A Uni ied Model Rep esen a ion o Machine
Lea ning Knowledge
J. G. En ´
ıquez1,⇤, A. Ma ´
ınez-Rojas1, D. Lizcano2
and A. Jim´
enez-Ram´
ı ez1
1Compu e Languages and Sys ems Depa men . Escuela T´
ecnica Supe io de
Ingenie ´
ıa In o m´
a ica, A enida Reina Me cedes, s/n, 41012, Se illa. Spain
2Uni e sidad a dis ancia de Mad id. Ca e e a de La Co u˜
na, KM.38,500, ´
ıa de
Se icio, no 15, 28400, Collado Villalba, Mad id. Spain
E-mail: [email p o ec ed]
⇤Co esponding Au ho
Recei ed 20 Decembe 2019; Accep ed 14 Ap il 2020;
Publica ion 03 June 2020
Abs ac
Nowadays, Machine Lea ning (ML) algo i hms a e being widely applied in
i ually all possible scena ios. Howe e , de eloping a ML p ojec en ails he
e o o many ML expe s who ha e o selec and con igu e he app op ia e
algo i hm o p ocess he da a o lea n om, be ween o he hings. Since he e
exis housands o algo i hms, i becomes a ime-consuming and challenging
ask. To his end, ecen ly, Au oML eme ged o p o ide mechanisms o
au oma e pa s o his p ocess. Howe e , mos o he e o s ocus on applying
b u e o ce p ocedu es o y di e en algo i hms o con igu a ion and selec
he one which gi es be e esul s. To make a sma e and mo e e icien
selec ion, a eposi o y o knowledge is necessa y. To his end, his pape
p oposes (1) an app oach owa ds a common language o consolida e he
cu en dis ibu ed knowledge sou ces ela ed he algo i hm selec ion in ML,
and (2) a me hod o join he knowledge ga he ed h ough his language in
a uni ied s o e ha can be exploi ed la e on, and (3) a aceabili y links
main enance. The p elimina y e alua ions o his app oach allow o c ea e a
Jou nal o Web Enginee ing, Vol. 19 2, 319–340.
doi: 10.13052/jwe1540-9589.1929
© 2020 Ri e Publishe s
320 J. G. En ´
ıquez e al.
uni ied s o e collec ing he knowledge o 13 di e en sou ces and o iden i y
a bunch o esea ch lines o conduc .
Keywo ds: Machine Lea ning, Au oma ed Machine Lea ning, Knowledge
Rep esen a ion, Model-D i en Enginee ing.
1 In oduc ion
Machine Lea ning (ML) en ails he s udy o algo i hms ha au oma ically
imp o e h ough expe ience [18]. This kind o algo i hms has been suc-
cess ully and b oadly applied in he pas [19] and nowadays is ecei ing
inc easing a en ion due o he a o dable access o bigge compu a ion powe
o machines.
A ML p ojec equi es selec ing an app op ia e algo i hm o p ocess
he da a o lea n om, which is ypically named c ea ing he da a model.
Howe e , he e a e housands o algo i hms unde he pa adigm o ML, each
o hem ailo ed o some speci ic asks o con ex s. In addi ion, many o hese
algo i hms o e a di e en se o pa ame e s o be con igu ed (e.g., selec ing
he numbe o laye s in a neu al ne wo k).
Many exis ing app oaches ocus on he la e ask, i.e., suppo ing he
use a e he algo i hm selec ion is done, and ew o hem ecommend an
algo i hm always a e he use has p o ided he da ase . As an example, he
ecen esea ch a ea o Au oML [28] aims o au oma e he di e en s eps o
ML p ojec s. None heless, such app oaches neglec he ea ly s ages o he
p ojec . Many o hem jus p o ide a b u e o ce mechanism ha uns se e al
algo i hms in la e s ages o he p ojec , i.e., when he da ase is eady. Thus,
li le e o has been done o suppo he use in he algo i hm selec ion in an
e icien manne (i.e., wi hou applying b u e o ce) and based on he p oblem
cha ac e is ics (i.e., he ea ly in o ma ion).
The algo i hm selec ion is speci ically challenging since he exis ing
knowledge ega ding his ask is dis ibu ed ac oss di e en sou ces and each
o hem is speci ied in a non-s anda d manne , hus, making i di icul o con-
solida e in o ma ion om di e en sou ces, i.e., he name o he algo i hms
—o amily o algo i hms—, he selec ion c i e ia, and he cha ac e is ics o
he p oblem ha a ec he selec ion a e he e ogeneous (c . Figu e 1).
To educe he isk o aking inaccu a e decisions due o a lack o in o ma-
ion, a cen al eposi o y o he ML Knowledge which s o es he in o ma ion
in a s uc u ed way is equi ed. In o de o add ess his p oblem, his pape
p oposes (c . Figu e 2), on he one hand, a uni ied language o ep esen ing
