ONTOLOGY-DRIVEN KNOWLEDGE-BASED HEALTH-CARE SYSTEM AN EMERGING AREA - CHALLENGES AND OPPORTUNITIES INDIAN SCENARIO

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1 ONTOLOGY-DRIVEN KNOWLEDGE-BASED HEALTH-CARE SYSTEM AN EMERGING AREA - CHALLENGES AND OPPORTUNITIES INDIAN SCENARIO Dr. Sunitha Abburu a, *, Sursh Babu Golla a a Dpt. of Computr Applications, Ahiyamaan Collg of Enginring, Tamilnau, Inia - (rsunithaabburu, sursh_babu_golla )@yahoo.com Commission TC VIII KEY WORDS: Knowlg Bas, Introprability, Qury, Visualization, Smantic Rtrival, Ontology ABSTRACT: Rcnt stuis in th cision making fforts in th ara of public halthcar systms hav bn trmnously inspir an influnc by th ntry of ontology. Ontology rivn systms rsults in th ffctiv implmntation of halthcar stratgis for th policy makrs. Th cntral sourc of knowlg is th ontology containing all th rlvant omain concpts such as locations, isass, nvironmnts an thir omain snsitiv intr-rlationships which is th prim objctiv, concrn an th motivation bhin this papr. Th papr furthr focuss on th vlopmnt of a smantic knowlg-bas for public halthcar systm. This papr scribs th approach an mthoologis in bringing out a novl concptual thm in stablishing a firm linkag btwn thr iffrnt ontologis rlat to isass, placs an nvironmnts in on intgrat platform. This platform corrlats th ral-tim mchanisms prvailing within th smantic knowlgbas an stablishing thir intr-rlationships for th first tim in Inia. This is hop to formulat a strong founation for stablishing a much await basic n for a maningful halthcar cision making systm in th country. Introuction through a wi rang of bst practics facilitat th aoption of this approach for bttr apprciation, unrstaning an long trm outcoms in th ara. Th mthos an approach illustrat in th papr rlat to halth mapping mthos, rusability of halth applications, an introprability issus bas on mapping of th ata attributs with ontology concpts in gnrating smantic intgrat ata riving an infrnc ngin for usr-intrfac smantic quris. 1. INTRODUCTION Halth plays a major an crucial rol in th lif of any iniviual an is a major concrn all ovr th worl. Halth car systms hav gain significant attntion an growth in rcnt yars. Halth bing a complx ntity, it pns on various factors lik nvironmnt, lif styl, social an conomic status tc. A complx analysis an multiisciplinary approach to knowlg is ssntial to unrstan th impact of various factors on public halth. Environmnt contributs an important paramtr in public halth. Th challngs for unrstaning an arssing th issus concrning th halth car systms ar us of Big ata, non-conformanc to stanars, htrognous sourcs (in htrognous ocumnts an formats), which n an immiat attntion towars multiisciplinary complx ata analytics an knowlg bas. Rcntly, th Ministry of Halth an Family Wlfar has approv th rcommnations ma by th mical council of Inia, on th stanars to b follow by th halth car organisations in th country (SHS, 2013a). It is mphasiz that ths stanars ar ynamically varying an activ ocumnts which unrgo a constant an rgular changs as pr th intrnational stanars. Th rgulations an th prioic rlass ar ivi into various catgoris for aaptation an as of aoption of ths accptabl stanars, which ar brifly list as follows: Vocabulary Stanars Contnt Exchang Stanars Clinical Stanars IT Stanars Softwar Stanars Data Privacy Scurity Stanars Aministrativ Safguars Stanars Physical Safguars Stanars, tc. Th primary n xist in bringing ths stanars togthr ar to: Promot introprability for spcific contnt xchang an vocabulary stanars to stablish a mtho for smantic introprability Support th volution an timly maintnanc an upgraation of aopt stanars Promot tchnical innovation using aopt stanars Encourag activ participation an