan et al. "ro"osed a

    model called 1U4 and used machine learnin! techni#ues to find similar conce"ts in

    different ontolo!ies. Dou et al. "ro"osed a framewor% for learnin! domain ontolo!ies

    usin! "attern decom"osition, clusterin!classification, and association rules minin!

    techni#ues. These wor%s attem"ted to e"lore a route to model world %nowled!e

    more efficiently.

    2.2 U'er Pro(i%e'

    User "rofiles were used in web information !atherin! to inter"ret the semantic

    meanin!s of #ueries and ca"ture user information needs. User "rofiles were defined

    by 1i and 2hon! as the interestin! to"ics of a user)s information need. They also

    cate!ori+ed user "rofiles into two dia!ramsE the data dia!ram user "rofiles ac#uired

    by analy+in! a database or a set of transactions3 the information dia!ram user "rofiles

    ac#uired by usin! manual techni#ues, such as #uestionnaires and interviews or

    automatic techni#ues, such as information retrieval and machine learnin!.

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    al. used a collection of user des%to" tet documents and emails, and cached web "a!es

    to e"lore user interests. Fa%ris et al. ac#uired user "rofiles by a ran%ed local set of

    cate!ories, and then utili+ed web "a!es to "ersonali+e search results for a user. These

    wor%s attem"ted to ac#uire user "rofiles in order to discover user bac%!round

    %nowled!e.

    User "rofiles can be cate!ori+ed into three !rou"sE interviewin!, semi-

    interviewin!, and non-interviewin!. Interviewin! user "rofiles can be deemed "erfect

    user "rofiles. They are ac#uired by usin! manual techni#ues, such as #uestionnaires,

    interviewin! users, and analy+in! user classified trainin! sets. One ty"ical eam"le is

    the TR4C $ilterin! Trac% trainin! sets, which were !enerated manually. The users

    read each document and !ave a "ositive or ne!ative (ud!ment to the document a!ainst

    a !iven to"ic. Gecause, only users "erfectly %now their interests and "references,

    these trainin! documents accurately reflect user bac%!round %nowled!e. emi-

    interviewin! user "ro-files are ac#uired by semi-automated techni#ues with limited

    user involvement. These techni#ues usually "rovide users with a list of cate!ories and

    as% users for interestin! ornon-interestin! cate!ories. One ty"ical eam"le is the web

    trainin! set ac#uisition model introduced by Tao et al., which etracts trainin! sets

    from the web based on user fed bac% cate!ories. Non-interviewin! techni#ues do not

    involve users at all, but ascertain user interests instead. They ac#uire user "rofiles by

    observin! user activity and behaviour and discoverin! user bac%!round %nowled!e. &

    ty"ical model is OGI'&N, "ro"osed by auche et al., which ac#uires user "rofiles

    based on users) online browsin! history. The interviewin!, semi-interviewin!, and

    non-interviewin! user "rofiles can also be viewed as manual, semiautomatic, and

    automatic "rofiles, res"ectively.

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    2.) *%o+a% ,no-%edge Ba'e'

    'orld %nowled!e is im"ortant for information !atherin!. &ccordin! to the definition

    "rovided by, world %nowled!e is common sense %nowled!e "ossessed by "eo"le and

    ac#uired throu!h their e"erience and education. &lso, as "ointed out by Nierenber!

    and Ras%in, Hworld %nowled!e is necessary for leical and referential disambi!uation,

    includin! establishin! reference relations and resolvin! elli"sis as well as for

    establishin! and maintainin! connectivity of the discourse and adherence of the tet to

    the tet "roducer)s !oal and "lans. In this "ro"osed model, user bac%!round

    %nowled!e is etracted from a world %nowled!e base encoded from the 1ibrary of

    Con!ress ub(ect eadin!s /1C0.

    'e first need to construct the world %nowled!e base. The world %nowled!e

    base must cover an ehaustive ran!e of to"ics, since users may come from different

    bac%!rounds. $or this reason, the 1C system is an ideal world %nowled!e base.

    The 1C was develo"ed for or!ani+in! and retrievin! information from a lar!e

    volume of library collections. $or over a hundred years, the %nowled!e contained in

    the 1C has under!one continuous revision and enrichment. The 1C re"resents

    the natural !rowth and distribution of human intellectual wor%, and covers

    com"rehensive and ehaustive to"ics of world %nowled!e. In addition, the 1C is

    the most com"rehensive non-s"eciali+ed controlled vocabulary in 4n!lish. In many

    res"ects, the system has become a de facto standard for sub(ect catalo!in! and

    indein!, and is used as a means for enhancin! sub(ect access to %nowled!e

    mana!ement systems.

