Link Prediction in Linked Data of Interspecies Interactions using Hybrid Recommendation Approach
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Linked Open Data for ACademia (LODAC) together with National Museum of Nature and Science have started collecting linked data of interspecies interaction and making link prediction for future observations. The initial data is very sparse and disconnected, making it very difficult to predict potential missing links using collaborative filtering alone. In this paper, we introduce Link Prediction on Interspecies Interaction (LPII) to solve this situation using hybrid recommendation approach. Our prediction model is a combination of three scoring functions, and takes into account collaborative filtering, community structure, and biological classification. We have found our approach, LPII, to be more accurate than other combinations of perdition models. Using statistical significance testing, we demonstrate that these scoring functions are important and play different roles depending on the conditions of linked data. This shows that LPII can be applied to deal with other real-world situations of link prediction.
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Link Prediction in Linked Data of Interspecies Interactions using Hybrid Recommendation Approach
- 1. Link Prediction in Linked Data of Interspecies Interactions using Hybrid Recommendation Approach Hideaki TAKEDA Professor Tsuyoshi HOSOYA Mycologist Rathachai CHAWUTHAI rathachai.c@gmail.com Chiang Mai, Thailand JIST 2014 November 10th, 2014
- 2. LODAC Linked Open Data for ACadamia “Salix pierotii” lodac:Salix species: hasSuperTaxon lodac: Salix_ pierotii
- 3. National Museum of Nature and Science 30,000 Interactions 4,000 Fungi 7,000 Hosts
- 4. Let’s find the Missing Links between species LPII Link Prediction on Interspecies Interactions Objective: To predict missing links between fungi and hosts
- 5. Agenda • Dataset • Introduction • Hybrid Recommendation • Collaborative Filtering • Community Structure • Biological Classification • Evaluation • Summary • Future work
- 6. Dataset lodac:Melampsora_yezoensis rdfs:label “Melampsora yezoensis”@la ; species:hasTaxonRank species:Species ; species:hasSuperTaxon lodac:Melampsora . lodac:Melampsora species:hasTaxonRank species:Genus. lodac:Salix_pierotii rdfs:label “Salix pierotii”@la ; rdf:type species:ScientificName ; species:hasSuperTaxon lodac:Salix . lodac:Salix species:hasTaxonRank species:Genus. lodac:Melampsora_yezoensis species:growsOn lodac:Salix_pierotii. 6 Fungus Host Link
- 7. lodac: Melampsora lodac: Salix species: hasSuperTaxon species: hasSuperTaxon species: growsOn lodac: Melampsora_ yezoensis lodac: Salix_ pierotii 7
- 8. 903 Rust Fungi 2,001 Hosts 2,966 Links Biological Classification of Fungi Biological Classification of Hosts Selected 8
- 9. DATA PREPARATION LPII APPROACH List of Fungus-Host interaction with predictive scores RESULT transform data using a Weight Function BIOLOGIST Making Observation Finding Missing Links Collaborative Filtering Score Score Score Combine 1 2 4 3 Introduction 9 Community Structure Biological Classification Fungus-Host Interaction Dataset Generate Result
- 10. Collaborative Filtering Some fungi found at the same host are common neighbors. If some close neighbors of the fungus f are found at a host h, the fungus f may be found at the host h. 10 1
- 11. f1 f2 Fungi Hosts f3 f4 f5 h1 h2 h3 h4 h5 11
- 12. f1 f2 h1 h2 PCF ( f1,h2 ) = ? Collaborative Filtering for Link Prediction Sum of similarities between fungi with common hosts 12
- 13. f1 f2 f3 f4 f5 h1 h2 h3 h4 h5 w = ? Jaccard Index 13
- 14. f1 f2 f3 f4 f5 h1 h2 h3 h4 h5 w = 0.50 w = 0.33 14
- 15. Predictive Score using Collaborative Filtering PCF( f1,h2 ) = 0.50 PCF( f2,h3 ) = 0.33 PCF( f1,h3 ) = ??? PCF( f4,h3 ) = ??? f1 f2 f3 f4 f5 h1 h2 h3 h4 h5 w = 0.50 w = 0.33 PCF( f4,h5 ) = ??? etc. 15 ( Dash red lines are predicted links)
- 16. Community Structure If a host h is commonly found in the community of the fungus f, the fungus f may be found at the host h. 16 2
- 17. f1 f2 f3 f4 f5 h1 h2 h3 h4 h5 0.50 0.33 f2 f4 f1 f5 0.50 0.33 f3 Projection of Fungi Bipartite Graph 17
- 18. Community Structure of Rust Fungi Using Modularity with Random Walk 18
- 19. f2 f4 f1 f5 0.50 0.33 f3 Projection of Fungi CommunityStructure h1 h2 h3 h4 h5 Community #1 Community #2 Community #3 PCS( f,h ) = Number of links between the community of the fungus f and the host h Number of all links given by the community of the fungus f PCS( f3,h1 ) = 2 5 = 0.40 19
- 20. How to deal wi th many very smal l communi t ies? 20
- 21. Biological Classification If a host h is commonly found in the biological classification of the fungus f, the fungus f may be found at the host h. 21 3