aoption by all vnors an stakholrs to volv bttr halthcar systms Kp implmnt afforabl an rasonabl costs for wir usag Formulat bst practics, xprincs, policis an framworks in a ocumnt for wi publicity an xposur Aopt an aapt to stanars thus gnrat which ar moular an not intrpnnt Halth car ata is gnrat in various sourcs in ivrs formats using iffrnt trminologis. Du to th htrogonous formats an lack of common vocabulary, th accssibility an utility of th big ata of halthcar is vry minimal for halth ata analytics an cision support systms. Vocabulary stanars us to scrib clinical problms an procurs, mications, an allrgis. Various mical vocabulary This contribution has bn pr-rviw. oi: /isprsarchivs-xl

2 stanars, to nam fw ar (Colin t al., 2011; 3MHDD, 2014): Logical Obsrvation Intifirs Nams an Cos (LOINC ), Intrnational Classification of Disass (ICD10), Systmatiz Nomnclatur of Micin--Clinical Trms (SNOMED-CT), Currnt Procural Trminology, 4th Eition (CPT 4), ATC Anatomic Thraputic Chmical Classification of Drugs, Gn Ontology (GO), RxNorm, Gnral Equivalnc Mappings (GEMs), an Provir Taxonomy. Effctiv halth car systms can b built only whn th larg amount of isass ata can b brought to uniform formats with common vocabulary which will b rcognis by man-machin. Uniform formats with th hlp of stablishing th stanars ar prsntly n to b arss an ma unrstanabl by th man-machin intrfac through Ontology-tools an schms. Thrfor, ontology plays a vital rol in public halthcar systm incrasing th awarnss an ncssity an applications for bttr an afforabl public halth schms. Prsntly, th following organisations an agncis ar using ontologis in halth car systms: Worl Halth Organization (WHO), svral rsarch communitis an acamicians from Australia, Canaa, Czch Rpublic, Dnmark, Estonia, Hong Kong, Iclan, Isral, Lithuania, Malaysia, Malta, Nthrlans, Nw Zalan, Polan, Singapor, Slovak Rpublic, Slovnia, Spain, Swn, Unit Kingom, Unit Stats an Uruguay tc. Thy ar wily using mical an halth car ontologis to: Improv accuracy of iagnoss by proviing ral tim corrlations of symptoms, tst rsults an iniviual mical historis Hlp to buil mor powrful an mor introprabl information systms in halthcar Support th n of th halthcar procss to transmit, r-us an shar patint ata Provi smantic-bas critria to support iffrnt statistical aggrgations for iffrnt purposs Bring halthcar systms to support th intgration of knowlg an ata. Th currnt rsarch work focuss on th vlopmnt of an ontology rivn smantic knowlg-bas for public halthcar systm. This papr scribs th approach an mthoologis in bringing out a novl concptual thm in stablishing a firm linkag btwn thr iffrnt ontologis rlat to isass, placs an nvironmnts in on intgrat common platform that corrlats th tru ralitis xisting in th smantic knowlgbas an stablishing thir intr-rlationships scintifically. Th rst of th papr is organiz as follows. Sction 2 givs brif scription of rlat work, sction 3 scribs th currnt approach to buil knowlg bas, sction 4 scribs implmntation, sction 5 shows rsults, sction 6 conclus. 2. RELATED RESEARCH WORK Dvlopmnt of halthcar systms is vry complx an information tchnology tchniqus an applications hav brought a nw imnsion to th halth car systms. Halth car systms hav to consir various ata sourcs such as patint, isas, rug an nvironmnt tc. A cntralis an complt knowlg bas for public halth car systm is ssntial for ata analysis an cision making. Th major issus to buil a cntralis knowlg bas ar smantic an syntactic htrognity in halth ata. Prsnt ay rsarch has brought in various mthos an tchniqus. Thy ar summaris brifly in th following paragraphs. (Bkkum, 2013) illustrats various stps an stanars to b follow to vlop halth car ontologis. It also scribs various ontologis of halth car