Francesco Osborne, Enrico Motta

KMi, The Open University, United Kingdom

November 2014

Inferring(Seman,c(Rela,ons(by(User(Feedback((

Two$categories$with$common$problems…$

Ontology$Engineers$$$Recommender$System$experts$

Ontology$cra;ing$is$<me=consuming$and$calling$for$specialist$exper<se$

ontology'engineer' recommender'system'expert'

Ontologies$are$the$best.$ Wow,$let$me$play$with$them!$

Once(upon(a(,me…(

Content=based/hybrid$Recommender$Systems$

$

Feedback$

Recommenda<ons$

Background$Knowledge$

Algorithm$

Content=based/hybrid$Recommender$Systems$

$

Feedback$

Recommenda<ons$

Domain$Ontology$

Algorithm$

Easier$to$share$and$reuse$system$knowledge$

e.g.,$spreading$ac<va<on$$

Two$categories$with$common$problems…$

Ontology$Engineers$$$Recommender$System$experts$

Ontology$cra;ing$is$<me=consuming$and$calling$for$specialist$exper<se$

ontology'engineer' recommender'system'expert'

Actually$ontology$cra;ing$is$a$long$process$and$we$need$domain$experts.$

And$a;er$all$this$$work$your$experts$do$not$even$agree$with$our$users.$We$need$to$do$it$again!$

But(soon(problems(arose…(

Two$categories$with$common$problems…$

Ontology$Engineers$$$Recommender$System$experts$

Ontology$cra;ing$is$<me=consuming$and$calling$for$specialist$exper<se$

ontology'engineer' recommender'system'expert'

Well$we$can$run$different$tests$and$use$the$version$that$maximize$accuracy….$

Also$we$just$added$400$new$items,$make$sure$they$are$described$in$the$ontology$by$tomorrow.$

But(soon(problems(arose…(

Two$categories$with$common$problems…$

Ontology$Engineers$$$Recommender$System$experts$

Ontology$cra;ing$is$<me=consuming$and$calling$for$specialist$exper<se$

ontology'engineer' recommender'system'expert'

I$quit!$ Ontologies$do$not$understand$us!$

But(soon(problems(arose…(

Problems$

1.  Ontology$cra;ing$is$<me=consuming$and$calling$for$specialist$exper<se;$

2.  A$domain$ontology$may$not$represent$accurately$the$viewpoint$of$the$targeted$user$community;$

3.  Ontologies$tend$to$provide$rather$sta<c$models,$which$fail$to$keep$track$of$evolving$user$perspec<ves.$$

Content=based/hybrid$Recommender$Systems$

$

Feedback$

Recommenda<ons$

Domain$Ontology$

Algorithm$

Solu<on$

Feedback$

Recommenda<ons$

Tailored$Domain$Ontology$

Algorithm$

Let’s$learn(and(enrich(the(ontology((from(user(feedbacks$

$

Advantages$•  It$makes$the$crea<on/enrichment$of$ontologies$quicker(and(easier;$

•  It$provides$a$useful(feedback(to$ontology$engineers;$

•  The$ontologies$learnt/corrected$by$user$feedback$are$Personal$Ontology$Views,$tailored(on(a(specific(community;(

•  The$ontology$will$be$able$to$evolve,$keeping$track$of$shi;ing$user$perspec<ves.$

$

Klink$

Klink$is$an$algorithm$designed$to$mine(seman,c(rela,onships(between(keywords.$It$was$developed$for$Rexplore,$a$tool$for$exploring$scholarly$data$and$can$be$used$to$augment$seman<cally$a$variety$of$data$mining$algorithm.$

(

A(Hybrid(Seman,c(Approach(to(Building(Dynamic(Maps(of(Research(Communi,es(

Francesco'Osborne,'Giuseppe'Scavo'and'Enrico'Mo<a'11:40,$Session$3,$EKAW$2014$

$

Klink$UM$It$uses$sta<s<cal$and$machine$learning$techniques$for$finding$rela,onships(between(keywords(associated(with(rated(items$for:$

1.  building$a$conceptual$taxonomy$from$scratch;$

2.  enriching$and$correc<ng$an$exis<ng$ontology$a)  automa<cally,$

b)  sugges<ng$poten<al$connec<ons$between$classes$to$be$addressed$by$ontology$engineers;$

3.  providing$a$numerical$es<mate$of$the$intensity$of$the$seman<c$rela<onships$according$to$a$group$of$users.$

From$Ra<ngs$to$Condi<onal$Probability$

•  Klink$infers$subsump<on$rela<onships$by$compu<ng$the$condi<onal$probability$that$a$document$tagged$with$term$x$will$be$also$associated$with$term$y.$

•  Klink$UM$computes$the$condi<onal$probability$that$a$user$who$has$a$posi,ve(or(nega,ve(opinion(of$x$will$have$the$same$opinion$of$y.$

$Classic(subsump,on:(P(x|y)$≥$α$and$P(y|x)$<'1''Klink(improved(subsump,on:((.(((((

xy$

K-Link UM

STATISTICAL INFERENCES

FILTER KEYWORD CLUSTERING

Hierarchical clustering

User Ratings ontags/keywords

Pre-existentontology

TAXONOMY GENERATION

Candidate ontology

PROPOSED MODIFICATIONS

EVALUATION

Final ontology

Parameters estimation withNelder-Mead algorithm

RS ALGORITHM

Recommendations

Evalua<on$

We$aimed$to$prove$that:$

•  Klink$UM$can$generate(conceptual(taxonomies(similar$enough$to$the$ones$cra;ed$by$human$experts$(e.g.,$Klink$UM$is$useful$for$OE)$

•  the$ontologies$generated$or$enriched$by$Klink$UM$are$tailored$to$a$par<cular$group$of$users,$and$useful(for(recommenda,on(purposes.$(e.g.,$Klink$UM$is$useful$for$RS)$

Ontology$learning$

Tested$on$two$ontologies$in$the$gastronomic$domain:$1) Cold'Cuts,$a$three$level$ontology$with$19$

classes,$describing$different$cuts$of$meat;$2) Drinks,$a$three$level$ontology$with$33$classes,$

describing$different$drinks.$

Ontology$learning$

Ontology$learning$

Recommender$performance$We$used$spreading(ac,va,on((as$in$Cena$et$al.$2013)$to$generate$sugges<ons.$$

We$compared$the$accuracy$of$three$approaches:$•  Spreading$ac<va<on$on$an$expert(craMed(ontology((labelled$S)$

•  Spreading$ac<va<on$on$an$expert$cra;ed$ontology,$corrected(and(enriched(by$accep<ng$by$default$Klink$UM$sugges<ons$(labelled$SE)$$

•  Spreading$ac<va<on$on$a$conceptual$taxonomy$generated(from(scratch(by$Klink$UM$(labelled$SG)$$

Recommender$performance$

Future$Work$

•  Cluster$groups$of$people$with$different$views$of$the$domain$in$order$to$build$tailored$versions$of$the$domain$ontology.$

•  Novel$heuris<cs$for$detec<ng$a$higher$number$of$seman<c$rela<onships.$$

Ques<ons?$

Interested(in(scholarly(data?((

SAVE=SD$2015$Seman<cs,$Analy<cs,$Visualisa<on:$Enhancing$Scholarly$Data$$Workshop$at$24th$Interna<onal$World$Wide$Web$Conference$$

May$19,$2015$=$Florence,$Italy$$$

Site:$cs.unibo.it/saveRsd(