Scientific Ontology Construction Based on Interval Valued Fuzzy Theory under Web 2.0

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1 JOUNAL OF SOFTWAE, VOL. 8, NO. 8, AUGUST Scenfc Onology Consrucon Based on Inerval Valued Fuzzy Theory under Web 2.0 Na Xue, Sulng Ja, Jnxng Hao and Qang Wang School of Economcs and Managemen, Behang Unversy, Beng, Chna Emal: Absrac Consrucon of he unfed and shared doman onology s sgnfcan for effecve knowledge managemen. For he acquson and sharng of scenfc research knowledge under Web2.0, a novel approach of buldng Inerval Valued Fuzzy Onology (IVFO) n scenfc research doman s presened. Through nerval valued fuzzy heory, he defnon and consrucng framework of IVFO s proposed. Then IVFO s appled o sem-auomac exracon of nformaon rereval research doman. The prelmnary consrucng of research doman onology s an essenal base for he knowledge managemen sysem of scenfc research. I can be effecve mehods for enhancng he effcency and producvy of researchng. Index Terms Scenfc esearch Onology, Inerval Valued Fuzzy Onology, Sem-auomac Consrucon, Inerval Valued Fuzzy Ses I. INTODUCTION Scenfc research s he born and communcaon base of new scence and new echnology. I requres effecve knowledge managemen o mprove he effcency of scenfc research. The adven of Web 2.0 provdes he echncal plaform for wde communcaon and cooperaon. Couplng wh he scenfc communy, Web 2.0 brngs abou he open scence reformaon n he Inerne and nformaon age. Open Scence (or Open esearch, Open Source Scence, Scence 2.0) becomes research ho spo problems. Communcaon and collaboraon by nework for knowledge sharng and nnovaon would promoe he developmen of scenfc research sgnfcanly. As formal specfcaon of a shared concepualzaon, onology s mporan for knowledge represenaon and sharng. Therefore, he consrucon of unfed and shared scenfc doman onology s parcularly urgen. Conceps n knowledge resources have varous knds of relaons. The boundary of conceps and her relaons s dffcul o be specfed. To deal wh he uncerany, fuzzy se heory was nroduced no onology by many researches. Consrucon of fuzzy onology makes more flexble o adap o he acual requremen. Tomohro Takag e al. [1] fused concepual fuzzy ses wh onology for represenng common conceps, and proposed a concepual machng mehod for nformaon rereval. Valere V. Cross and Clare Voss [2] performed fuzzy onology query for mullngual documen exploaon. The fuzzy ses heory was nroduced no he concep and relaonshp n he consrucon of onology snce hen. And researches on he fuzzy onology are emergng gradually [3-13]. However, exsng fuzzy onology models were no fully capable of modelng conceps and obecs n a way compable wh he human percepon. Tradonal ype-1 fuzzy ses employed n fuzzy onology could resolve some unceranes. Bu conceps could no be dvded by a sngle creron for dfferen people have dfferen measures. Besdes, vews on a ceran obec from dfferen researchers are dffcul o be unfed. Especally n he crcumsance of open scence, he unfed cognon needs o be reached. Therefore, nerval valued fuzzy conceps and relaons ha can embrace he dfferen vewpons would fulfll doman knowledge sharng. Based on hs, we proposed a novel mehod of buldng scenfc doman onology based on nerval valued fuzzy ses, whch s a knd of ype-2 fuzzy ses. I can well handle he noon of people and a hgh degree of unceranes. A framework of buldng nerval valued fuzzy onology s proposed n hs paper based on he nerval valued fuzzy ses heory. The approach s used n scenfc doman onology for knowledge represenaon. The remander of hs sudy s organzed as follows. Secon 2 revews