omain. Aiarus t al., (Aiarus t al., 2013) scrib an implmnt an ontology bas information rtrival systm for halth car information systm. Nigl t al., (Nigl t al., 2010) scrib a systm to intify halth rlat vnts from txt ocumnts using txt mining tchniqus an multilingual ontology. (Abullahi t al., 2010) illustrats implmntation of wb bas GIS systm for public halthcar cision support. It also scribs rusability of halth applications an introprability issus. (Stfan an Catalina, 2013) iscuss introprability improvmnt in halth car omain using omain ontologis. (Colin t al., 2011) scribs issus of ontology alignmnt, mapping an motivation in halth car omain to support patint ata intgration an analysis. (Gao t al., 2012) illustrats an architctur for smantic halth information rtrival by intgrating non-spatial smantics an gospatial smantics. Craig t al., (Craig an Kuzimsky, 2010) scrib a four stag approach for ontology bas halth information systm. Th four stags of th systm ar 1) spcification an concptualization 2) formalization 3) implmntation an valuation an maintnanc. (Davi t al., 2012) illustrats an ontology bas cision support systm for chronically ill patints by rlating prsonal ontology with patint ontology. Furkh t al., (Furkh an Raziah, 2012) iscuss vlopmnt of mical ontology in th ynamic halth car nvironmnt. Castllón t al., (Nurfsan t al., 2012) vlop an ontology bas GIS systm for public halth. (Lambrix an Tan, 2006; Jan-Mary t al., 2009; Hanif an Aono, 2009) scrib mical ontology mapping mthos. Th stuy on various mthos an tchniqus for public halth car systm hav inicat that thr xist th following opn challngs to b arss in orr to buil ffctiv knowlg bas an vlop a bttr halth car systm Ranomly maintain halth rcors gnrat by various halth cntrs with varying vocabulary structur Urgnt n to stablish smantic rlationship btwn halth an nvironmnt ata Non-availability of a cntralis knowlg bas that inclus halth, plac an nvironmnt ata Th currnt rsarch work is structur bas on th n for a robust mthoology that buils cntralis knowlg bas by arssing th abov challngs in a mthoical, wll-structur scintific approach. 3. APPROACH Th objctiv of th approach is builing an fficint an ffctiv smantic knowlg bas for public halth car systm. Th rsulting cntralis knowlg bas provis smantic association among isas ata with nvironmnt an plac information. Th approach is organiz in to th following aras: This contribution has bn pr-rviw. oi: /isprsarchivs-xl

3 3.1 Data Collction an Extraction Th major contributions hav bn assimilat bas on th ata collct from hospitals, clinics an mical cntrs rgaring th isas ata. MOSDOC is th major sourc for satllit ata focusing on nvironmntal factors. Th chosn stuy ara is Krishnagiri istrict, Tamilnau, Inia. Figur 1 shows stuy ara. INDIA Tamilnau homognous formats of EHR an EMR coul b riv from EMR an EHR stanars as pr th rgulations of mical council of Inia N for Stanars for EMR an EHR: Th major sourc for ffctiv halth car managmnt systm is rliabl ata. Larg volums of isas ata is availabl in various hospitals in iffrnt formats. To organis this ata structur, ffctiv ata xchang, rus an analysis tchniqus hlp in riving nw an ffctiv conclusions. Th ata xchang an rusability is possibl whn th ata is stor in stanar formats that achivs introprability. Incorporation of stanars for introprability of isas rcors, a tchnical solution is gnrat in th currnt rsarch work. Th knowlg bas is gnrat through th availabl voluminous halth ata which is thn maintain for rcors. This is achiv by machin larning an ata xtraction tchniqus. Disas ata prsnt in various formats is maintain in th prscrib formats of EMR an EHR by ata xtraction an machin larning algorithms shown in figur 2. A training algorithm is vlop with th