Introduction to the W3C for Semantic Web Health Care and ... · Drug Discovery Technology of...

Introduc tion to the W3C for S emantic Web H ea lth C are and

L ife S c ienc es Interes t G roup

M . S c ott M ars ha llH C LS IG c o-cha ir

K now ledg e

“data”, “information”, “fac ts ”, “know ledg e”

K now ledg e is a s ta tement tha t c an be tes ted for truth.

(by a m ac hine)Otherw is e, the c om puter c an’t help

very m uc h

V is ion: C onc ept-bas ed interfac es

• T h e s c ie n t is t s h o u ld b e a b le t o w o r k in te rm s o f c om m only us ed c o n c e p t s .

• T h e s c ie n t is t s h o u ld b e a b le t o w o r k in te rm s o f pers onal c onc epts a n d hypothes es .

- N o t b e fo r c e d to m a p c o n c e p t s to th e te rm s t h a t h a v e b e e n c h o s e n fo r a g iv e n a p p l ic a t io n b y th e a p p l ic a t io n b u i ld e r .

Interfac e S ketc h:Finding a bas is for rela tion

Epigenetic Mechanisms Transcription

Chromatin Transcription Factors

“There is a relation”

Common DomainInstancesClasses

Hypothesis

Histone Modification

Transcription Factor Binding Sites

position

KSinBIT’06

B iolog ic a l c a rtoon a s interfac e

Source: Marco Roos

B irth of the H C LS IG

• W 3C W o r k s h o p o n S e m a n t ic W e b fo r L i f e S c ie n c e s , O c to b e r 2 0 0 4 , C a m b r id g e , M A , U S A

• 1 1 5 p a r t ic ip a n t s

• h t t p ://w w w .w 3 .o r g /2 0 0 4 /0 7 /s w ls -w s .h tm l

• H C L S IG C h a r t e r in e a r ly 2 0 0 5

R ec harter in June 2008

• C o -c h a ir s C h im e z ie O g b u j i (C le v e la n d C l in ic F o u n d a t io n ), M . S c o t t M a r s h a l l (U n iv e r s i t y o f A m s te r d a m ), S u s ie S te p h e n s (E l i L i l ly )

• W 3 C te a m c o n ta c t is E r ic P r u d ’h o m m e a u x (W 3 C )

• 8 9 fo rm a l p a r t ic ip a n t s a c r o s s p h a rm a , h e a l t h c a r e , a c a d e m ia , a n d t e c h n o lo g y c o m p a n ie s

• M a i l in g l is t o f >5 0 0 p e o p le

What is the M is s ion of H C LS IG ?

• T h e m is s io n o f th e H C L S IG is to develop, advoc ate, and s upport the us e of S emantic Web tec hnolog ies for biolog ic a l s c ienc e, trans la tiona l medic ine and hea lth c a re. T h e s e d o m a in s s ta n d to g a in t r e m e n d o u s b e n e f i t b y a d o p t io n o f S e m a n t ic W e b te c h n o lo g ie s , a s th e y d e p e n d o n th e in te r o p e r a b i l i t y o f in f o rm a t io n f r o m m a n y d o m a in s a n d p r o c e s s e s fo r e f f ic ie n t d e c is io n s u p p o r t .

What does the g roup do?

• Doc ument us e c as es to a id in d iv id u a ls in u n d e r s t a n d in g t h e b u s in e s s a n d te c h n ic a l b e n e f i t s o f u s in g S e m a n t ic W e b te c h n o lo g ie s

• Doc ument g uidelines t o a c c e le r a t e th e a d o p t io n o f t h e te c h n o lo g y

• Implement a s e le c t io n o f the us e c as es a s p r o o f-o f-c o n c e p t d e m o n s t r a t io n s

• E x p lo r e th e p o s s ib i l i t y o f developing h ig h -le v e l voc abula ries

• Dis s eminate information a b o u t t h e g r o u p 's w o r k a t g o v e r n m e n t , in d u s t r y , a n d a c a d e m ic e v e n t s