he relaed work of scenfc doman onology. An approach of buldng nerval valued fuzzy onology s proposed n Secon 3. Secon 4 descrbes he scenfc doman onology exracon mehod. Secon 6 concludes he paper. II. ELATED WOK A. Onology and Fuzzy Onology Onology s a concepualzaon of a doman no a human undersandable, machne-readable forma conssng of enes, arbues, relaonshps, and axoms [14]. I s engneered by - bu ofen for - members of a doman by explcang a realy as a se of agreed upon erms and logcally-founded consrans on her use [15]. Onology s wdely used for knowledge represenaon n arfcal nellgence, nformaon rereval and semanc web. do: /sw

2 1836 JOUNAL OF SOFTWAE, VOL. 8, NO. 8, AUGUST 2013 However, ypcal onology wh crsp descrpon could no handle he unceranes n real applcaons for he lack of clear-cu dsncons of doman conceps. For example, a manuscrp can be que nnovave, general nnovave or less nnovave. Therefore, exper opnons on he manuscrp can be dfferen or even oppose. To handle wh hs problem, several researchers employed fuzzy se heory o consruc fuzzy onology. Lee C. S. e al. [16] proposed a fuzzy onology and appled o news summarzaon. Quan Thanh Tho e al. [17] ncorporaed fuzzy logc no onology o represen uncerany nformaon, and proposed Fuzzy Onology Generaon framework for auomac generaon of fuzzy onology on uncerany nformaon. LAU. e al. [18] llusraed a novel concep map generaon mechansm whch s underpnned by a fuzzy doman onology auomac exracon algorhm. C. A. Yagunuma e al. [19] red o use fuzzy logc conceps n crsp onologes for a more expressve represenaon of vague nformaon relevan o some domans, and presened DISFOQuE sysem for daa negraon based on fuzzy onology. B. Scenfc Onology Scenfc knowledge managemen has araced aenon from many researchers o on he sudy. Here we revewed he works by means of onology echnque and mehod. [20] M. Eorre e al. developed an expermenal knowledge managemen ool servng he ICA-CN, a research nsue of he Ialan Naonal Councl of esearch. I appled n he formalzed groups (such as proec groups) where collaborave work akes place and nformal groups (communes of pracce) ha may arse around common problems, neress and obecves. The ool provdes a web-based envronmen for knowledge creaon, sharng and access. I helps researchers quckly se up her own porals, creae and organze knowledge ems (such as news, papers, lnks, announcemens, proecs, ec.), share hem whn workgroups, search for a specfc documen or browse hrough a se of relaed documens (onology-drven browsng). Ths should be done by basng on he bul of he esearch Onology and he Knowledge Iem Onology, whch conan he formal specfcaon of applcaon doman conceps, relaons and consrans. Q.T. Tho e al. [21] employed conex-based cluser analyss (CCA) and conex-based onology generaon framework (COGA) o develop a sgnfcanly mproved caon-based documen rereval sysem. COGA ams o generae onology from clusers relaonshp bul by CCA. The mproved rereval sysem s appled o fnd research doman expers. J.C. de Almeda Bolchn e al. [22] developed he onologes o descrbe knowledge regardng sysemac revews of expermenal sudes. As an explc specfcaon of concepualzaon, he scenfc research onology can be useful o guaranee he ermnologcal homogeney of he conceps ha are o be used by dfferen researchers, conrbung o a hgher conssency beween he rereved nformaon and he consequen resuls. [23] Sheng-Yuan Yang developed an onologysuppored nformaon negraon and recommendaon sysem for scholars. I can exrac mporan nformaon from doman documens by nformaon negraon and recommendaon rankng. Onology daabase s bul o serve he webpage crawler