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    Table 1

    Comparison of Different World Taxonomies

    !C"H !CC DDC RC

    J of To"ics 7B8,A;A 8,658 5K,8:6 5AA,AAA

    tructure Directed

    &cyclic ra"h

    Tree Tree Directed

    &cyclic ra"h

    De"th 7; ; 67 5A

    emantic

    Relations

    Groader, Used-

    for, Related-to

    u"er- and

    ub- class

    u"er- and

    ub- class

    u"er- and

    ub- class

    The 1C system is su"erior com"ared with other world %nowled!e taonomies used

    in "revious wor%s. Table 5 "resents a com"arison of the 1C with the 1ibrary of

    Con!ress Classification /1CC0 used by $ran% and >aynter , the Dewey Decimal

    Classification /DDC0 used by 'an! and 1ee and ?in! et al., and the reference

    cate!ori+ation /RC0 develo"ed by auch et al. usin! online cate!ori+ations. &s shown

    in Table 5, the 1C covers more to"ics, has a more s"ecific structure, and s"ecifies

    more semantic relations. The 1C descri"tors are classified by "rofessionals, and

    the classification #uality is !uaranteed by well-defined and continuously refined

    catalo!in! rules. These features ma%e the 1C an ideal world %nowled!e base for

    %nowled!e en!ineerin! and mana!ement.

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    CHAPTER )

    D4IN

    CHAPTER ): DE"I*N

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    ).1or%d ,no-%edge Repre'entation

    The structure of the world %nowled!e base used in this "ro(ect is encoded from the

    1C references. The 1C system contains three ty"es of referencesE Groader term

    /GT0, Used-for /U$0, and Related term /RT0 . The GT references are for two sub(ects

    describin! the same to"ic, but at different levels of abstraction /or s"ecificity0. In our

    model, they are encoded as the is-a relations in the world %nowled!e base. The U$

    references in the 1C are used for many semantic situations, includin! broadenin!

    the semantic etent of a sub(ect and describin! com"ound sub(ects and sub(ects

    subdivided by other to"ics. The com"le usa!e of U$ references ma%es them difficult

    to encode. Durin! the investi!ation, we found that these references are often used to

    describe an action or an ob(ect. 'hen ob(ect & is used for an action, & becomes a "art

    of that action /e.!., Ha for% is used for dinin!03 when & is used for another ob(ect, G,

    & becomes a "art of G /e.!., Ha wheel is used for a car0. These cases can be encoded

    as the "art-of relations. Thus, we sim"lify the com"le usa!e of U$ references in the

    1C and encode them only as the "art-of relations in the world %nowled!e base.

    The RT references are for two sub(ects related in some manner other than by

    hierarchy. They are encoded as the related-to relations in our world %nowled!e base.

    The "rimitive %nowled!e unit in our world %nowled!e base is sub(ects. They

    are encoded from the sub(ect headin!s in the 1C. These sub(ects are formali+ed as

    followsE

    Definition 1.Let be a set of subjects, an element s is formalized as a 4-tuple

    s := label,nei!"bor,ancestor,descendant#, $"ere

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    Label is t"e "eadin! of s in t"e LC%& t"esaurus' (ei!"bour is a function returnin! t"e subjects t"at "a)e direct lin*s to s in t"e

    $orld *no$led!e base' +ncestor is a function returnin! t"e subjects t"at "a)e a "i!"er le)el of

    abstraction t"an s and lin* to s directl or indirectl in t"e $orld *no$led!e

    base' Descendant is a function returnin! t"e subjects t"at are more specific t"an s

    and lin* to s directl or indirectl in t"e $orld *no$led!e base'

    The sub(ects in the world %nowled!e base are lin%ed to each other by the

    semantic relations of is-a, "art-of, and related-to. The relations are formali+ed as

    followsE

    Definition 2. Let be a set of relations, an element r is a -tuple r :=

    ed!e,tpe#, $"ere

    +n ed!e connects t$o subjects t"at "old a tpe of relation' + tpe of relations is an element of .is-a, part-of, related-to/0

    'ith Definitions 5 and 6, the world %nowled!e base can then be formali+ed as

    followsE

    Definition 3.Let W2 be a $orld *no$led!e base, $"ic" is taxonam constructed as

    a directed acclic !rap"0 T"e W2 consists of a set of subjects lin*ed b t"eir

    semantic relations and can be formall defined as a -tuple W2 := , # $"ere

    is a set of subjects := .s1,s,000,sm/'

    is a set of semantic relations := .r1,r,000,rn/ lin*in! t"e subjects in 0

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    $i! 6. & am"le >art of the world %nowled!e base