support of training ata st that xtracts an brings out th ata in th stanar formats of EMR an EHR. Data from Halth Car Cntrs Figur 1. Stuy ara Halth an Disas Data Assimilation: Data is collct from various primary halth cntrs an Govrnmnt hospitals. Documnts an ata formats ar prsntly scattr in iffrnt formats in practical prspctiv. It is obsrv that, most of th hospitals in Inia ar maintaining hug ata in manual rcors an in tabular or xcl shts. No spcific stanar formats ar bn follow on par with ithr national or intrnational stanars of halth rcors. Rcntly, th mical council of Inia rlass rcommnations on EMR an EHR stanars, which has bn approv by ministry of halth an family wlfar, Govrnmnt of Inia (SHS, 2013). As pr th rcommnations an approv stanars by ministry of halth an family wlfar th following Minimum Data St (MDS) rquir for EHR to b us in Inia is as givn in tabl 1. An EHR is an volving concpt fin as a systmatic collction of lctronic halth information about iniviual patints or population (Guntr an Trry, 2005). EMR is a rpository of information rgaring th halth of a subjct of car in computr procssabl form that is abl to b stor an transmitt scurly, an is accssibl by multipl authoriz usrs (SHS, 2013a). Thr ariss a n for bringing halth rcors ata from htrognous formats to homognous formats. Th Data Extraction EMR, EHR an PHR Figur 2. Data xtraction from halth rcors into EMR an EHR formats UHID Altrnat UHID Nam Dat of Birth Ag Gnr Occupation Arss Typ Arss Lin 2 City/Town/Vill ag/polic Station District Stat Pin Co Country Co Phon Typ Phon Numbr Tabl 1. Stanars of EHR approv by ministry of halth an family wlfar. Arss Lin 1 ID This contribution has bn pr-rviw. oi: /isprsarchivs-xl

4 3.1.3 Data Formulation from Spatial an Environmntal Paramtrs: Mtorology an Ocan Satllit Data Archival Cntr (MOSDAC) (MOSDAC, 2013) is a major sourc of nvironmnt an plac ata. Th ata is rciv an availabl to th usr at rgular intrvals in HDF5 formats. Th currnt work vlops an algorithm using Java HDF API a srvic provi by HDF group (JHDF5, 2014). Th algorithm xtracts ata from HDF5 fils can b stor in CSV, RDF tc. formats as shown in figur 3. Thr is a n of intgrat ata from various halth organizations to buil a cntraliz knowlg bas. Rsourc Dscription Framwork (RDF) is a powrful notation that provis common structur an associat smantics to th ata. RDF rprsntation of halth ata is critical phas of builing cntral knowlg bas for public halth car systm. RDF rprsnts ata in machin unrstaning format an supports smantic qury on halth ata an construct RDF tripls from halth rcors. Plac an Environmnt Data from Satllits Figur 3. Extracting plac an nvironmnt ata from HDF5 fils 3.2 Domain Ontologis Ontology is a formal an xplicit spcification of a shar concptualization (Grubr, 1993). Domain ontologis provi vocabularis of concpts an thir rlations within a particular omain. Spatial an non-spatial ata plays a vital rol in halth car systms. It is wll known fact that impact of nvironmnt on isass is high. Thr is a strong potntial link btwn plac, nvironmnt an isass. Hnc, thr is a n for a tail stuy to stablish a firm link an corrlation btwn th concpts of ths omains. To stablish th smantic rlations, ontology plays a vital rol. Th ontologis stui for th currnt rsarch work ar isas, plac an nvironmnt ontologis. Th sourcs of th ontologis ar (BBO, 2014; SWEET, 2014; Protégé Ontology, 2014; DAML, 2014; Ontologis an Vocabularis, 2014). 