for queryng he webpage of relaed scholars, and o asss he webpage classfer n processng webpage classfcaon, ec. Ban Wen-yu e al. [24] dscussed he problems n presen knowledge managemen for scenfc research n Chna, such as complcaed knowledge sources, scaered sorage and nnovaed knowledge s delayed sorage, ec. Onology s adoped o buld knowledge model of scenfc research. The sudy consruced an onology sysem of scenfc research knowledge and showed he formal expresson of scenfc research knowledge onology. Hong Na and Zhang Zh-xong [25] chosen scence ndvduals as analyss obecs, usng onology consrucon ool (Proégé) o creae Scence Onology and reason on a smple relaon. And he sudy also summed up some problems sll exsng n large scale onology sorage and managemen. Zhang Qang [26] proposed he leraure onology srucure and semanc dconary srucure hrough combnng onology consrucon ools (Proégé) and onology nference machne (acer). OWL onology descrpon language s employed o express he semanc nformaon of leraure doman onology. The research realzed he semanc and nellgen rereval of leraures. The exsng researches are manly concerned he scenfc doman obecs lke leraure rereval and fndng expers. Bu conceps of scenfc research heme and her unceran boundary are gnored. Besdes, here also has he problem of unfyng dfferen personnel cognve under he Web2.0 envronmen. Even many researches on fuzzy onology only consder a real number o descrbe he concep herarchy. Bu assgnng an exac number o an exper s opnon s oo resrcve, and ha he assgnmen of an nerval of values s more realsc. On he oher hand, nerval fuzzy ses could be used o handle group decson process as hey can model dfferen beween exper preferences. Ths requres he consrucon of scenfc research doman onology wh ype-2 fuzzy heory, as deals wh human knowledge represenaon. Chang-Shng Lee e al. [27] combnng ype-2 fuzzy ses wh onology for he frs me o develop he personal de recommend sysem. The ype-2 fuzzy onology mproved sasfacon of expers and users a he classfcaon of personal de. Inerval fuzzy ses are used as a specal suaon of ype-2 fuzzy ses. Based on hs, we promoe nerval valued fuzzy ses n scenfc research doman. The unceranes manly concerned nclude he follows: (1) unceranes assocaed wh sensed measuremens and he level of scholars; (2) unceranes assocaed wh changng relaons and applcaon conex;

3 JOUNAL OF SOFTWAE, VOL. 8, NO. 8, AUGUST (3) unceranes of lngusc meanng o dfferen people; (4) unceranes assocaed wh he expers opnons and crerons. Type-2 fuzzy ses could be used o handle he above unceranes n group knowledge sharng as hey can model he unceranes beween exper preferences. Inerval valued fuzzy ses are mos wdely used ype-2 fuzzy ses. Therefore we employ nerval valued fuzzy ses o model he scenfc conceps for knowledge represenaon. III. CONSTUCTION OF INTEVAL VALUED FUZZY ONTOLOGY A. Inerval Valued Fuzzy Theory Curren fuzzy onology employs he ype-1 fuzzy ses heory, n whch he membershp of each elemen s a crsp number n [0, 1]. Defnon 1. A (ype-1) fuzzy se A n X s a se of ordered pars A = {( x, μ A ( x) )}, x X where μ ( x ) s he grade of membershp of x n A A and μ : X [ 0, 1] s called he membershp funcon [28]. A Usng ype-1 fuzzy ses o deal wh vague knowledge, he correlaon value of conceps n fuzzy onology s defned as a number n [0, 1]. I breaks he lmaons of classc onology ha can only deermne wheher he conceps are relaed or unrelaed. Fuzzy ses are requred o defne he descrpve conceps n scenfc doman onology. Alhough ype-1 fuzzy ses can allevae he mpac of uncerany o a ceran exen, he nformaon from he membershp degree s no