1. Mapping Ontology classes to Wordnet synsets
Xiangqian Li

2. Content Objectives Process Evaluation Construct taxonomies Matching
Mapping selection
postprocessing
Evaluation

3. Previously on SView Portal
搜索语义tags, 关键词搜索实体 go
Selected Tags:
location X
city,metropolis X
Categories:
national capital
national capital
state capital
provincial capital
national capital
others
national capital
state capital
provincial capital

4. Objectives
A semantic mapping from an ontology class C to a noun synset S indicates the relateness between two heterogeneous concepts in human senses.
Map ontology classes to wordnet synsets
Ontology class to
Wordnet synset mappings
Tag entities
With semantic tags (synset)
Find entities via Navigation with human senses

5. Objectives ... ... Uniform taxonomy derived from Wordnet
noun synsets with the hyponymy
relations. We call it Synset Hyponymy
Graph(SHG).

6. Process construct taxonomy for the ontology and corresponding SHG.
matching with multiple strategies
Concept Cone Matcher
Linguistic Matcher
Similarity Flooding method based on SPG
select mappings via
homomorphism constraint
Naive selection
based on greedy strategy
Select suitable strategies
blocking
skeleton
selection
supplement
selection

7. Process / construct taxonomies
Ontology Classification:
Use HermiT to inference undercovered subsumption relations underlying in the ontology.
This process outputs a DAG, with node containing one class or several equivalent classes and edge representing subsumption relations.
Load a synset hyponymy graph that the nodes are in the whole SHG and is homomorphic to the whole SHG.

8. Process / matching ConceptCone matcher
Construct virtual document of each concept in ontologies and Wordnet using Concept Cone.
Caculate similarity using cosine value of two document vectors.
current
closer
to the
current
node
higher
coefficient
of term
concept cone

9. Process / matching Linguistic Matcher
Compute the maximum similarity values between multiple labels of a class taxonomy node and the words in a synset taxonomy node as the similarity values of the two nodes using ISub.
Combine the similarity of it to the similarity of the ConceptCone matcher using linear weighted sum.

10. