Method of Etating Is-A and at-of Relations Using atten ais in Mass Cous Se-Jong Kim Yong-Hun Lee and Jong-Heok Lee Deatment of Comute Siene and Engineeing ohang Univesit of Siene and ehnolog (OSECH) San 31 Hoja-dong Nam-gu ohang 790-784 Reubli of Koea {sejong hlee95 jhlee}@osteh.a.k Abstat. his ae ooses a method that etats tem ais satisfing is-a elations o at-of elations fom a mass ous using ais of attens shaing a tem. We etated eliable single attens and atten ais using some tem ais that satisf the taget elation and etated eliable tem ais using these attens. he etated tem ais wee used to etat new single attens and atten ais and we eeated these stes seveal times. he oosed method ahieved 71.5% aua in deteting is-a elations and 88% aua in deteting at-of elations and etated 144 new is-a elations and 85 new at-of elations whih ould not be etated using single attens. hese esults ae useful in onstuting an ontolog and a thesauus beause these language knowledge bases onsist mainl of is-a elations and at-of elations. Kewods: Bootstaing algoithm shaed tem eliabilit equation subjetive evaluation mohologial analsis. 1 Intodution An ontolog is a language knowledge base that oganizes ategoies of onets and defines elations among the ategoies. he ontolog is useful in semanti analsis of natual language oessing (NL) and infomation etieval (IR). Methods that onstut the ontolog fall into two ategoies: those that ombine eisting ontologies and thesauuses (Maedhe and Staab 2002) and those that semi-automatiall onstut the ontolog using a mass ous (Kavale and Svatek 2005). he fist method fouses on eanding the ontolog using eisting esoues and the seond method fouses on onstuting new ontologies. atiulal onstuting the ontolog using a mass ous begins with automati etation of semanti elations. Etation of semanti elations is a oess that etats tem ais that satisf a seified taget elation. he most ommon aoah in this oess is a atten-based aoah fo is-a and at-of elations (Beland and Chaniak 1999; Giju et al. 2003; Heast 1992; antel and ennahiotti 2006; Ravihandan and Hov 2002). his aoah egads wods aeaing between tems as attens and etats tem ais using the attens. Heast (1992) etats is-a elations using manual attens and ooses a bootstaing algoithm using seed tem ais. o etat at-of elations Beland and Chaniak (1999) measue the eliabilities of tems using the fequenies of tems that have the onet of whole o at and Giju et al. (2003) uses a leial database (WodNet) and a deision tee. Ravihandan and Hov (2002) etat semanti elations fo vaious tems in a question answeing (QA) sstem. antel and he wok eoted in this ae was suoted in at b the Koea Siene and Engineeing Foundation (KOSEF) gant funded b the Koean govenment (MES No. 2009-0075211) and in at b the BK 21 ojet in 2009. Coight 2009 b Se-Jong Kim Yong-Hun Lee and Jong-Heok Lee 23d aifi Asia Confeene on Language Infomation and Comutation ages 260 268 260
ennahiotti (2006) oose Esesso algoithm that uses the eliabilities of tems and attens and that has good efomane. his algoithm is alied to a oefeene esolution (Yang and Su 2007). Howeve these methods onside onl single sentenes to etat attens and an not etat ais of tems in diffeent sentenes. In single sentenes the tem ais satisfing is-a elations ae in fat few and the kind of attens efleting eah taget elation ae ve estited. If evious methods use an oen-domain ous these methods ma not be useful. We oose a method that etats tem ais satisfing is-a elations o at-of elations fom a mass ous using ais of attens shaing a tem and omae the esult with a evious method (antel and ennahiotti 2006) that uses single attens. he oosed method an etat ais of tems in diffeent sentenes in the ous beause this method uses ais of attens that an aea in diffeent sentenes. his haateisti of atten ais will enable etation of eliable tem ais and odue good efomane. Fo the evious method and the oosed method we evaluate the aua of the etated tem ais and ount the new tem ais whih an not be etated using single attens. A desition of the oosed method is given in Setion 2 esults ae given in Setion 3 a disussion is given in Setion 4 and a onlusion is given in Setion 5. 