complee, especally for modelng exper group decson process. Neverheless, s sll a grea challenge o specfy a crsp membershp degree o descrbe doman conceps and her boundary n scenfc onology. For example, consder a ype-1 fuzzy se represenng he lngusc evaluaon of a ceran research arcle from hree expers n Fg. 1. As shown, he expers gve dfferen scores on he same aspec. However, f we have o assgn a number o evaluae he praccaly, lke defnng he mean of all scores as he fnal score, we wll mss sgnfcan nformaon abou he revews. Acually, ype-1 fuzzy ses are no capable o deal wh hgh unceranes n several suaons [29] : (1) Lngusc expresson whose meanng s dfferen from dfferen revewers; (2) Group opnons whch are no unfed on he same obec. These unceranes are dffcul o be descrbed by s crsp number wh ype-1 fuzzy ses. To deal wh hese unceranes, we employed ype-2 fuzzy ses whose membershp funcons are fuzzy se nsead of a crsp number. Fg. 1 Type-1 fuzzy ses for lngusc evaluaon on an arcle from hree expers Dfferen from he ype-1 fuzzy ses, ype-2 fuzzy ses have he membershp funcons mappng from U o [0, 1]. Tha s o say, he membershp funcon of ype-2 fuzzy ses ranges over ype-1 fuzzy ses. Defnon 2. A ype-2 fuzzy se A % s characerzed by a ype-2 membershp funcon μ ( x, u ), where A% x X and u J x [0,1],.e., A% = {(( x, u), μ (, ), [0,1] A% xu x X u Jx } n whch [29] 0 μ (, x u) 1. A% When he secondary grades all equal one, he resulng ype-2 fuzzy ses are called nerval valued fuzzy ses. I.e., for u J [0,1], f( u) 1. Such ses are he mos x x wdely used ype-2 fuzzy ses o dae. Inerval valued fuzzy ses are he mos wdely used ype-2 fuzzy ses because hey are smple o use. Le I be a closed un nerval,.e., I =[0,1], and [ I] = { a = [ a -, a + ]: a - < a +, a -, a + I }. Defnon 3. Le X be an ordnary se, mappng A: X [ I] s called an nerval valued fuzzy se (IVFS) on X. B. Inerval Valued Fuzzy Onology (IVFO) A fuzzy onology s exended doman onology wh fuzzy conceps and fuzzy relaonshps [5]. Type-2 fuzzy onology s a knowledge represenaon model o descrbe he doman knowledge wh uncerany. I s an exenson of he doman onology [27]. We wll gve he formal defnon of ype-2 fuzzy onology. Defnon 4. A Type-2 Fuzzy Onology (T2FO) s a 6-uple 2 {, C O F = C A,, A, H, X}, where C s a se of C conceps; A s he se of arbues descrbng each concep; = ( T, N) represens he ype-2 fuzzy relaon, ncludng he axonomy relaon T and non-axonomy relaon N ; A defnes he arbues of ype-2 fuzzy relaon ; H descrbes he concep herarchy; X s a se of axoms. When = ( T, N) are he nerval fuzzy relaons, he ype-2 fuzzy onology s he Inerval Valued Fuzzy Onology (IVFO).

4 1838 JOUNAL OF SOFTWAE, VOL. 8, NO. 8, AUGUST 2013 Defnon 5. An Inerval Valued Fuzzy Onology [ ], C O = C A,, A, H, X, where (IVFO) s a 6-uple I { } C C s a se of conceps; A s he se of arbues descrbng each concep; = ( T, N): C [ I] represens he nerval valued fuzzy relaon, ncludng he axonomy relaon T : C [ I] and non-axonomy relaon N : C [ I] ; A defnes he arbues of ype-2 fuzzy relaon ; H descrbes he concep herarchy; X s a se of axoms. When comes specfcally o scenfc onology, C can refer o a ceran form of scenfc oucomes (such as ournal arcle); hen A C s he arbues descrbng he oucomes (for example, he le, keywords, absrac, auhor, reference of he arcle and so on). If C s he research opc concep, hen A C conans opc name, research obec, mehod, relaed publcaon and so on. The general scenfc doman onology covers research opc, researcher, acvy, oucome, organzaon, and facly. Each caegory can be furher classfed. For example, he scenfc oucome ncludes paen, book, repor, arcle and so on. Ths