H C LS IG Doc uments

HC LS Web S ite

Source: http://www.w3.org/2001/sw/hcls/

H C LS Wik i

Source: http://esw.w3.org/topic/HCLSIG

M otiva tion for N ew M embers

• B enefits to g etting involved inc lude:– e a r ly a c c e s s to u s e c a s e s a n d b e s t p r a c t ic e

– in f lu e n c e s t a n d a r d r e c o m m e n d a t io n s

– c o s t e f f e c t iv e e x p lo r a t io n o f n e w te c h n o lo g y th r o u g h c o l la b o r a t io n

– n e tw o r k w i t h o t h e r s w o r k in g w it h S e m a n t ic W e b

• G et involved by c ontac ting the c ha irs :– t e a m -h c ls -c h a ir s @ w 3 .o r g

• Forma l F2F, January 2006, C ambridg eForma l F2F, Oc tober 2006, Ams terdam

• Works hop, IS WC N ovember 2006, B anff• Informa l F2F (Demo) 3 x M arc h/April,

2007, C ambridg eWorks hop, WWW 2007, M ay 2007, B anff

• Informa l F2F (U R I), July 2007, C ambridg eForma l F2F, N ovember 2007, C ambridg eForma l F2F, Oc tober 2008, M andelieu

http://es w .w 3.org /topic /H C LS IG /F2F/2008-10_F2F

•

M eeting s

•

S ubmitted to B riefing s in B ioinformatic s S pec ia l Is s ue:� Life S c ienc es on the S em antic Web: The N euroc ommons and

B eyond

A lan R uttenberg , Jonathan R ees , M atthia s S am w a ld, M . S c ott M ars ha ll

In P rint:� A dvanc ing trans la tiona l res earc h w ith the S em antic Web

A lan R uttenberg , Tim C la rk, William B ug , M atthia s S amw a ld, O livier B odenreider, H elen C hen, Dona ld Doherty, K ers tin Fors berg , Y ong G ao, V ipul K as hyap, June K inos hita , Joanne Luc iano, M S c ott M ars ha ll, C himezie Og buji, Jonathan R ees , S us ie S tephens , G w endolyn T Wong , E lizabeth Wu, Davide Z ac c ag nini, Tonya H ong s ermeier, E ric N eumann, Ivan H erman and K ei-H oi C heung , B M C B ioinformatic s 2007, 8(S uppl 3):S 2

� A n Ontolog y-bas ed approac h for Da ta Integ ra tion - An A pplic a tion in B iomedic a l R es ea rc h V ipul K as hyap, K ei-H oi C heung , Don Doherty, M a tthia s S amw a ld, M . S c ott M ars ha ll, Joanne Luc iano, S us ie S tephens , Ivan H erm anand R ay H ookw ay, B ook C hapter in C a rdos o, J ., H epp, M .,Lytra s , M . (E ds .) "R ea l-w orld Applic a tions of S emantic Web Tec hnolog y and Ontolog ies " , S pring er 2007.

Public a tions

• WWW2007 Demo

IS M B 2007 Dem o• IS M B B ioOntolog y S IG Pos ter 2007• S oc iety for N euros c ienc e Pos ter N ov 2007

• S elec tion of pres enta tion venues of m embers s how ing H C LS w orkB ridg ing Pha rma a nd IT

� Drug D is c overy Tec hnolog y of Innova tive Therapeutic s� 1s t E uropea n S em antic Web C onferenc e� B io-IT World� N orw eg ian S ema ntic Web Day� InfoTec h Pha rm a� M odern D rug D is c overy and Development S ummit� M a s s ac hus etts B iotec hnolog y Pa nel� eS c ienc e Ins titute; R DF, Ontolog ies a nd M eta -Da ta Works hop� V irg inia B iotec hnolog y S ummit� S ys tem s B io log y� S emantic Web G a thering� A llen Ins titute for B ra in S c ienc es� Informatic s a nd Interac tomes in H unting ton’s D is ea s e� Ontolog y for B iomedic a l Informa tic s Works hop� C linic a l Tria l Ontolog y Works hop� J a c ks on Labora tories� Pubmed P lus� N IH B lueprint N IF Works hop

