Abstract. The variety of classification systems and the new discovery of taxonomists lead to the diversity of biological information, especially taxon concepts. The association among taxon concepts across research institutes is very difficult to establish, because there is no single interpretation of the name of a taxon concept. Owing to this difficulty, further integration of more biological knowledge is very complicated when they deal with many sources of data or depending on different taxon concepts. This research aimes to develop a framework for linking some multiple related taxon concepts across research repositories, and preserving background knowledge of their changes. Therefore, we propose a logical model for taxon concepts in Resource Description Framework (RDF). Herewith, we implement a prototype to demonstrate the feasibility of our approach. It has been found that our model can publish taxon information as linked data and, hence, with additional benefits from Linked Open Data (LOD) cloud.

1. Link Prediction in Linked Data of
Interspecies Interactions using
Hybrid Recommendation Approach
Hideaki TAKEDA
Professor
Chiang Mai, Thailand JIST 2014 November 10th, 2014
Tsuyoshi HOSOYA
Mycologist
Rathachai CHAWUTHAI
rathachai.c@gmail.com

2. Linked Open Data for ACadamiaLODAC
“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 speciesLPII
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. 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.
Dataset
6
Host
Fungus
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. List of
Fungus-Host
interaction with
predictive scores
DATA PREPARATION LPII APPROACH
RESULT
transform data using
a Weight Function
BIOLOGIST
Making Observation
Collaborative
Filtering
Finding
Missing
Links
Combine
Score Score Score
1 2
3
4
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
f3
f4
f5
h1
h2
h3
h4
h5
Fungi Hosts
11

12. f1
f2
h1
h2
P
CF
( 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
f4
f5
0.50
0.33
Bipartite GraphProjection of Fungi
f2
f1
f3
17

18. Community
Structure
of
Rust Fungi
18
Using Modularity
with Random Walk

19. f4
f5
0.50
0.33
Projection of Fungi
f2
f1
f3
CommunityStructureh1
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. 20
How to
deal with
many
very small
communities?

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)
 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
Classification Example
22

23. f1
f2
f3
f4
f5
h1
h2
h3
h4
h5
with Biological Classification
G1
G2
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. PCF( f,h )
PII( f,h )
Hybrid Recommender Approach
PCS( f,h )
PBC( f,h )
Collaborative
Filtering
Community
Structure
Biological
Classification
24
Combination of

25. Evaluation
25

26. 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
Learning and Testing
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)
Low AUCHigh 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. RDF data of
Interspecies
Interactions
Projection
of Fungi
Collaborative
Filtering
Community
Structure
Biological
Classification
SPARQL
querying
being input of
Scoring Functions
ranking
predictions
in decreasing
order
Predicted Missing Links
of Fungus-Host together with
prediction scores
DATA PREPARATION LPII APPROACH
RESULT
Bipartite Graph
Missing
Links
Community
Detection Method
transform data using
a Weight Function
DOMAIN
EXPERT
found?
yes
update
knowledgebase
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
linkssharing
LOD
Cloud
PII(f,h) +
PCF(f,h) PCS(f,h) PBC(f,h)
1 2
3
4
29
Overall

30. PCF( f,h )PII( f,h )
Hybrid Recommender Approach
PCS( f,h )
PBC( f,h )
α
β
γγshould be very
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. PCF( f,h )PII( f,h )
Future Work
PCS( f,h )
PBC( f,h )
α
β
γ
x1 (f,h)
x2 (f,h)
x3 (f,h)
32

33. RDF data of
Interspecies
Interactions
NFungi-Projection
or GProjFungi
Collaborative
Filtering
Community
Structure
Biological
Classification
SPARQL
querying
being input of
Scoring Functions
ranking
predictions
in decreasing
order
Predicted Missing Links
of Fungus-Host together with
prediction scores
DATA PREPARATION LPII APPROACH
RESULT
Bipartite Graph
GBipt
including
LExist
Missing
Links
Or
LMiss
clustering using
a Community
Detection Method
transform data using
a Weight Function
DOMAIN
EXPERT
found?
yes
update
knowledgebase
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
linkssharing
LOD
Cloud
PII(f,h) +
PCF(f,h) PCS(f,h) PBC(f,h)
1 2
3
4
Overall
α β γ
33

34. Any idea for improvement?