sudy manly ams a dscussng how o consruc knowledge onology sysem of research doman opc. Par of daa mnng research concep herarchy s shown n Fg. 2. C. Consrucon Framework of Inerval Valued Fuzzy Onology Generally, here are wo ways o buld onology: manually dervng onology from knowledge and daa; [8,10] sem-auomac exracng onology from daa. Manually dervng onology requres exper group o predefne conceps and gve ou he annoaon. Auomacally exracng onology from corpus apples nformaon rereval approach. Manual generaon of onology s dffcul and edous. Therefore, auomac generaon of concep s hghly desrable. Onology generaon daa sources nclude nonsrucured, sem-srucured and srucured daa. Beween hese, ex daa are he mos used. In hs sudy, we presen he generaon of nerval valued fuzzy onology of scenfc doman from he ex daa. The framework s shown n Fg. 3. Fg. 2 Daa mnng research concep herarchy IV. SEMI-AUTOMATIC GENEATION OF SCIENTIFIC ONTOLOGY A. Concep Exracon The scale of he core concep n scenfc doman onology s small han upper onology. For example, he number of conceps n dfferen onology s dsrbued as: General Feld > Scenfc Doman > Busness Inellgence Dscplne > Daa Mnng Topc. In he scenfc doman, he conceps can be dvded no hree caegores: basc concep, evaluave concep and exploraory concep. Basc concep s he normal doman specfcaon, such as he ournal, auhor, and algorhm and so on. Evaluave concep comprses a sgnfcan degree of descrpve conen and s evaluavely loaded. For example, exper, op ournal, ho subec ec. Exploraory concep s nnovaed research whch sll beng argued. Ths knd of classfcaon represens he evoluon of researchng. Accordng o he fuzzy degree of concep, here are crsp concep, (ype-1) fuzzy concep and nerval fuzzy concep n nerval valued fuzzy onology. Crsp concep has clear defnon of nenson and exenson. The membershp degree of a fuzzy concep s a number n [0, 1]. Semanc annoaon of fuzzy concep membershp can be he sgnfcance of erm [2], and ec. The elemen of nerval valued fuzzy concep s fuzzy concep. Fuzzy concep and nerval valued fuzzy concep are suable for overlappng conceps. The doman knowledge em wh specfc arbue s always wha scholar consders. For example, he concep of book of research oucome could have arbues as reference value, advanage. Whle n eachng feld, can have learnably, unversaly and applcably. Doman knowledge s he nellgen reasonng par combnes wh relevan concep daa, whch can mos reflec he value of knowledge onology. Therefore, s fuzzer and more complex, whch s applcable usng nerval valued fuzzy heory. The hree man mehods of concep exracon are rulebased approach, machne learnng based on sascs and mehod based on srucured daa. The measures ha are used o esmae he membershp degree nclude Jaccard (JA), condonal probably (CP), Kullback-Lebler dvergence (KL), Expeced Cross Enropy (ECH), Nomalzed Google Dsance (NGD) and Muual Informaon (MI). We employ he balanced Muual Informaon (BMI) [8] o oban he membershp degree of he erm o concep (see Eq. (1)). μc ( ) BMI(, ), ) + 1 = β [, )log2 ( ) (1) ) ), ) + 1 +, )log2 ( )] ) ), ) + 1 (1 β) [, )log2 ( ) ) ), ) + 1 +, )log2 ( ] ) )

