Transfer Learning of Link Specifications

  • 52 views
Uploaded on

ICSC 2013 (http://ieee-icsc.org/icsc2013/) presentation on transfer learning of link specifications

ICSC 2013 (http://ieee-icsc.org/icsc2013/) presentation on transfer learning of link specifications

More in: Education , Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
52
On Slideshare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
2
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Towards Transfer Learning of Link Specications Axel-Cyrille Ngonga Ngomo Jens Lehmann Mofeed Hassan 2013-09-16 Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 1 / 29
  • 2. Outline 1 Motivation 2 Transfer Learning Framework 3 Experimental Setup 4 Results 5 Conclusions and Future Work Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 2 / 29
  • 3. Outline 1 Motivation 2 Transfer Learning Framework 3 Experimental Setup 4 Results 5 Conclusions and Future Work Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 3 / 29
  • 4. Why Link Discovery? 1 2 3 Fourth Linked Data principle Links are central for Cross-ontology QA Data Integration Reasoning Federated Queries ... 2011 topology of the LOD Cloud: 31+ billion triples ≈ 0.5 billion links owl:sameAs in most cases Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 4 / 29
  • 5. Why is it dicult? Denition (Link Discovery) S and T of resources and relation R Find M = {(s , t ) ∈ S × T : R(s , t )} Given sets Task: Common approaches: Find = {( , ) ∈ = {( , ) ∈ Find M M s t S × T : σ(s , t ) ≥ θ} s t S × T : δ(s , t ) ≤ θ} Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 5 / 29
  • 6. Why is it dicult? Denition (Link Discovery) S and T of resources and relation R Find M = {(s , t ) ∈ S × T : R(s , t )} Given sets Task: Common approaches: Find = {( , ) ∈ = {( , ) ∈ Find M M 1 s t S × T : σ(s , t ) ≥ θ} s t S × T : δ(s , t ) ≤ θ} Time complexity Large number of triples Quadratic a-priori runtime 69 days for mapping cities from DBpedia to Geonames (1ms per comparison) Decades for linking DBpedia and LGD ... Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 5 / 29
  • 7. Why is it dicult? 2 Complexity of specications Combination of several attributes required for high precision Tedious discovery of most adequate mapping Dataset-dependent similarity functions Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 6 / 29
  • 8. LIMES Framework Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 7 / 29
  • 9. Link Specication Detection of accurate link specication is key Link Specications has three components: Two sets of restrictions RS ... RS resp. RT ... RT that specify the m 1 1 k sets resp. , A specication of a complex similarity metric σ via the combination of several atomic similarity measures σ1 , ..., σn and A set of thresholds τ1 , ..., τn such that τi is the threshold for σi . S T Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 8 / 29
  • 10. Transfer Learning Classical Learning of Link Specs Transfer Learning of Link Specs Current Linking Task Different Linking Tasks Task Repository spec accuracy: α class similarity: ζ property similarity: π Learning System Learning System Learning System In our approach we use Transfer Learning System Transductive Transfer Learning Class and property matching is assumed to be known already (numerous approaches from ontology matching can be employed) the goal is to nd the complex similarity metric Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 9 / 29
  • 11. Outline 1 Motivation 2 Transfer Learning Framework 3 Experimental Setup 4 Results 5 Conclusions and Future Work Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 10 / 29
  • 12. Transfer Learning Framework I Transfer Learning of link specications is reduce to three subproblems: Restrictions/class similarity ζ : 2C × 2C → [0, 1] e.g. ζ({City , Village }, {Town}) = 0.6 Property similarity: ξ : 2P × 2P → [0, 1] e.g. ξ({rdfs : label }, {rdfs : label }) = 1.0 Accuracy of link specications: α : Q → [0, 1] Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 11 / 29
