1.
Inferring(Seman,c(Rela,ons(by(User(
Feedback((
Francesco Osborne, Enrico Motta
KMi, The Open University, United Kingdom
November 2014

2.
Two$categories$with$common$
Once(upon(a(,me…(
problems…$
Ontology$Engineers$
Ontology$cra;ing$is$<me=consuming$and$
calling$for$specialist$exper<se$
Ontologies$are$the$best.$ Wow,$let$me$play$with$them!$
Recommender$System$experts$
ontology'engineer' recommender'system'expert'

3.
Content=based/hybrid$
Recommender$Systems$
$
Feedback$
Recommenda<ons$
Background$
Knowledge$
Algorithm$

4.
Content=based/hybrid$
Recommender$Systems$
$
Feedback$
Easier$to$share$and$reuse$system$knowledge$
Recommenda<ons$
Domain$
Ontology$
Algorithm$

5.
e.g.,$spreading$ac<va<on$$

6.
Two$categories$with$common$
But(soon(problems(arose…(
problems…$
Actually$ontology$cra;ing$
is$a$long$process$and$we$
need$domain$experts.$
Ontology$Engineers$
Ontology$cra;ing$is$<me=consuming$and$
calling$for$specialist$exper<se$
Recommender$System$experts$
And$a;er$all$this$$work$your$
experts$do$not$even$agree$
with$our$users.$We$need$to$do$
it$again!$
ontology'engineer' recommender'system'expert'

7.
Two$categories$with$common$
But(soon(problems(arose…(
problems…$
Well$we$can$run$different$
tests$and$use$the$version$
that$maximize$accuracy….$
Ontology$Engineers$
Ontology$cra;ing$is$<me=consuming$and$
calling$for$specialist$exper<se$
Recommender$System$experts$
Also$we$just$added$400$new$
items,$make$sure$they$are$
described$in$the$ontology$by$
tomorrow.$
ontology'engineer' recommender'system'expert'

8.
Two$categories$with$common$
But(soon(problems(arose…(
problems…$
Ontology$Engineers$
Ontology$cra;ing$is$<me=consuming$and$
calling$for$specialist$exper<se$
I$quit!$ Ontologies$do$not$understand$us!$
Recommender$System$experts$
ontology'engineer' recommender'system'expert'

9.
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.$$

10.
Content=based/hybrid$
Recommender$Systems$
$
Feedback$
Recommenda<ons$
Domain$
Ontology$
Algorithm$

11.
Solu<on$
Feedback$
Recommenda<ons$
Tailored$Domain$
Ontology$
Algorithm$
Let’s$learn(and(enrich(the(ontology(
(from(user(feedbacks$
$

12.
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$perspec<ves.$
$

13.
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$2014$
$

14.
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.$

15.
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$y.$
$
x
Classic(subsump,on:(
P(x|y)$≥$α$and$P(y|x)$<'1'
y$
'
Klink(improved(subsump,on:(
(
.(
(
(

16.
K-Link
UM
STATISTICAL
INFERENCES
FILTER KEYWORD
CLUSTERING
Hierarchical clustering
User Ratings on
tags/keywords
Pre-existent
ontology
TAXONOMY
GENERATION
Candidate ontology
PROPOSED
MODIFICATIONS
Parameters estimation with
Nelder-Mead algorithm
EVALUATION
Final ontology
Recommendations
RS ALGORITHM

17.
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)$

18.
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.$

19.
Ontology$learning$

20.
Ontology$learning$

21.
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)$$

22.
Recommender$performance$

23.
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.$$

24.
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(