Pres enta tions -> 2007

M otiva tion for N ew M embers






• A s ian S emantic Web C onferenc e, H C LS Works hop

• S WA T4LS 2008 Works hop

• IS WC 2008 S em antic Web for H ea lth C are and Life S c ienc es Tutoria l

R eas oning Web S um mer S c hool 2008

• WWW2008 Works hop

• IS M B 2008 Dem o• S elec tion of pres enta tion venues of members

s how ing H C LS w ork:� Drug D is c overy Tec hnolog y of Innova tive Therapeutic s� E uropean S emantic Web C onferenc e� B io-IT World� InfoTec h Pharma 2008� …

Pres enta tions and Outreac h in 2008

Tas k Forc es

• Terminolog y – S emantic Web repres enta tion of exis ting res ourc es– T a s k le a d - J o h n M a d d e n

• B ioR DF – integ ra ted neuros c ienc e know ledg e bas e– T a s k le a d - K e i C h e u n g

• L ink ing Open Drug Da ta – ag g reg a tion of Web-bas ed drug da ta – T a s k le a d - C h r is B iz e r

• S c ientific D is c ours e – building c ommunities throug h netw orking– T a s k le a d s - T im C la r k , J o h n B r e s l in

• C linic a l Obs ervations Interoperability – pa tient rec ruitment in tria ls– T a s k le a d - V ip u l K a s h y a p

• O ther P rojec ts : C linic a l Dec is ion S upport, U R I Works hop, C ollaborations w ith C D IS C & H L7

Terminolog y: Overview

• G o a l is t o id e n t i f y u s e c a s e s a n d m e th o d s f o r e x t r a c t in g S e m a n t ic W e b r e p r e s e n t a t io n s f r o m e x is t in g , s t a n d a r d m e d ic a l r e c o r d te rm in o lo g ie s , e .g . U M L S

• M e t h o d s s h o u ld b e r e p r o d u c ib le a n d , to th e e x t e n t p o s s ib le , n o t lo s s y

• I d e n t i f y a n d d o c u m e n t is s u e s a lo n g th e w a y r e la t e d to id e n t i f ic a t io n s c h e m e s , e x p r e s s iv e n e s s o f t h e r e le v a n t la n g u a g e s

• I n i t ia l e f f o r t w i l l s t a r t w i t h S N O M E D -C T a n d U M L S S e m a n t ic N e tw o r k s a n d f o c u s o n a p a r t ic u la r s u b -d o m a in (e .g . p h a rm a c o lo g ic a l c la s s i f ic a t io n )

B ioR DF: A ns w ering Ques tions

G oa ls : G e t a n s w e r s to q u e s t io n s p o s e d t o a b o d y o f c o l le c t iv e k n o w le d g e in a n e f f e c t iv e w a y

K now ledg e us ed: P u b l ic ly a v a i la b le d a t a b a s e s , a n d te x t m in in g

S tra teg y: In t e g r a t e k n o w le d g e u s in g c a r e f u l m o d e l in g , e x p lo i t in g S e m a n t ic W e b s ta n d a r d s a n d te c h n o lo g ie s

B ioR DF: Looking for Targ ets for A lzheimer’s

• S ig n a l t r a n s d u c t io n p a th w a y s a r e c o n s id e r e d to b e r ic h in “d r u g g a b le ” ta r g e t s

• C A 1 P y r a m id a l N e u r o n s a r e k n o w n to b e p a r t ic u la r ly d a m a g e d in A lz h e im e r ’s d is e a s e

• C a s t in g a w id e n e t , c a n w e f in d c a n d id a t e g e n e s k n o w n to b e in v o lv e d in s ig n a l t r a n s d u c t io n a n d a c t iv e in P y r a m id a l N e u r o n s ?

