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Ekaw2014 - Inferring Semantic Relations by User Feedback

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Inferring Semantic Relations by User Feedback
by F. Osborne, E. Motta

URL: http://oro.open.ac.uk/41162/

In the last ten years, ontology-based recommender systems have been shown to be effective tools for predicting user preferences and suggesting items. There are however some issues associated with the ontologies adopted by these approaches, such as: 1) their crafting is not a cheap process, being time consuming and calling for specialist expertise; 2) they may not represent accurately the viewpoint of the targeted user community; 3) they tend to provide rather static models, which fail to keep track of evolving user perspectives. To address these issues, we propose Klink UM, an approach for extracting emergent semantics from user feedbacks, with the aim of tailoring the ontology to the users and improving the recommendations accuracy. Klink UM uses statistical and machine learning techniques for finding hierarchical and similarity relationships between keywords associated with rated items and can be used for: 1) building a conceptual taxonomy from scratch, 2) enriching and correcting an existing ontology, 3) providing a numerical estimate of the intensity of semantic relationships according to the users. The evaluation shows that Klink UM performs well with respect to handcrafted ontologies and can significantly increase the accuracy of suggestions in content-based recommender systems.

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Ekaw2014 - Inferring Semantic Relations by User Feedback

  1. 1. Inferring(Seman,c(Rela,ons(by(User( Feedback(( Francesco Osborne, Enrico Motta KMi, The Open University, United Kingdom November 2014
  2. 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. 3. Content=based/hybrid$ Recommender$Systems$ $ Feedback$ Recommenda<ons$ Background$ Knowledge$ Algorithm$
  4. 4. Content=based/hybrid$ Recommender$Systems$ $ Feedback$ Easier$to$share$and$reuse$system$knowledge$ Recommenda<ons$ Domain$ Ontology$ Algorithm$
  5. 5. e.g.,$spreading$ac<va<on$$
  6. 6. Two$categories$with$common$ But(soon(problems(arose…( problems…$ Actually$ontology$cra;ing$ is$a$long$process$and$we$ need$domain$experts.$ Ontology$Engineers$ Ontology$cra;ing$is$<me=consuming$and$ calling$for$specialist$exper<se$ Recommender$System$experts$ And$a;er$all$this$$work$your$ experts$do$not$even$agree$ with$our$users.$We$need$to$do$ it$again!$ ontology'engineer' recommender'system'expert'
  7. 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. 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. 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. 10. Content=based/hybrid$ Recommender$Systems$ $ Feedback$ Recommenda<ons$ Domain$ Ontology$ Algorithm$
  11. 11. Solu<on$ Feedback$ Recommenda<ons$ Tailored$Domain$ Ontology$ Algorithm$ Let’s$learn(and(enrich(the(ontology( (from(user(feedbacks$ $
  12. 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. 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. 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. 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. 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. 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. 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. 19. Ontology$learning$
  20. 20. Ontology$learning$
  21. 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. 22. Recommender$performance$
  23. 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.$$
  24. 24. Ques<ons?$ Interested(in(scholarly(data?( ( SAVE=SD$2015$ Seman<cs,$Analy<cs,$Visualisa<on:$Enhancing$Scholarly$Data$$ Workshop$at$24th$Interna<onal$World$Wide$Web$Conference$$ May$19,$2015$=$Florence,$Italy$$ $ Site:$cs.unibo.it/saveRsd(

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