2 Mateials and Methods 2.1 Oveview A tem is a meaningful noun o noun hase that does not inlude atiles and a atten is the set of ositions of the tems and the wods between the tems. he ositions use a vaiable X and Y. If a sentene is A human is an animal tems ae human and animal and a atten is X is an Y. Esesso algoithm (antel and ennahiotti 2006) uses a bootstaing algoithm (Heast 1992) and has good efomane. his algoithm etats attens aeaing between some eaed tem ais (seed tem ais) that satisf a seified taget elation. he eliabilities of these attens ae measued b the atten eliabilit equation ( ) using ointwise mutual infomation (mi) and disounting fato (df) (antel and Ravihandan 2004) as: whee d d mi df ma mi df. (1) In (1) is a atten; ( ) is a tem ai; is a set of tem ais used to etat attens and is the tem ai eliabilit equation whee d (2) whee is a set of umulative attens used to etat tem ais. he attens ae soted b and the most eliable atten is used to etat new tem ais. he eliabilities of new tem ais ae also measued b. he eliable tem ais ae seleted b the eliabilities of tem ais and these tem ais ae used to etat new attens. Esesso eeats these stes seveal times and aumulates tem ais ontinuall. 261
2.2 Mateials We onstuted eeimental tet data onsisting of sentenes with OS tags. he sentenes wee etated fom Wikiedia 1 beause this web site was not a estited domain and we ould easil etat man sentenes. We emoved some tet eos using a tet edito (UltaEdit-32) and simle ules. We omiled a mass ous b gatheing 947625 sentenes and added OS tags to the mass ous using a mohologial analze (Stanfod tagge 2 ). We oded five ogams to oess the eeimental data. he ogams wee the tem ai etato the single atten etato the atten ai etato the set {tem ai single atten} etato and the semanti elation etato. hese wee develoed using a Java-based develoment tool (NetBeans IDE 6.1 Java 1.5). 2.3 Methods We etated is-a elations and at-of elations using ais of attens shaing a tem. Ou methods wee based on the Esesso algoithm. o do this wok we eaed seed tem ais etated neessa data fom the ous and modified eliabilit equations to al atten ais to the Esesso algoithm. 2.3.1. eaing seed tem ais We eaed 10 seed tem ais fo eah elation to etat single attens and atten ais in the fist iteation (able 1). Eah tem was a singula noun and had a OS tag. able 1: Seed tem ais with OS tag fo two semanti elations. Semanti elation Is-a elation at-of elation {wheat/nn o/nn} {memo/nn omute/nn} {miami/nn it/nn} {dawe/nn desk/nn} {shak/nn fish/nn} {oof/nn house/nn} {ale/nn fuit/nn} {hdogen/nn wate/nn} {man/nn human/nn} {head/nn bod/nn} {milk/nn beveage/nn} {banh/nn tee/nn} {flowe/nn lant/nn} {wing/nn ailane/nn} {omute/nn mahine/nn} {sea/nn eath/nn} {desk/nn table/nn} {lae/nn team/nn} {noise/nn sound/nn} {wheel/nn a/nn} 2.3.2. Etating data We etated tem ais single attens atten ais sets of {tem ai single atten} fom the mass ous and measued the fequen of eah. he tem ais wee etated when a ai of tems satisfing the egula eession (3) aeaed in one sentene. adj noun* noun e.? adj. noun* noun.. (3) In (3) the * oeato indiates thee ae zeo o moe eeding element and the? oeato indiates thee is zeo o one eeding element. he single attens wee etated when wods aeaed between the etated tems. When two tems aeaing befoe o afte two single attens wee the same tem (em C) the two single attens wee egaded as one atten ai 1 Wikiedia htt://www.wikiedia.og. his web site ovides baku dums of wikitet soue. We used the baku dum in 2008-06-13 as eeimental tet data. 2 Stanfod tagge htt://nl.stanfod.edu/softwae/tagge.shtml. 262