    ).2 Onto%og& Con'truction

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    Definition 4. T"e structure of an ontolo! t"at describes and specifies topic is a

    !rap" consistin! of a set of subject nodes0 T"e structure can be formalized as a 3-

    tuple := %, tax%

    ,rel#, $"ere

    % is a set of subjects of t"ree subsets %, %-and %, $"ere % is a set of positi)e

    subjects re!ardin! , %- % is ne!ati)e, and % % is neutral'

    tax% is t"e taxonomic structure of , $"ic" is a noncclic and directed

    !rap" 5%,60 7or eac" ed!e e tpe5e6=is-a or part-of, iff s1 s# ,

    taxs1 s#= True means s1 is-a or is a part-of s'

    rel is a 2oolean function definin! t"e related-to relations"ip "eld b t$o

    subjects in %0

    The sub(ects of user interest are etracted from the '?G via user interaction. & tool

    called Ontolo!y 1earnin! 4nvironment /O140 is develo"ed to assist users with such

    interaction with 'eb User Interface. Re!ardin! a to"ic, the interestin! sub(ects consist

    of two setsE "ositive sub(ects are the conce"ts relevant to the information need, and

    ne!ative sub(ects are the conce"ts resolvin! "aradoical or ambi!uous inter"retation

    of the information need. Thus, for a !iven to"ic, the O14 "rovides users with a set of

    candidates to identify "ositive and ne!ative sub(ects. These candidate sub(ects are

    etracted from the '?G.

    $or each s , the s and its ancestors are retrieved if the label of s contains

    any one of the #uery terms in the !iven to"ic /e.!., Heconomic and Hes"iona!e0.

    $rom these candidates, the user selects "ositive sub(ects for the to"ic. The user-

    selected "ositive sub(ects are "resented on the to"-ri!ht "anel in hierarchical form.

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    The candidate ne!ative sub(ects are the descendants of the user-selected

    "ositive sub(ects. They are shown on the bottom-left "anel. $rom these ne!ative

    candidates, the user selects the ne!ative sub(ects. These user-selected ne!ative

    sub(ects are listed on the bottom-ri!ht "anel /e.!., H>olitical ethics and Htudent

    ethics0. Note that for the com"letion of the structure, some "ositive sub(ects /e.!.,

    H4thics, HCrime, HCommercial crimes, and HCom"etition Unfair0 are also

    included on the bottom-ri!ht "anel with the ne!ative sub(ects. These "ositive sub(ects

    will not be included in the ne!ative set.

    The remainin! candidates, which are not fed bac% as either "ositive or

    ne!ative from the user, become the neutral sub(ects to the !iven to"ic.

    &n ontolo!y is then constructed for the !iven to"ic usin! these user fed bac%

    sub(ects. The structure of the ontolo!y is based on the semantic relations lin%in! these

    sub(ects in the '?G. The ontolo!y contains three ty"es of %nowled!eE "ositive

    sub(ects, ne!ative sub(ects, and neutral sub(ects. $i!. 7 illustrates the ontolo!y

    /"artially0 constructed for the sam"le to"ic H4conomic es"iona!e, where the white

    nodes are "ositive, the dar% nodes are ne!ative, and the !rey.

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    $i!ure 7.&n ontolo!y constructed for s"ecific word economic es"iona!e

    ).) Onto%og& /ining (or "e0antic "peci(icit&

    Ontolo!y minin! discovers interestin! and on-to"ic %nowled!e from the conce"ts,

    semantic relations, and instances in ontolo!y. In this section, a 6D ontolo!y minin!

    method is introducedE "ecificity and 4haustivity. "ecificity /denoted s"e0

    describes a sub(ect)s focus on a !iven to"ic. This method aims to investi!ate the

    sub(ects and the stren!th of their associations in an ontolo!y.

    The semantic s"ecificity is investi!ated based on the structure of O/T0

    inherited from the world %nowled!e base. The stren!th of such a focus is influenced

    by the sub(ect)s locality in the taonomic structure ta of O/T0. &s stated in

    Definition 8, the taof O/T0 is a !ra"h lin%ed by semantic relations. The sub(ects

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    located at u""er bound levels toward the root are more abstract than those at lower

    bound levels toward the Hleaves. The u""er bound level sub(ects have more

    descendants, and thus refer to more conce"ts, com"ared with the lower bound level

    sub(ects. Thus, in terms of a conce"t bein! referred to by an u""er bound and lower

    bound sub(ects, the lower bound sub(ect has a stron!er focus because it has fewer

    conce"ts in its s"ace. ence, the semantic s"ecificity of a lower bound sub(ect is

    !reater than that of an u""er bound sub(ect.