3.3 Knowlg Bas Formulation HDF5 Rar Plac an Environmnt Data A complt halth knowlg bas hlps th policy makrs an halth profssionals to riv conclusions an tak right cisions. Th knowlg bas bcoms vry ffctiv cision making sourc. Ontology bas smatic tchniqus provis a grat support to buil ffctiv halth knowlg bas. Th ffctiv knowlg bas nabls to achiv (1) to provi a tail an soun scription of th rlation among, isas in rlation to th plac an th nvironmnt, (2) to automatically obtain an intrvntion plan rfrnc to th halth-car rquirmnts, (3) to hlp physicians in th procsss of isas prvntion an tction, an (4) to facilitat th task of fining th most accurat intrvntion in ach particular momnt unr th prvailing rlationships btwn isas, placs an nvironmnt, (5) this Ontology bas knowlg bas will hlp th policymakrs in volving national guilins through th rsults obtain through th intr-rlationships. Th knowlg bas formulation, is a two stag procss. Mrging an unification of multipl ontologis of a omain Fining th corrlation btwn isas, plac an nvironmnt Sinc ontology is collction of concpts an proprtis that scribs a rlationship. Various ontological lmnts can b formally fin as: O C P O Z c p o P 1 z O i z c ij z p ij P ij : Ontology st : Concpt st : Proprty st : Ontology st of omain z : Concpt : Proprty : Ontology : Driv proprty st : i th ontology of omain z : j th concpt of i th ontology of omain z : j th proprty of i th ontology of omain z : j th riv proprty of i th ontology p ij : Driv proprty btwn i th concpt of on ontology to j th concpt of anothr ontology in htrognous omains z : k th iniviual of j th concpt of i th ontology of omain z I ijk Mrging an Unification of Multipl Ontologis of a Domain: Many rsarchrs hav vlop multipl ontologis to rprsnt th knowlg of various omains. Thr is a strong n to intgrat th ontologis of a spcific omain by nhancing th rusability of th xisting ontologis an ruc th uplication of knowlg rprsntation of th sam omain. Th intgrat ontology rprsnts complt knowlg about th omain which can b shar among th groups. Multipl ontologis of a omain can b mrg (Sunitha an Sursh Babu, 2013) an th mrg procss involvs mapping concpts of iffrnt ontologis of a omain. Figur 4 shows multipl ontology mapping an mrging procss. Concptually th mrg procss stablishs th mapping btwn th concpts. AgrmntMakr (Danil t al., 2013) is an opn sourc ontology mapping tool. It intifis an rivs rlationships btwn concpts of iffrnt larg scal ontologis. Th currnt rsarch work aopts AgrmntMakr to map concpts of iffrnt ontologis of th sam omain, which can b riv using quation (1). P ij c ij c kl whr i = j or i j (1) Th final complt mrg ontology is obtain by mrging ontologis an riv proprtis, not as quation (2): This contribution has bn pr-rviw. oi: /isprsarchivs-xl

5 n n i=1 (2) o = i=1 C i P i P whr O = n Ontologis of th Sam Domain 0 : No corrlation f 1 : Strongly corrlat ij = { 0.5 : Moratly corrlat < 0.5 : Wakly corrlat. (3) Ontology Mapping an Mrging Statistical Data Analysis with Mining Tchniqus Appli on Ral Data: Voluminous isas ata is availabl all ovr th worl. Historical ata is a major sourc to corrlat th isas an nvironmnt ontologis. Effctiv analysis an mining tchniqus la to nw rivations an practical conclusions. Applying ata analytics an mining tchniqus rivs corrlation cofficint btwn concpts of isass an nvironmnt. Figur 5. Dpicts linkag of ontologis of htrognous ontologis. Corrlation cofficint btwn concpts c i an c j is not by r ci,c j an it is givn by quation (4) (Gupta an Kapoor, 2000): r ci,c j = COV(c i,c j ) σ ci.