Source: Alan Ruttenberg

NeuronDB

BAMS

Literature

Homologene

SWAN

Entrez Gene

Gene Ontology

Mammalian Phenotype

PDSPki

BrainPharm

AlzGene

Antibodies

PubChem

MESH

Reactome

Allen Brain Atlas

BioRDF: Integrating Heterogeneous Data

Source: Susie Stephens

“find me g enes involved in s ig na l trans duc tion tha t a re rela ted to

pyram ida l neurons ”

S c ientific Ques tion

B ioR DF: S PAR QL Query


B ioR DF: R es ults : G enes , P roc es s es

• DR D1, 1812 adenyla te c yc la s e ac tiva tion• A DR B 2, 154 adenyla te c yc la s e ac tiva tion• A DR B 2, 154 arres tin m edia ted des ens itiza tion of G -protein c oupled rec eptor protein

s ig na ling pa thw ay• DR D1IP , 50632 dopamine rec eptor s ig na ling pa thw ay• DR D1, 1812 dopamine rec eptor, adenyla te c yc la s e ac tiva ting pa thw ay• DR D2, 1813 dopamine rec eptor, adenyla te c yc la s e inhibiting pa thw ay• G R M 7, 2917 G -protein c oupled rec eptor protein s ig na ling pa thw ay• G N G 3, 2785 G -protein c oupled rec eptor protein s ig na ling pa thw ay• G N G 12, 55970 G -protein c oupled rec eptor protein s ig na ling pa thw ay• DR D2, 1813 G -protein c oupled rec eptor protein s ig na ling pa thw ay• A DR B 2, 154 G -protein c oupled rec eptor protein s ig na ling pa thw ay• C A LM 3, 808 G -protein c oupled rec eptor protein s ig na ling pa thw ay• H TR 2A , 3356 G -protein c oupled rec eptor protein s ig na ling pa thw ay• DR D1, 1812 G -protein s ig na ling , c oupled to c yc lic nuc leotide s ec ond m es s eng er• S S TR 5, 6755 G -protein s ig na ling , c oupled to c yc lic nuc leotide s ec ond m es s eng er• M TN R 1A , 4543 G -protein s ig na ling , c oupled to c yc lic nuc leotide s ec ond m es s eng er• C N R 2, 1269 G -protein s ig na ling , c oupled to c yc lic nuc leotide s ec ond m es s eng er• H TR 6, 3362 G -protein s ig na ling , c oupled to c yc lic nuc leotide s ec ond m es s eng er• G R IK 2, 2898 g lutamate s ig na ling pa thw ay• G R IN 1, 2902 g lutamate s ig na ling pa thw ay• G R IN 2A , 2903 g lutamate s ig na ling pa thw ay• G R IN 2B , 2904 g lutamate s ig na ling pa thw ay• A DA M 10, 102 integ rin-m edia ted s ig na ling pa thw ay• G R M 7, 2917 neg a tive reg ula tion of adenyla te c yc la s e ac tivity• LR P 1, 4035 neg a tive reg ula tion of Wnt rec eptor s ig na ling pa thw ay• A DA M 10, 102 N otc h rec eptor proc es s ing• A S C L1, 429 N otc h s ig na ling pa thw ay• H TR 2A , 3356 s erotonin rec eptor s ig na ling pa thw ay• A DR B 2, 154 trans m em brane rec eptor protein tyros ine k ina s e ac tiva tion (dimeriza tion)• PTP R G , 5793 rans m em brane rec eptor protein tyros ine k inas e s ig na ling pa thw ay• E PH A 4, 2043 trans m em brane rec eptor protein tyros ine k ina s e s ig na ling pa thw ay• N R TN , 4902 trans m em brane rec eptor protein tyros ine k ina s e s ig na ling pa thw ay• C TN N D 1, 1500 Wnt rec eptor s ig na ling pa thw ay

Many of the genes are related to AD through gamma

secretase (presenilin) activity


Tec hnolog y

S o fa r about 350M triples (~20G b on dis k)Openlink V irtuos o - open s ourc e triple s toreC ommodity H ardw are: 2x2c ore duo/2 dis ks /8G R amB ig g es t s o fa r is M eS H as s oc ia tions to a rtic les (200M triples )S ma ller, from 10K to 10M triples /s ourc eA va ilable for dow nload and m irroring