  • 13. Transfer Learning Framework II Overall similarity measure for transfer learning: ω(t , t ) = α(q ) · ζ(ψ(q ), C) · ζ(ψ (q ), C ) · ξ(sp (q ), PL ) · ξ(tp (q ), PL ) (details in paper) Each similarity measure can be implemented in manifold approaches Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 12 / 29
  • 14. Transfer Learning Framework II Overall similarity measure for transfer learning: ω(t , t ) = α(q ) · ζ(ψ(q ), C) · ζ(ψ (q ), C ) · ξ(sp (q ), PL ) · ξ(tp (q ), PL ) (details in paper) Each similarity measure can be implemented in manifold approaches Implementations of class similarity function ζ in framework: label-based similarity name-based similarity (URI similarity) data-centric similarity Properties similarities ξ are dened analogously Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 12 / 29
  • 15. Transfer Learning Framework II Overall similarity measure for transfer learning: ω(t , t ) = α(q ) · ζ(ψ(q ), C) · ζ(ψ (q ), C ) · ξ(sp (q ), PL ) · ξ(tp (q ), PL ) (details in paper) Each similarity measure can be implemented in manifold approaches Implementations of class similarity function ζ in framework: label-based similarity name-based similarity (URI similarity) data-centric similarity Properties similarities ξ are dened analogously Similarities between single classes/properties can be extended to sets (e.g. using arithmetic / geometric mean of max. similarity) Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 12 / 29
  • 16. Transfer Learning Framework II Overall similarity measure for transfer learning: ω(t , t ) = α(q ) · ζ(ψ(q ), C) · ζ(ψ (q ), C ) · ξ(sp (q ), PL ) · ξ(tp (q ), PL ) (details in paper) Each similarity measure can be implemented in manifold approaches Implementations of class similarity function ζ in framework: label-based similarity name-based similarity (URI similarity) data-centric similarity Properties similarities ξ are dened analogously Similarities between single classes/properties can be extended to sets (e.g. using arithmetic / geometric mean of max. similarity) Spec can be transferred by replacing properties with most similar properties in PL and PL Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 12 / 29
  • 17. Example (New Link Task) Example link specication for mapping drugs in two datasets DBpedia and Drugbank (DBpedia-Drugbank.xml): Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 13 / 29
  • 18. Example (Restriction part) Three parts of link specs: Restrictions part Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 14 / 29
  • 19. Example (Properties Part) Three parts of link specs: Restrictions part Properties part Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 15 / 29
  • 20. Example (Similarities Measures Part) Three parts of link specs: Restrictions part Properties part Similarity Measures part: similarity metric and thresholds Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 16 / 29
  • 21. Example (Link Repository) Transfer learning is applied using a repository → restrictions and relevant properties are assumed to be known → nd the similarity measure by comparing with all specs in the repository, e.g. DBpedia-SiderDrugs.xml Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 17 / 29
  • 22. Example (Restriction Similarities) Restrictions in both specications les Type DBpedia-Drugbank.xml DBpedia-SiderDrugs.xml Source Target rdf:type dbpedia-owl:Drug rdf:type drug:drugs rdf:type dbpedia-owl:Drug rdf:type sider:drugs Straightforward label/URI similarity For instance, trigram metric in URI similarity without prexes: ζ({dbpedia-owl:Drug}, {dbpedia-owl:Drug}) = 1.0 ζ({sider:drugs}, {drug:drugs}) = 1.0 Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 18 / 29
  • 23. Example (Restriction Similarities) Restrictions in both specications les Type DBpedia-Drugbank.xml DBpedia-SiderDrugs.xml Source Target rdf:type dbpedia-owl:Drug rdf:type drug:drugs rdf:type dbpedia-owl:Drug rdf:type sider:drugs Straightforward label/URI similarity For instance, trigram metric in URI similarity without prexes: ζ({dbpedia-owl:Drug}, {dbpedia-owl:Drug}) = 1.0 ζ({sider:drugs}, {drug:drugs}) = 1.0 1 Data-centric: ζd (s , s ) = |P (s )||P (s sim(x , y ) where x ∈P (s ) y ∈P (s ) P (s ) = {x : s p x ∧ p rdf:type owl:DatatypeProperty} (extends similarity to instances) Ngonga et. al (Univ. Leipzig) )| Transfer Learning of Link Specs 2013-09-16 18 / 29