5 JOUNAL OF SOFTWAE, VOL. 8, NO. 8, AUGUST Fg. 3 The generaon framework of nerval valued fuzzy onology Where μ ) s he membershp funcon o calculae c ( he degree of erm C A belongng o concep c C. P, ) s he on probably ha boh erms appear n a ( ex wndow. ) w w s he probably of erm = appears n a ex wndow, n whch w s he number of wndows conanng he erm and w s he oal number of wndows consruced from a corpus. represens conan no. β > 0. 5 s used o conrol he relave mporance of wo knds of evdence. In order o ge more nformaon, we une he wndow lengh o be 5 and 10. Afer he ex daa been processed, he nersecon of wo erm ses s he fnal resul of he concep se. The wo membershp values are end pon of he nerval. For example, for δ = 5, he degree of he erm recommendaon belongng o concep nellgence s 0.62; for δ = 10, he degree of he erm recommendaon belongng o concep nellgence s Therefore, he nerval of recommendaon belongng o nellgence s [0.62, 0.81]. + (1-α) B. Inerval Valued Fuzzy elaon Exracon The herarchcal relaon of doman onology ncludes hypernymy relaon and hyponymy relaon. Nonherarchcal relaon ncludes synonymy relaon and oher operaonal relaons. As he concep herarchy s a parally ordered se [10], whch can be generaed by he probable order of erms appear n ex wndows. The smlary of erms s effecvely measured by semanc dconary, for canno be refleced by he cooccurrence. In our sudy, we urn o WordNe - a lexcal nework of Englsh words. WordNe has he semanc senses of he words and has become one of he mos wdely adoped resources for semanc analyss. In WordNe, nouns, verbs, adecves, and adverbs are each organzed no neworks of synonym ses (synses) ha each represens one underlyng lexcal concep and are nerlnked wh a varey of relaons. A polysemous word wll appear n one synse for each of s senses. Wh WordNe, he calculaon of semanc smlary of wo dfferen erms s shown n Eq. (2). S(, ) = αs + (1-α) S s s = α s s s r r - r r + r Where α 1 decdes he relave mporance and conrbuons of semanc nformaon S and word order s (2) nformaon S ; α s suggesed o r s and r are semanc vecor enropy and word order vecor of respecvely [30]. C. Inerval Valued Fuzzy Onology Axom Onology axom s he consran on he concep s and relaon s arbue value, or he relaons beween conceps. Axoms cover concep axom, arbue axom and nsance axom. The forma descrbng axom s SWL [31], as followng: aneceden consequen Expanded o fuzzy forma for fuzzy onology, denoed by FSWL, as shown: aneceden consequen [ a] [ b] Furher expanded o nerval fuzzy forma for nerval valued fuzzy onology, denoed by IFSWL, as followng: aneceden consequen + + [ a, a ] [ b, b ] - + where a [0,1], b [0,1], and [ a, a ] [0,1], - + [ b, b ] [0,1]. In scenfc area knowledge onology, he man rules and axoms are endowed as follows. Imples (Aneceden (conss-of (C 1, C 2 ) [a,b]) Consequen (belong-o (C 2, C 1 ) [a,b])) Imples (Aneceden (belong-o (C1, C2) [a,b]) Consequen (conss-of (C 2, C 1 ) [a,b])) // he relaon of conss-of and belong-o are reverse Imples (Aneceden (super-area (C1, C2) [a,b]) Consequen (sub-area (C 2, C 1 ) [a,b])) Imples (Aneceden (sub-area (C1, C2) [a,b]) Consequen (super-area (C 2, C 1 ) [a,b])) // he relaon of super-area and sub-area are reverse Imples (Aneceden (relevan (C1, C2) [a,b]) Consequen (relevan (C 2, C 1 ) [a,b])) Imples (Aneceden (relevan (C1, C2) [a,b] relevan (C2, C3) [c,d]) Consequen(relevan(C 1, C 3 )[ a*c, b*d] )) // he relaon of relevan are ransve V. PEFOMANCE EVALUATION Expermens on daases are conduced o show he effecveness of he proposed mehod. We ry o evaluae he generaed onology wh he onology developed by dfferen expers. The expermens are carred ou on a PC runnng Wndows 7 wh Inel Penum Dual-Core CPU E5200 (2.50GHz), 3.0G AM. The daa ses are 100 papers of 10 opcs abou nformaon managemen and knowledge engneerng