(Figue 1). atten ais wee goued into fou tes b ositions of the tems: {X C C Y}; {X C Y C}; {C X C Y}; {C X Y C}. Figue 1: Changing two single attens into one atten ai. em C aeas at the end of atten and the beginning of atten so and ae meged into a atten ai. 2.3.3. Modifing eliabilit equations We oosed two methods that modified the initial eliabilit equations (1) and (2). Method Ⅰ modified mi and df and Method Ⅱ used ombining statistial values fo eah atten of the ai and the weight of the shaed tem to modif (1) and (2). 2.3.3.1. Method Ⅰ o oose eliabilit equations that ould oess atten ais we modified mi and df as: df mi ai ai min 1 min ai ai log log * * ** ** * * * * * * ai * * ai min 1 min *** ai * * 1 ai * * * * * * * * * 1 (4) (5) whee ai is a atten ai ( ); * is an one of all tems o attens fo eah osition; and ae shaed tems; is a set of all tems. (4) and (5) wee alied to (1) and (2) of the Esesso algoithm. 2.3.3.2. Method Ⅱ We hanged the mutualit equation (d) between the tem ai and the atten in (1) and (2) of the Esesso algoithm into an equation fo atten ais using the ombining ds fo eah 263
atten of the atten ai and the weighted shaed tems. We defined the mutualit equation (d ai ) between the tem ai ( ) and the atten ai ( ) as: d d d d d d d ai 1 (6) whee is a shaed tem; and 1 - ae weights using fequenies of tem ais and attens fo eah d. he weight (Csoe) of the shaed tem () was defined as: Csoe. (7) We alulated d ai s and Csoes fo all shaed tems satisfing the atten ai and modified (1) and (2) of the Esesso algoithm as: d Csoe ai (8) d Csoe d ai. (9) 2.4 Aling modified eliabilities We alied thee ases (able 2) to is-a and at-of elation etations to omae the esults. Case 1 (evious method) used onl single attens and Case 2 (Method Ⅰ) and Case 3 (Method Ⅱ) used single attens and atten ais. Fo all thee ases we efomed 10 iteations and aumulated 200 tem ais. able 2: Equations of thee ases fo etating attens and tem ais. Case Equations used atten em ai to 2 single attens to single atten to atten ai to 20 tem ais 1 (1) not used not used (2) 2 not used (1) (1) aling (4) and (5) (2) aling (4) and (5) 3 not used (1) (8) (9) We evaluated the aua of the etated tem ais and measued the numbe of the new tem ais whih ould not be etated using single attens. he aua of the tem ais 264
was judged manuall b whethe the sentene that onsisted of the tem ai and the tial single atten efleting the taget elation was natual o not. Fo eamle if A is B was natual {A B} was is-a elation and if B onsists of A was natual {A B} was at-of elation. We divided the numbe of the etated tem ais satisfing the taget elation b the numbe of all the etated tem ais and egaded this value as the aua. We also ounted the new tem ais and evaluated the aua of these. 3 Results 3.1 Oveview We etated is-a elations and at-of elations using single attens and atten ais. We eaed 10 seed tem ais fo eah elation and etated 2409100 tem ais 13538 single attens 1012334 atten ais and 2674684 sets of {tem ai single atten} fom the mass ous. o genealize attens atiall we elaed tems in attens with the unique label (R) and etated onl attens that aeaed moe than 20 times in the mass ous. Fo eah elation etation we seleted the eliable attens using the seed tem ais and and seleted the to 20 tem ais using these attens and. hese tem ais wee used to etat new attens. We efomed 10 iteations and evaluated the aumulated 200 tem ais. 3.2 Is-a elation etation We alied Case 1 Case 2 and Case 3 to is-a elation etation. he suess ates wee 60.5% fo Case 1 67.5% fo Case 2 and 71.5% fo Case 3 (Figue 2). Comaed