- 22. BIOLOGICAL CLASSIFICATION (TAXONOMY) Classification Example Domain e.g. Eukaryota Kingdom e.g. Fungi Phylum e.g. Basidiomycota Class e.g. Urediniomycetes Order e.g. Uredinales Family e.g. Melampsoraceae Genus e.g. Melampsora Species e.g. Melampsora Yezoensis 22
- 23. f1 f2 f3 f4 f5 h1 h2 h3 h4 h5 G1 G2 with Biological Classification Biological Classification 23 PBC( f,h ) = Number of links between the biological classification of the fungus f and the host h Number of all links given by the biological classification of the fungus f PBC( f4,h2 ) = 1 4 = 0.25
- 24. Hybrid Recommender Approach PCF( f,h ) PII( f,h ) PCS( f,h ) PBC( f,h ) Collaborative Filtering Community Structure Biological Classification 24 Combination of
- 25. Evaluation 25
- 26. Learning and Testing Training set (2,500 links) Test set (500 links) Candidates (400,000 links) f1 f2 f3 f4 f5 h1 h2 h3 h4 h5 f1 f2 f3 f4 f5 h1 h2 h3 h4 h5 f1 f2 f3 f4 f5 h1 h2 h3 h4 h5 All Possible Links Existent Links Missing Links 0.421 0.864 0.466 0.490 0.366 0.515 0.313 0.076 0.362 0.902 0.069 0.524 0.876 0.464 0.839 0.504 26
- 27. AUC Area Under the receiver operating characteristic Curve ① PII( f1,h2 ) = 0.70 ② PII( f2,h3 ) = 0.60 ③ PII( f1,h3 ) = 0.50 ④ PII( f4,h3 ) = 0.40 ⑤ PII( f2,h2 ) = 0.30 ⑥ PII( f3,h3 ) = 0.20 ⑦ PII( f4,h3 ) = 0.10 ① PII( f1,h2 ) = 0.70 ② PII( f2,h2 ) = 0.60 ③ PII( f3,h3 ) = 0.50 ④ PII( f2,h3 ) = 0.50 ⑤ PII( f1,h3 ) = 0.40 ⑥ PII( f4,h3 ) = 0.30 ⑦ PII( f4,h3 ) = 0.10 Predicted List #1 (sorted by predictive score) High AUC Low AUC For n comparisons, • n' is number of times when the test links have higher score than the missing links. • n" is number of times when the test links have same score as the missing links. Predicted List #2 (sorted by predictive score) 27 ( Red scores are test links)
- 28. AUC Area Under the receiver operating characteristic Curve Combination Scoring Function(s) AUC Stand-alone function PCF 0.859 PCS 0.823 PBC 0.680 Summation of functions PCF + PCS 0.867 PCF + PBC 0.876 PCS + PBC 0.865 PCF + PCS + PBC 0.892 Multiplication of functions PCF × PCS 0.817 PCF × PBC 0.862 PCS × PBC 0.827 PCF × PCS × PBC 0.818 28
- 29. DATA PREPARATION LPII APPROACH LOD Cloud RDF data of Interspecies Interactions Projection of Fungi Collaborative Filtering transform data using a Weight Function Community Structure Biological Classification SPARQL querying being input of Scoring Functions ranking predictions in decreasing order Bipartite Graph update knowledgebase Predicted Missing Links of Fungus-Host together with prediction scores RESULT Missing Links Community Detection Method DOMAIN EXPERT found? yes NOTE select connected fungi clustering using Biological Classification make observation Data Process Third party method Scoring Function Input argument Linear Operation Decision Dataflow + find missing sharing links PII PCF (f,h) + (f,h) PCS (f,h) PBC (f,h) 1 2 4 3 29 Overall
- 30. Hybrid Recommender Approach PII( f,h ) PCF( f,h ) PCS( f,h ) PBC( f,h ) α β γ should be very γ low as about 0.1 and 0.2. 30
- 31. Conclusion Informatics Biology • RDF Model for Interspecies Interaction • Improve the use of Collaborative filtering with sparse dataset using • Community Structure • and Biological Classification • It has been found that • In general case, PCF + PCS is enough. • But when a node • having a few common neighbors • and locating in a small community, • PBC becomes a key player for making link prediction. • This model supports the view that most fungi under the same genus have similar parasite behavior. • Some predicted links having high predictive score, such as, • Phragmidium mucronatum ハマナス • Phragmidium fusiforme ハマナス • Phragmidium potentillae イワキンバイ have been discovered from other literatures. • Next enhancement is to analyze fungal species into fungal spore types. 31
- 32. Future Work PII( f,h ) PCF( f,h ) PCS( f,h ) PBC( f,h ) α β γ x1 (f,h) x2 (f,h) x3 (f,h) 32
- 33. DATA PREPARATION LPII APPROACH LOD Cloud RDF data of Interspecies Interactions transform data using a Weight Function NFungi-Projection or GProjFungi Collaborative Filtering Community Structure Biological Classification SPARQL querying being input of Scoring Functions ranking predictions in decreasing order Bipartite Graph GBipt including LExist update knowledgebase Predicted Missing Links of Fungus-Host together with prediction scores RESULT Missing Links Or LMiss clustering using a Community Detection Method DOMAIN EXPERT found? yes NOTE select connected fungi clustering using Biological Classification make observation Data Process Third party method Scoring Function Input argument Linear Operation Decision Dataflow + find missing sharing links PII PCF (f,h) + (f,h) PCS (f,h) PBC (f,h) 1 2 4 3 Overall α β γ 33
- 34. Any idea for improvement?