  • 24. Example (Property Similarities) type DBpedia-Drugbank.xml DBpedia-SiderDrugs.xml Source rdfs:label Target rdfs:label drug:genericName rdfs:label foaf:name rdfs:label Applying similarity function to all properties: For instance trigram based on URIs and arithmetic mean as aggregation: ξ({rdfs : label }, {rdfs : label , foaf : name }) = 0.9 ξ({rdfs : label , drug : genericName }, {rdfs : label }) = 0.8 Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 19 / 29
  • 25. Example (Overall Similarity) Based on, e.g. F-score assign quality value to q = DBpedia-SiderDrugs.xml, in our case α(q ) = 0.89 The nal step is calculating the overall similarity measure ω(DBpedia − Drugbank .xml , DBpedia − SiderDrugs .xml ) = 0.89 * 1.0 * 1.0 * 0.9 * 0.8 = 0.64 The steps are repeated for all link specications in the repository Most similar link spec can be transferred by replacing its properties with the most similar ones in the computed property matching Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 20 / 29
  • 26. Outline 1 Motivation 2 Transfer Learning Framework 3 Experimental Setup 4 Results 5 Conclusions and Future Work Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 21 / 29
  • 27. Experimental Setup I The goal of evaluation is two-fold: Evaluating whether transfer learning can be used to build templates for link spec Discover whether the transferred templates can be used directly 113 specications were retrieved from LATC, each has manual links evaluation 15% 10% 3% 2% Persons 1% 3% Events Locations Diseases Drugs Organizations Misc 66% Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 22 / 29
  • 28. Experimental Setup II Leave-one-out evaluation 1.) Compare top-scored specication (most similar) and check whether it uses the same combination of similarity functions  assign 1 for match and 0 for no match 2.) Compute F-measure of learned link specs directly  works only on specs with both endpoints alive (only 12 out of 113) Used URI similarity Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 23 / 29
  • 29. Outline 1 Motivation 2 Transfer Learning Framework 3 Experimental Setup 4 Results 5 Conclusions and Future Work Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 24 / 29
  • 30. First Experiments Set Results Detecting right specication in 81% of all cases In geo-spatial domain 91% In persons domain 58% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Ave s s s s e rag erson Event cation sease P Di Lo Ngonga et. al (Univ. Leipzig) gs ns Dru izatio n rga O Transfer Learning of Link Specs Mis c 2013-09-16 25 / 29
  • 31. Second Experiments Set Results In the second Experiments series, source and target endpoints need to be alive such that we can execute transferred link spec (12 out of 113) In general low F-measures 100% 80% 60% 40% Precision Recall F-Measure 20% s nt ry op e ne i-c ou ty si ex t er at at iv gu a- pe di a- te nd un a- at pe di db db n on so rs er pe -p d- on oo og f -d db pe di a- lin ke dg eo d nt or t rp ai at a- m -r st ad ev en ts ee r so n -d er db pe di ali nk ts e en ev ve er -e -p fo od og ge od l3 s p_ bl er ed s ty ci ar ie K- ul vU ab go st da ta di a- se nt ev e db pe di pe db e- im Ngonga et. al (Univ. Leipzig) -d co u aco n es - -e ss xe ra eu bc r rk db lp -d at as em an tic w eb - ur re se ar ch er nt ry 0% Transfer Learning of Link Specs 2013-09-16 26 / 29
  • 32. Outline 1 Motivation 2 Transfer Learning Framework 3 Experimental Setup 4 Results 5 Conclusions and Future Work Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 27 / 29
  • 33. Summary Conclusions: Detecting right template in 81% of all cases Transfer learning cannot replace the learning of thresholds in specications Future Work: Combination with machine-learning approaches for link specications (e.g., EAGLE, COALA), in particular for learning thresholds More sophisticated class and property similarity approaches Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 28 / 29
  • 34. The End Jens Lehmann lehmann@informatik.uni-leipzig.de AKSW/Uni Leipzig Questions Geo Know http://geoknow.eu Ngonga et. al (Univ. Leipzig) Transfer Learning of Link Specs 2013-09-16 29 / 29