LODD: Introduc tion

B C

Thing

typedlinks

A D E

typedlinks

typedlinks

typedlinks

Thing

Thing

Thing

Thing

Thing Thing

Thing

Thing

Thing

Search Engines

Linked DataMashups

Linked DataBrowsers

Use Semantic Web technologies to1. publish structured data on the Web2. set links between data from one data source to data within other data sources

Source: Chris Bizer

Linked Da ta P rinc iples

1. U s e U R Is a s names for thing s .2. U s e H TTP U R Is s o that people c an

look up thos e names .3. When s omeone looks up a U R I,

provide us eful R DF information.4. Inc lude R DF s ta tements that link to

other U R Is s o that they c an dis c over rela ted thing s .

• Tim B erners -Lee 2007• http://w w w .w 3.org /Des ig nIs s ues /L inkedData .htm l

LODD: Potentia l L inks betw een Data S ets

Source: Chris Bizer

LODD: Da ta S et E va lua tion

Source: Chris Bizer

LODD: Potentia l ques tions to ans w er

• P h y s ic ia n s a n d P h a rm a c is t s– W h a t a r e a l t e r n a t iv e d r u g s fo r a g iv e n in d ic a t io n (d is e a s e )?

– W h a t a r e e q u iv a le n t d r u g s (g e n e r ic v e r s io n o f a b r a n d n a m e , o r th e c h e m ic a l n a m e o f a a c t iv e in g r e d ie n t)?

– A r e th e r e o n g o in g c l in ic a l t r ia ls fo r a d r u g ?

• P a t ie n t s– W h a t b a c k g r o u n d in f o rm a t io n is a v a i la b le a b o u t a d r u g ?

– W h a t a r e th e c o n t r a in d ic a t io n s o f a d r u g ?

– W h ic h a l t e r n a t iv e d r u g s a r e a v a i la b le ?

– W h a t a r e th e r e s u l t s o f c l in ic a l t r ia ls fo r a d r u g ?

• P h a rm a c e u t ic a l C o m p a n ie s– W h a t a r e o th e r c o m p a n ie s w it h d r u g s in s im i la r a r e a s ?

– W h ic h c o m p a n ie s h a v e a s im i la r t h e r a p e u t ic fo c u s ?

Source: Chris Bizer

LODD: L inked V ers ion of C linic a lTria ls .g ov

• T o t a l n u m b e r o f t r ip le s : 6 ,9 9 8 ,8 5 1

• N u m b e r o f T r ia ls : 6 1 ,9 2 0

• R D F l in k s t o o th e r d a ta s o u r c e s : 1 7 7 ,9 7 5

• L in k s to :

– D B p e d ia a n d Y A G O (f r o m in t e r v e n t io n a n d c o n d i t io n s )

– G e o N a m e s (f r o m lo c a t io n s )

– B io 2 R D F .o r g 's P u b M e d (f r o m r e f e r e n c e s )Source: Chris Bizer

LODD: M as hing C linic a l Tria ls and G eo

Classification of Places

GeoCoordinates

Source: Chris Bizer

S c ientific D is c ours e: Overview

Source: Tim Clark

S c ientific D is c ours e: G oa ls

• P rovide a S emantic Web pla tform for s c ientific dis c ours e in biomedic ine– Linked to

• key c onc epts , entities and know ledg e

– S pec ified• by ontolog ies

– Integ ra ted w ith• exis ting s oftw are tools

– U s eful to• Web c ommunities of w ork ing s c ientis tsSource: Tim Clark

S c ientific D is c ours e: S ome Parameters

• D is c o u r s e c a te g o r ie s : r e s e a r c h q u e s t io n s , s c ie n t i f ic a s s e r t io n s o r c la im s , h y p o th e s e s , c o m m e n t s a n d d is c u s s io n , a n d e v id e n c e