σ cj an 0 r ci,c j 1 (4) Ontologis of Htrognous Domains Exprt Knowlg Figur 4. Multipl ontology mapping an mrging Smantic Rlation Drivation btwn Concpts Corrlation btwn Disas, Plac an Environmnt: Mapping of isas ontology with plac an nvironmnt supports halth ata with nvironmnt in rlationship with placs. Ontology mapping hlps in builing ffctiv knowlg bas for halth that supports spatial an non-spatial smantic quris an fficint cision making. Statistical an Data Mining Tchniqus Th firm corrlation can b riv in two stags. Domain xprt knowlg Statistical ata analysis with mining tchniqus appli on ral ata Domain Exprt Knowlg: Du to highly tchnical an significant omain, builing knowlg bas rquirs strong collaboration among multipl omain xprts. Th xprt knowlg can b fram in th form of confinc matrix. Th matrix is rawn to intify th strngth of corrlation btwn th concpts of iffrnt omain ontologis which can b formally fin in tabl 2. C C c 1 c 2 -- c j -- c m c 1 f 11 f 12 - f 1j - f 1m c 2 f 21 f 22 - f 2j - f 2m c i f i1 f i2 f ij f im c n f n1 f n2 - f nj - f nm Tabl 2. Concpt corrlation tabl f ij Dnots corrlation btwn i th concpt of th nvironmnt: c i an j th concpt of isas: c j an 0 f ij 1, 1 i n an 1 j m. Rlation btwn th concpts can b riv quation (3). Figur 5. Linkag of ontologis of htrognous ontologis For instanc caus is th significant rlation btwn nvironmnt an isas an th corrlation cofficint of caus rlationship can b fin as shown in quation (5): 0 : No rlation 1 : Strong r ci,c = { j 0.5 : Morat < 0.5 : Wak. Th analysis of th corrlation btwn th concpts of iffrnt omains rsults in nw conclusion that provis impact on nvironmnt an causs of isass. Th riv conclusion is not as shown in quation (6): p H p ij c i c j Link Ontologis of Htrognous Domains Linkag btwn nvironmnt, plac an isas is rprsnt as shown in quation (7): p H ik c i c p k ; p H kj c p k c j p H ij c i c j (7) Figur 6 shows association of isas, plac an nvironmnt ata with ontologis. (5) (6) This contribution has bn pr-rviw. oi: /isprsarchivs-xl

6 Disas, Plac an Environmnt Ontologis Plac an Environmnt Data EMR, EHR an PHR Plac an Environmnt Data 5. RESULTS Th rsults from th implmntation of this approach ar vry promising. Figur 9 shows onto graph notation of link ontologis. Figur 10 picts RDF rprsntation of sampl EHR ata. Data Mapping <S><P><O> <S><P><O> <S><P><O> <S><P><O> Instanc Data Figur 6. Association of isas, plac an nvironmnt ata with ontologis Figur 9. Mapp ontology: maps isas, plac an nvironmnt ontologis 4. IMPLEMENTATION MECHANISM Th propos approach is implmnt using Java, Jna API (Apach Jna, 2014), oracl smantic stor (Chuck, 2014), Joski (Joski, 2013) an Sgvizlr Java script API (Martin, 2012). Figur 7 an 8 shows hom pag an smantic qury intrfac of th componnt. To valuat th propos approach, halth rcors ar collct from various hospitals in Hosur, Tamilnau, Inia. A ata st is crat in CSV format from th collct ata as pr stanars rcommn by EMR Stanars Committ. Th propos approach taks th sampl EHR ata st as input, xtracts ata, rprsnts in RDF an buils knowlg bas by mapping RDF tripls with appropriat concpts of th omain ontology. Figur 10. RDF tripls from th sampl EHR ata Lt Q1: fin srt placs whr mtabolism isass ar occurring, Q2: fin gnr ratio of snior citizns suffring from viral infctious isass in ti zon, ar xcut on th knowlg bas. Figur 11 an 12 show rsults of th two smantic quris rspctivly. Figur 7. Hom pag Figur 11. Rsults of Q1 Figur 8. Smantic qury intrfac Figur 12. Rsults of Q2 This contribution has bn pr-rviw. oi: /isprsarchivs-xl

7 6. CONCLUSION Th Ontology-cntric approach provis an innovativ, ffctiv an an fficint mans of capturing an organizing knowlg to rprsnt th mical halthcar omain ara for halthcar managmnt systm aiing th national policy makrs an th gnral public which is prsntly, th n of th hour. This papr prsnts a robust mthoology to vlop an implmnt a cntralis smatic knowlg bas for halthcar systm in Inia. This knowlg bas inclus mapping of isass with plac an nvironmntal ontologis. This is achiv through xtraction of ata from structur an smantic htrognous halth rcors rprsnting xtract ata in RDF tripl format associating RDF tripls with th appropriat omain ontology concpts. This infrs ata