to Case 1 the auaies of Case 2 and Case 3 wee imoved b 7% and 11% esetivel. Case 2 etated 135 new tem ais whih ould not be etated using single attens (Case 1) and these new tem ais ahieved 62.22% aua. Case 3 etated 144 new tem ais and these tem ais ahieved 67.36% aua. 75 Aua [%] 68 61 54 1 4 7 10 Iteations Figue 2: Aua vs. iteation in thee ases of is-a elation etation. Filled tiangles shot-boken line: Case 1; Clea iles long-boken line: Case2; Filled iles solid line: Case 3. In is-a elations Case 1 ould not adequatel etat single attens satisfing the seed tem ais fom the mass ous. his situation odued low aua in the fist iteation and the tem ais that had low aua subsequentl etated new single attens that also had low eliabilit. Howeve Case 2 and Case 3 ould use the seed tem ais suessfull to etat atten ais and these atten ais ould also etat eliable tem ais that had high aua. Reliable tem ais onsistentl etated new eliable single attens and atten 265
ais (able 3) and this situation odued the high efomane. he high aua of the new tem ais was also one of easons fo the high efomane. able 3: Single attens and atten ais in eah iteation in Case 3 of is-a elation etation. Iteation atten Single atten atten ai 1 Y / o/ a/dt X {C on/in his/$ X C wee/vbd on/in the/dt Y} 2 Y and/ a/dt X {C / a/dt R / o/ a/dt X C / a/dt Y} 3 Y o/ a/dt X {X of/in the/dt R o/ a/dt C Y o/ a/dt R of/in the/dt C} 4 X and/ one/d Y {C eah/dt X C a/dt Y} 5 Y o/ b/in the/dt X {C of/in the/dt X C of/in the/dt following/vbg Y} 6 X / but/ fom/in Y {C of/in this/dt X C of/in the/dt following/vbg Y} 7 Y / and/ a/dt R / and/ a/dt X {X sine/in that/dt C Y at/in a/dt C} 8 X and/ thee/d Y {X afte/in that/dt C Y at/in a/dt C} 9 X and/ si/d Y {C within/in the/dt X Y at/in a/dt C} 10 X and/ seven/d Y {C within/in a/dt X Y at/in a/dt C} 3.3 at-of elation etation We alied Case 1 Case 2 and Case 3 to at-of elation etation. he auaies wee 84% fo Case 1 86.5% fo Case 2 and 88% fo Case 3 (Figue 3). Comaed to Case 1 the aua was 2.5% bette fo Case 2 and 4% bette fo Case 3. Case 2 etated 97 new tem ais and these tem ais ahieved 86.6% aua. Case 3 etated 85 new tem ais and these tem ais ahieved 91.76% aua. 90 Aua [%] 87 84 81 1 4 7 10 Iteations Figue 3: Aua vs. iteation in thee ases of at-of elation etation. Filled tiangles shot-boken line: Case 1; Clea iles long-boken line: Case2; Filled iles solid line: Case 3. In at-of elations Case 1 Case 2 and Case 3 ould suessfull etat single attens o atten ais satisfing the seed tem ais fom the mass ous but Case 1 ould not maintain the high efomane of the fist iteation. Howeve Case 2 and Case 3 maintained high efomanes beause these ases ontinuall etated new eliable single attens and atten ais in eah iteation (able 4) and deteted the new tem ais that had high aua. 266
able 4: Single attens and atten ais in eah iteation in Case 3 of at-of elation etation. Iteation atten Single atten atten ai 1 X of/in a/dt Y {X ove/in ou/$ C C of/in thei/$ R 's/os Y} 2 X of/in a/dt R 's/os Y {X ove/in ou/$ C C of/in the/dt Y} 3 Y and/ this/dt X {C leading/vbg to/to the/dt X C of/in the/dt Y} 4 X of/in that/dt Y {X ove/in thei/$ C C of/in thei/$ R 's/os Y} 5 X of/in an/dt Y {C of/in the/dt X C of/in the/dt R of/in a/dt Y} 6 X of/in one/d Y {C of/in the/dt X C of/in a/dt Y} 7 X of/in suh/jj a/dt Y {C -b-/-b- the/dt X C -b-/-b- a/dt Y} 8 X as/in a/dt Y {C -b-/-b- the/dt X C -b-/-b- if/in the/dt Y} 9 Y 's/os X {C -b-/-b- an/dt X C -b-/-b- a/dt Y} 10 Y whose/w$ X {C 's/os X Y 's/os R to/to a/dt C} 4 Disussion In etating is-a elations the auaies of ou methods Case 2 and Case 3 wee 67.5% and 71.5% esetivel. Comaed to the aua of the evious method Case 1 (60.5%) these wee imovements of 7% and 11% esetivel. In etating at-of elations the auaies of ou