• B io m e d ic a l c a t e g o r ie s : g e n e s , p r o t e in s , a n t ib o d ie s , a n im a l m o d e ls , la b o r a t o r y p r o t o c o ls , b io lo g ic a l p r o c e s s e s , r e a g e n t s , d is e a s e c la s s i f ic a t io n s , u s e r -g e n e r a t e d ta g s , a n d b ib l io g r a p h ic r e f e r e n c e s

• D r iv in g b io lo g ic a l p r o je c t : c r o s s -a p p l ic a t io n o f d is c o v e r ie s , m e th o d s a n d r e a g e n t s in s te m c e l l , A lz h e im e r a n d P a r k in s o n d is e a s e r e s e a r c h

• In f o rm a t ic s u s e c a s e s : in t e r o p e r a b i l i t y o f w e b -b a s e d r e s e a r c h c o m m u n i t ie s w i t h (a ) e a c h o th e r (b ) k e y b io m e d ic a l o n t o lo g ie s (c ) a lg o r i t h m s fo r b ib l io g r a p h ic a n n o ta t io n a n d te x t m in in g (d ) k e y r e s o u r c e s

Source: Tim Clark

S c ientific D is c ours e: S WA N +S IOC

• S IOC - S emantic a lly-Interlinked Online C ommunities– R epres ent ac tivities and c ontributions of

online c ommunities– Integ ra tion w ith blog g ing , w ik i and C M S

s oftw are– U s e of exis ting ontolog ies , e.g . FOAF, S K OS ,

DC

• S WAN - S emantic Web Applic a tions in N euromedic ine– R epres ents s c ientific dis c ours e (hypothes es ,

c la ims , evidenc e, c onc epts , entities , c ita tions )– U s ed to c rea te the S WAN A lzheimer know ledg e

bas e– Ac tive beta partic ipa tion of 144 A lzheimer

res earc hers– Ong oing integ ra tion into S C F Drupa l toolk it

Source: Tim Clark

S c ientific D is c ours e: S IOC Ontolog y

Source: John Breslin

S c ientific D is c ours e: S WA N K B

Source: Tim Clark

C OI: B ridg ing B enc h to B eds ide

• H o w c a n e x is t in g E le c t r o n ic H e a l t h R e c o r d s (E H R ) fo rm a t s b e r e u s e d fo r p a t ie n t r e c r u i tm e n t?

• Q u a s i s ta n d a r d fo rm a t s fo r c l in ic a l d a ta :– H L 7 /R IM /D C M – h e a l t h c a r e d e l iv e r y s y s te m s

– C D IS C /S D T M – c l in ic a l t r ia l s y s te m s

• H o w c a n w e m a p a c r o s s th e s e fo rm a t s ?– C a n w e a s k q u e s t io n s in o n e fo rm a t w h e n th e d a ta is r e p r e s e n t e d in a n o th e r f o rm a t?

Source: Holger Stenzhorn

C OI: U s e C as e

P h a rm a c e u t ic a l c o m p a n ie s p a y a lo t t o te s t d r u g s

P h a rm a c e u t ic a l c o m p a n ie s e x p r e s s p r o to c o l in C D IS C

-- p r e c ip i t o u s g a p –

H o s p i t a ls e x c h a n g e in fo rm a t io n in H L 7 /R IM

H o s p i t a ls h a v e r e la t io n a l d a ta b a s e s

Source: Eric Prud’hommeaux

• Type 2 diabetes on diet and exercise therapy or• monotherapy with metformin, insulin• secretagogue, or alpha-glucosidase inhibitors, or• a low-dose combination of these at 50%• maximal dose. Dosing is stable for 8 weeks prior• to randomization. • …• ?p a t i en t t a k es m ef o r m i n .

Inc lus ion C riteria


E xc lus ion C riteria

Use of warfarin (Coumadin), clopidogrel(Plavix) or other anticoagulants.…?p a t i en t d oesNo t Ta k e an t i c o a gu l a n t .


?m ed i c a t i o n 1 sd t m :su b j ec t ?p a t i en t ;sp l :a c t i ve In g r ed i en t ?i n g r ed i en t 1 .