from omain ontologis an RDF tripls using Oracl nativ infrnc ngin using OWLPrim ruls. Th knowlg bas vlop for public halthcar using th prsnt approach supports spatial an non-spatial smantic quris nabling public halth car systm stakholrs to tak ffctiv an fficint cisions. This approach is valuat using ata collct from hospitals at Krishnagiri, Tamil Nau, Inia. ACKNOWLEDGEMENTS Th author woul lik to thank Dr Iyyanki V Murali Krishna an Dr.M.R.Nayak for thir constant support an valuabl inputs for this rsarch work. REFERENCES 3MHDD, M Halthcar Data Dictionary: Controll mical vocabulary srvr /mia/175976o/fact-sht-3m-halthcar-ata-ictionary-h pf?fn=ci_h_ovrviw _fs.pf (13 Nov. 2014). Abullahi, F. B., Lawal, M. M., Agushaka, J. O., Dsign An Implmntation Of A Wb-Bas Gis For Public Halthcar Dcision Support Systm in Zaria Mtropolis, IJRRAS, 4 (4), pp Aiarus, M. I., Hussin, A. H., Abullalm, A. M., Ontology-Drivn Information Rtrival for Halthcar Information Systm: A Cas Stuy, Intrnational Journal of Ntwork Scurity & Its Applications (IJNSA), 5 (1), January, pp Apach Jna, Apach Jna Rlass, (15 Fb. 2014). Bkkum, M. A. V., Stat of th art for Halthcar ontology, FP7 CRYSTAL projct livrabl (D ), vrsion 1.0., uploa/dlivrabls/crystal_d_407_010_v1.0.pf (31 Jan. 2013). Biological an Biomical Ontologis (BBO), Th Opn Biological an Biomical Ontologis obofounry. org/ (14 Jun. 2014). Chuck, M., Oracl Databas Smantic Tchnologis Dvlopr's Gui, 11g Rlas 2 (11.2), com/c/e11882_01/appv.112/25609.pf (Jan. 2014). Colin, P., Karthik, G., Prtk, J. t al., Multipl Ontologis in Halthcar Information Tchnology: Motivations an Rcommnation for Ontology Mapping an Alignmnt, In Proc: Intrnational confrnc on Biomical Ontologis, NY, USA, pp Craig, E., Kuzimsky, F. L., A Four Stag Approach for Ontology-Bas Halth Information Systm Dsign, Artificial Intllignc in Micin, pp: DAML Ontology, DAML Ontology Library (4 Aug. 2014). Danil, F., Catia, P., Emanul, S., Matto, P., Isabl, F. C., Francisco, M. C., Th AgrmntMakrLight Ontology Matching Systm, Lctur Nots in Computr Scinc (LNCS), Springr, 8185, pp Davi, R., Francis, R., Joan, A. L., Fabio, C., Sara, E., Patrizia, M., Robrta, A., Carlo, C., An Ontology-Bas Prsonalization of Halth-Car Knowlg to Support Clinical Dcisions for Chronically Ill s, Journal of Biomical Informatics, pp: Furkh, Z., Raziah, M., Mical Ontology in th Dynamic Halthcar Environmnt, In Proc.: 3r Intrnational Confrnc on Ambint Systms, Ntworks an Tchnologis (ANT), pp Gao, S., Anto, F., Mioc, D., Boly, H., Non-Spatial an Gospatial Smantic Qury of Halth Information, In Proc.: Intrnational Archivs of th Photogrammtry, Rmot Snsing an Spatial Information Scincs, Volum XXXIX-B2, 2012, ISPRS Congrss, 25 August 01 Sptmbr, Mlbourn, Australia. Grubr. T. R., A Translation Approach to Portabl Ontology Spcifications, Knowlg Acquisition, 5, pp Guntr, T. D., Trry, N. P., Th Emrgnc of National Elctronic Halth Rcor Architcturs in th Unit Stats an Australia: Mols, Costs, an Qustions, Journal of Mical Intrnt Rsarch, 7(1), /articls/pmc / (14 Mar. 2005). Gupta, S.C. an Kapoor, V.K., Funamntals of Mathmatical Statistics a Morn Approach, 10th Eition. Hanif, M. S., Aono, M., An Efficint an Scalabl Algorithm for Sgmnt Alignmnt of Ontologis of Arbitrary Siz, Journal of Wb Smantics, 7(4), pp Jan-Mary, Y. R., Shironoshita, E. P., Kabuka, M. R., Ontology Matching with Smantic Vrification, Journal of Wb Smantics, 7 (3), pp JHDF5, JHDF5 (HDF5 for Java), (25 May 2014). Joski, Oracl: SPARQL Enpint (27 Fb. 2014). Lambrix, P., Tan, H., SAMBO - A Systm for Aligning an Mrging Biomical Ontologis, Journal of Wb Smantics, 4 (1), pp This contribution has bn pr-rviw. oi: /isprsarchivs-xl

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