6 1840 JOUNAL OF SOFTWAE, VOL. 8, NO. 8, AUGUST 2013 from onlne Web of Knowledge dgal daabase (hp://apps.webofknowledge.com/). Each opc has 10 papers. The concep exracon algorhm s EnropyConcepExracon, relaon exracon algorhm s SubcaelaonExracon. Our evaluaon mercs are precson, recall and F-measure of he onology consrucon mehod. CE CS Precson = C CS CE CS ecall = C CE E S Precson = S E S ecall = E Precson = ω Precson + (1-ω ) Precson O P C P ecall = ω ecall + (1-ω ) ecall O C where C and C represen he se of concep found E N from he onology creaed by human expers and ha generaed by our mehod respecvely. Smlarly, and E are he se of relaons of he onology gven by S human expers and ha generaed by our mehod respecvely. The parameer ω P s used o une he onology precson measure based on a weghed sum of he concep precson and relaon precson respecvely. Smlarly, he parameer ω s used o une he onology recall measure. For he expermens conduced n hs sudy, we adop ω = ω = 0.5. The sandard F-measure P s shown n he followng equaon. 2 ( 1+ η ) Precson ecall Fη = 2 η Precson + ecall If we assume ha precson s as mporan as recall (.e. η = 1 ), hen he onology F-measure s calculaed by: 2 PrecsonO ecallo FO = PrecsonO + ecallo The precson, recall and F-measure of he onology acheved over he 10 opcs are shown n Table 1. The dvded opc resuls are shown n erms of onology precson, onology recall and onology F-measure. The second and hrd columns refer o he number of conceps and relaons generaed by he mehod. As can be seen from he average resuls, he onology F-measure means he onology generaed by our mehod can manly represen he doman knowledge as perceved by he doman exper. The second expermen we conduced s o model he commens from dfferen expers. We seleced commens of 5 revewers on a ceran opc research on he web. Fg. 4 shows a sample of an arcle revewed commens from dfferen revewers. TABLE 1. THE ESULTS OF THE GENEATED ONTOLOGY OF DIFFEENT TOPICS Fg. 4 The sample of commens on a ceran opc from 5 revewers. The descrpons on he sudy from he exper are assgned as numbers n [0, 1]. For example, descrpve commens can be an elemen of {poor, no suffcen, general, well organzed, and excellen} on a specfc aspec, whch s ranslaed o be {0.1, 0.3, 0.5, 0.7, and 0.9} respecvely. Each elemen represens he se of synonymy erm group. Tha s o say, ousandng and excellen boh represen he score of 0.9 as hey are n he same synonymy erm group. Accordng o he descrpon caegory, scenfc obecs evaluaon onology can be obaned. Wh he score generaed n deal, he aggregae assessmen from one exper could be generaed. Tha s expressed wh he ype-1 fuzzy onology. In oher words, ype-1 fuzzy onology can be used o represen he revew resul from one exper. I suppors boh he overall evaluaon and drllng down deals. The ype-1 fuzzy onology generaed from he fourh revewer descrbng he repor s shown n Fg. 5. As can be seen from he fgure, he llusraon of he key conceps and conclusons are consdered o be mproved accordng o he revewer. And he enre assessmen can be nferred from he boom resuls. Afer he opnon of one exper s delvered by ype-1 fuzzy onology, we can generae he annoaons of a ceran obec from dfferen expers. And Fg. 6 represens he aggregae resul of he commens wh nerval valued fuzzy onology. The commens are llusraed wh he nerval value, n whch he lower bound and upper bound are mnmzed and maxmzed of