?i n gr ed i en t 1 sp l :c l a ssCod e 6809 . #m et f o r m i n

OPTIONAL {

?m ed i c a t i o n 2 sd t m :su b j ec t ?p a t i en t ; sp l :a c t i v e In g r ed i en t ?i n g r ed i en t 2 .?i n gr ed i en t 2 sp l :c l a ssCod e 1 1 289 . #an t i c o a gu l a n t

} FILTER (!BOUND(?m ed i c a t i o n 2))

C riteria in S PAR QL


Other applic a tions of C O I tec h

• A pply bridg ing tec hnique to bridg e M eS H terms and terms in H C LS K now ledg e B as e

C ha lleng es

• S u p p o r t o f le g a c y d a ta (b a s e s )

• I n t e r f a c e (e .g . s u p p o r t f o r a u to -c o m p le t io n )

• T e rm in o lo g y a l ig n m e n t

• Q u e r y t r a n s la t io n te c h n o lo g y (fe d e r a t e d in s t e a d o f w a r e h o u s e )

• L a r g e s c a le r e a s o n in g (o v e r la r g e K B )

• I n t e g r a t in g k n o w le d g e a b o u t c h e m ic a l c o m p o u n d s (U R I 's f o r c h e m ic a l c o m p o u n d s !)

C ha lleng es of s pec ia l importanc e to L ife

S c ienc es• S e m a n t ic a n n o ta t io n o f im a g e d a ta

– s p a t ia l iz a t io n , r e g io n m a r k u p , c o m m o n c o o r d in a t e s y s t e m s

• S e m a n t ic a n n o ta t io n o f S p r e a d s h e e t d a ta

• S e m a n t ic a n n o ta t io n o f R s c r ip t s

N ew Tec hnolog ies

• S P A R Q L -D L

• S e m a n t ic W ik i (in t e g r a t io n w i t h K B ’s )

• C lo u d C o m p u t in g (e .g . A m a z o n )

• Q u e r y r e w r i t in g : S P A R Q L -> S Q L– L e g a c y in t e g r a t io n

– Im p r o v e in t e r f a c e s

• S P A R Q L -F e d : F e d e r a t e d q u e r y

P lus c a c hang e..

..plus c ’es t la meme c hos e.• M odels c hang e:

– E p ig e n e t ic s : T h e c o d e is n ’t o n ly in th e D N A

– P o ly p h a rm a c o lo g y : W e s h o u ld a im fo r l ig a n d s w i t h m u l t ip le ta r g e t s .

• Terminolog ies c hang e:– I n P e r s o n a l M e d ic a l R e c o r d s :

• P e r u s e r : p a t ie n t , c l in ic ia n

Why S ynonym S ervic es ?

Example: CBP has now officially been renamed KAT3A p300 is now KAT3B

See new nomenclature rules for chromatin remodellers: Cell, Vol 131, 633-636, 16 November 2007

S PAR QL-DL

Life Sciences on the Semantic Web: The Neurocommons and Beyond,

Ruttenberg et al.


Ruttenberg et al.

D is tributed Query - B efore


Ruttenberg et al.

D is tributed Query - A fter


Ruttenberg et al.

S omeday, w e s hould be able to find this a s evidenc e for a fac t in the K now ledg e B as e

R ec ipe for a S emantic Web

• F o l lo w L in k e d O p e n D a ta p r in c ip le s

• A t t e m p t to u s e S h a r e d N a m e s (s a m e U R I ’s )

• Q u e r y r e w r i t in g to m a p f r o m : – S P A R Q L -> (q u e r y la n g u a g e )

– S P A R Q L (te rm 1 ) -> S P A R Q L (te rm 2 )

• A d d fe d e r a te d q u e r y s u p p o r t t o S P A R Q L e n g in e im p le m e n ta t io n s

Join U s !






Introduction to the W3C for Semantic Web Health Care and ... · Drug Discovery Technology of...

Documents

Transcript of Introduction to the W3C for Semantic Web Health Care and ... · Drug Discovery Technology of...