methods Case 2 and Case 3 wee 86.5% and 88%. Comaed to the aua of Case 1 (84%) these wee imovements of 2.5% and 4%. Fo eah elation Case 3 had the best efomane and these esults mean that the method using atten ais was useful in is-a and at-of elation etations and that the method of ombining statistial values fo eah atten of the ai and the weight of the shaed tem was moe useful than the othe methods. Single attens satisfing a seified tem ai ould not aea in the ous but atten ais satisfing the tem ai ould be etated fom the ous beause eah atten of the ai ould aea in the ous. his haateisti of atten ais enabled etation of eliable tem ais using the eliable atten ai. hese tem ais etated eliable single attens and atten ais onsistentl. Fo new tem ais whih ould not be etated using the evious method that onsideed onl single attens we etated 135 new is-a elations (tem ais) and 97 new at-of elations using Case 2 and 144 new is-a elations and 85 new at-of elations using Case 3. he auaies of these new tem ais wee 62.22% 86.6% 67.36% and 91.76% esetivel. Case 3 etated man new tem ais that had the best efomane and disoveies of these new tem ais wee one of the easons fo the good efomane of ou methods. We ould use the eliable new tem ais to etat single attens and atten ais beause atten ais ould etat eliable ais of tems in diffeent sentenes in the ous. he eliable atten ais deteted new tem ais onsistentl and the aumulated new tem ais odued the good efomane. Ou esults wee moe useful in is-a and at-of elation etations than the evious esults using single attens. We will onstut vaious mass ooa and eae seed tem ais fo vaious elations and al ou methods to vaious othe elations. 5 Conlusion his stud oosed a method that etated tem ais satisfing is-a elations o at-of elations fom a mass ous using ais of attens shaing a tem and omaed the esult with the evious aoah that used single attens. he oosed method ahieved 71.5% aua in deteting is-a elations and 88% aua in deteting at-of elations. Comaed to the evious method these wee imovements of 11% and 4% esetivel. Futhemoe we etated 144 new is-a elations and 85 new at-of elations whih ould not be etated 267
using single attens. hese esults will be useful in onstuting an ontolog and a thesauus beause these language knowledge bases onsisted mainl of is-a elations and at-of elations. Refeenes Beland M. and E. Chaniak. 1999. Finding ats in ve lage ooa. oeedings of Assoiation fo Comutational Linguistis 57-64. Giju R. A. Badulesu and D. Moldovan. 2003. Leaning semanti onstaints fo the automati disove of at-whole elations. oeedings of Human Language ehnolog / Noth Ameian Assoiation fo Comutational Linguistis 1-8. Heast M.A. 1992. Automati aquisition of honms fom lage tet ooa. oeedings of the 14th onfeene on Comutational linguistis vol. 2 539-545. Kavale M. and V. Svatek. 2005. A Stud on Automated Relation Labelling in Ontolog Leaning. Ontolog Leaning and oulation fom et: Methods Evaluation and Aliations IOS ess 44-58. Maedhe A. and S. Staab. 2002. Measuing similait between ontologies. Intenational Confeene on Knowledge Engineeing and Management LNAI 2473 251-263. antel. and D. Ravihandan. 2004. Automatiall labeling semanti lasses. oeedings of Human Language ehnolog / Noth Ameian Assoiation fo Comutational Linguistis 321-328. antel. and M. ennahiotti. 2006. Esesso: Leveaging genei attens fo automatiall havesting semanti elations. oeedings of Assoiation fo Comutational Linguistis 113-120. Ravihandan D. and E. Hov. 2002. Leaning sufae tet attens fo a question answeing sstem. oeedings of Assoiation fo Comutational Linguistis 41-47. Yang X. and J. Su. 2007. Coefeene esolution using semanti elatedness infomation fom automatiall disoveed attens. oeedings of Assoiation fo Comutational Linguistis 528-535. 268