7 JOUNAL OF SOFTWAE, VOL. 8, NO. 8, AUGUST he commens on a ceran aspec of he obec. The super nerval s he weghed average of s sub value nervals from he boom o he op. Fg. 5 The ype-1 fuzzy onology obaned from he fourh revewer. a valuable reference o onology buldng n oher domans n Web 2.0 ages. Scenfc knowledge onology buldng s a complcaed proec. In addon, onology of he researcher, acvy, oucome, organzaon and facly sub area sll need o be suded. The work of sandardzaon of he process of formal onology and s nference should be conduced n he fuure. ACKNOWLEDGMENT The work repored n hs paper has been funded n par by he Chna Naonal naural scence foundaon youh scence fund proec ( ), docoral program of hgher educaon by he specalzed research fund proec ( ) and he specal fund of cenral unversy basc scenfc research busness expenses. EFEENCES Fg. 6 The nerval valued fuzzy onology obaned from all revewers. As s shown n Fg. 5 and Fg. 6, he commen of a revewer on a ceran research oucome can be well descrbed n deal by he fuzzy onology. Exended o more general suaons, onlne scholars can make annoaons on a ceran documen and oher scenfc onology obecs. For example, researchers can evaluae from as ny as an arcle heorem, o as large as a subec's op ournals or leadng scenss. Wh he aggregae onlne commens, hen valuable doman knowledge s avalable o scholars. VI. CONCLUSIONS Wh he dversy of scenfc research subec and he surge of he research achevemens, effecvely arrangemen of research knowledge s urgenly needed o make full use of scenfc nellecual asses. Web 2.0 echnologes are promong he scenfc research hrough more onlne communcaon and collaboraon o share knowledge. The consrucon of shared and reused scenfc onology could promoe research effcency. Ths paper proposed a novel approach o generae he scenfc onology hrough nerval valued fuzzy ses. The nerval valued fuzzy heory s nroduced o deal wh he uncerany of conceps and dfferen comprehenson under he conex of Web 2.0. To acheve he uned doman knowledge, we employed he onology consrucon mehod o generae doman concep and relaons. epresened by he research opc, he semauomac generaon framework s llusraed, whch ncludes concep exracon, relaon exracon and axoms. The mehodology proposed n hs sudy provdes [1] Tomohro Takag, Shnch Kasuya, Masao Mukadono, Toru Yamaguch, Takash Kokubo, ealzaon of Soundscape Agen by he Fuson of Concepual Fuzzy Ses and Onology, Proceedngs of IEEE Inernaonal Fuzzy Sysems Conference, [2] Valere V. Cross, Clare Voss, Fuzzy onologes for mullngual documen exploaon, 18h Inernaonal Conference of he Norh Amercan Fuzzy Informaon Processng Socey- Nafps, 1999: [3] Chang-Shng Lee, Me-Hu Wang, Huan-Chung L, and Wen-Hu Chen, Inellgen Onologcal Agen for Dabec Food ecommendaon, IEEE Inernaonal Conference on Fuzzy Sysems, 2008: [4] Chang-Shng Lee, Yea-Juan Chen, Zh-We Jan, Onology-based fuzzy even exracon agen for Chnese e-news summarzaon, Exper Sysems wh Applcaons, 2003, 25(3): [5] Chang-Shng Lee, Zh-We Jan, Ln-Ka Huang, A fuzzy onology and s applcaon o news summarzaon, IEEE Transacons on Sysems Man and Cybernecs Par B- Cybernecs, 2005, 35(5): [6] Umbero Sracca, Towards a Fuzzy Descrpon Logc for he Semanc Web, The Semanc Web: esearch and Applcaons, 2005: [7] Wang, Me-Hu, Chang-Shng Lee, Zh-We Chen, Han Hagras, Su- E. Kuo, Hu-Chng Kuo, and Hu-Hua Cheng. "A Type-2 Fml-Based Fuzzy Onology for Deary Assessmen." Chap. 9 In On he Power of Fuzzy Markup Language, eded by Govann Acampora, Vncenzo Loa, Chang-Shng Lee and Me-Hu Wang. Sudes n Fuzzness and Sof Compung, : Sprnger Berln Hedelberg, [8] aymond Lau, Dawe Song, Yuefeng L, Terence Cheung, Jn-Xng Hao, Towards a fuzzy doman onology exracon mehod for adapve e-learnng, IEEE Transacons on Knowledge and Daa Engneerng, 2009, 21(6): [9] JIANG, B., ZHU, M., WANG, J., Onology-Based Informaon Exracon of Crop Dseases on Chnese Web Pages, Journal of Compuers, 2013, 8(1): [10] Tho Q.T., Hu S.C., Fong ACM, Cao T.H., Auomac Fuzzy Onology Generaon for Semanc Web, IEEE Transacons on Knowledge and Daa Engneerng, 2006, 18(6):

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