    The semantic s"ecificity is measured based on the hierarchical semantic

    relations /is-a and "art-of0 held by a sub(ect and its nei!hbours in ta . Gecause

    sub(ects have a fied locality on the taof O/T0, semantic s"ecificity is also called

    absolute s"ecificity and denoted by s"ea/L0.

    ). Arcitecture o( te "earc Engine 3Onto%og& /ode% Backend4

    The "ro"osed ontolo!y model aims to discover user bac%!round %nowled!e and

    learns "ersonali+ed ontolo!ies to re"resent user "rofiles. $i!. 8 illustrates the

    architecture of the ontolo!y model. & "ersonali+ed ontolo!y is constructed, accordin!

    to a !iven to"ic. Two %nowled!e resources, the !lobal world %nowled!e base and the

    user)s local instance re"ository, are utili+ed by the model.

    The world %nowled!ebase "rovides the taonomic structure for the

    "ersonali+ed ontolo!y. The user bac%!round %nowled!e is discovered from the user

    local instance re"ository. &!ainst the !iven to"ic, the s"ecificity of sub(ects are

    investi!ated for user bac%!round %nowled!e discovery.

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    $i!ure 8. &rchitecture

    ).5 Preci'ion and Reca%%

    The "erformance of the e"erimental models was measured by three methodsE the

    "recision avera!es at 55 standard recall levels /55>R0, the mean avera!e "recision

    /F&>0,and the $5Feasure. These are modern methods based on "recision and recall,

    the standard methods for information !atherin! evaluation. >recision is the ability of a

    system to retrieve only relevant documents. Recall is the ability to retrieve all relevant

    documents.

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    The $5Feasure is calculated by

    8recision = 9 .rele)ant documents/ .retrie)ed documents/

    9 . retrie)ed documents/ 9

    ecall = 9 .rele)ant documents/ .retrie)ed documents/

    9 .total rele)ant documents/ 9

    71= ; 8recision ; ecall

    8recision ecall

    $5 measure is the harmonic mean of the "recision and recall.

    Gased on these, we can conclude that the Ontolo!y model is very close to the

    TR4C model, and si!nificantly better than the baseline models. These evaluation

    results are "romisin! and reliable.

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    C&>T4R 8

    T4CNO1OI4 U4D

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    CHAPTER : TECHNO!O*IE" U"ED

    This >ro(ect is im"lemented usin! the followin! technolo!ies on HFy4cli"se

    4nter"rise 'or%bench 5A.A with &"ache Tomcat : installedE

    5. @ava 44 ;

    6. &"ache truts

    7. Do(o @avacri"t $ramewor%

    8. Fy1

    6a7a EE 8:

    The al!orithms and the readin! from !lobal %nowled!e base which is in MF1 form

    and the validation of lo!in form, re!istration form etc., are im"lemented usin! @ava

    44. $orm validation is done usin! @ava Geans. The @ava erver >a!es of @ava 44 is

    used for renderin! the customi+ed "a!es to the users. ervlets are used with truts on

    server side.

    Apace "trut':

    &"ache truts is an o"en-source web a""lication framewor% for develo"in! @ava 44

    web a""lications. It uses and etends the @ava ervlet &>I to encoura!e develo"ers to

    ado"t a model-view-controller /F

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    The !oal of truts is to se"arate the model /a""lication lo!ic that interacts with

    a database0 from the view /TF1 "a!es "resented to the client0 and the controller

    /instance that "asses information between view and model0. truts "rovides the

    controller /a servlet %nown as &ction ervlet0 and facilitates the writin! of tem"lates

    for the view or "resentation layer /ty"ically in @>, but MF1M1T and

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    Do(o contains Di(it wid!et system which is very feature rich com"onent set

    that can be readily used by our web a""lication.Do(o wid!ets are com"onents

    com"risin! @avacri"t code, TF1 mar%-u", and C style declarations that "rovide

    cross-browser, interactive features such asE

    --Fenus, tabs, and toolti"s

    --ortable tables

    --Dynamic charts

    --6D vector drawin!s

    --&nimated effects

    --Tree wid!ets /used in our Ontolo!y 1earnin! 4nvironment0

    --

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    CHAPTER 5

    I/P!E/ENTATION

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    CHAPTER 5: I/P!E/ENTATION

    5.5 Introduction

    The >ro(ect is develo"ed usin! Fy4cli"se 5A.A with &"ache Tomcat : installed. User

    >rofiles are created when user lo!s onto the system. & new table is created for storin!

    the credentials of user and his "ersonali+ed bac%!round %nowled!e. The re!istration

    of the user the user and lo!in form are validated usin! @ava Geans.

    &fter 1o!in user is !iven a menu with o"tions to select from. Data set content

    and "recision-recall values calculation module are "resent in menus. &fter user selects

    one o"tion dataset content is "resented to select a sub(ect. 'hen a sub(ect is selected

    then our ontolo!y learnin! module will be "resented facilitatin! the user to select

    "ositive and ne!ative sub(ects. 'hen search o"tion selected to search the sam"le

    inde of lin%s with descri"tions we calculate the semantic s"ecificity for that sub(ect

    and the relevant results are dis"layed accordin! to the learnt bac%!round %nowled!e

    from Ontolo!y 1earnin! 4nvironment.

    The Ontolo!y 1earnin! 4nvironment is im"lemented usin! Do(o di(it wid!ets

    and the results "a!e is im"lemented usin! @ava erver "a!es. Then the ontolo!y is

    "ersonali+ed because it is ta%in! user)s "ersonal interests into account. It is semantic

    web search because the search is not done usin! the !iven %eyword blindly.

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    5.2 P'eudo Code "nippet'

    5.2.1. A%gorit0 1. Ana%&ing 'e0antic re%ation' (or 'peci(icit&

    Input : a personalized ontolo! := tax%, rel#' a coefficient bet$een 5

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    The determination of a sub(ect)s spea is described in &l!orithm 5. The

    and are two functionsin the al!orithm satisfyin!

    Theis returns a set of sub(ects that

    satisfy H PTrue and The returnsa set

    of sub(ects that satisfy P True and

    . &l!orithm 5 is efficient with the com"leity of only

    . The al!orithm terminates eventually because is a

    directed acyclic !ra"h, as defined in Definition 8.

    5.2.2. Do9o "cript (or 'e%ection o( po'iti7e and negati7e 'u+9ect'

    dojo.requre!"dojo.d#t#.Ite$%&eRe#dStore"'(dojo.requre!"djt.Tree"'(dojo.requre!"dojo.p#r)er"'(var*ou+t = 1(function)etPo)t,e!' -

    vartree = djt.yId!"ptree"'(var+ode = tree.#ttr!")e&e*tedIte$"'(vard#t#/ = "d#t#" *ou+t(var

    po)/ = "po)" *ou+t(*ou+t = !*ou+t 1'(

    varro = do*u$e+t.et&e$e+tyId!d#t#/'(vardde+ = do*u$e+t.et&e$e+tyId!po)/'(ro.++erT = +ode.+#$e(dde+.,#&ue = +ode.+#$e(

    28

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    This scri"t is intended for selection of "ositive and ne!ative sub(ects from

    do(o.tree data structure which is a user interface core com"onent of our ontolo!y

    learnin! environment.

    CHAPTER .&. Chirita, C.. $iran, and '. Ne(dl, H>ersonali+ed uery 4"ansion for the

    'eb, >roc. &CF IIR /)A;0, "". ;-58, 6AA;.

    Q; R.F. Colomb, Information "acesE The &rchitecture of Cybers"ace. "rin!er,

    6AA6.

    QK &. Doan, @. Fadhavan, >. Domin!os, and &. alevy, H1earnin! to

    Fa" between Ontolo!ies on the emantic 'eb, >roc. 55th Int)l Conf. 'orld 'ide

    'eb /''' )A60, "". ::6-:;7, 6AA6.

    QB D. Dou, . $rish%off, @. Ron!, R. $ran%, &. Falony, and D. Tuc%er, HDevelo"ment

    of Neuroelectroma!netic Ontolo!ies/N4FO0E & $ramewor% for Finin! Grainwave

    Ontolo!ies, >roc. &CF I?DD /)A;0, "". 6;A-6;B, 6AA;.

    48

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    Q5A D. Downey, . Dumais, D. 1ieblin!, and 4. orvit+, HUnderstandin! the

    Relationshi" between earchers) ueries and Information oals, >roc. 5;th &CF

    Conf. Information and ?nowled!e Fana!ement /CI?F )AK0, "". 88B-89K, 6AAK.

    Q55 . auch, @. Chaffee, and &. >retschner, HOntolo!y-Gased >ersonali+ed earch

    and Growsin!, 'eb Intelli!ence and &!ent ystems, vol. 5, nos. 78, "". 65B-678,

    6AA7.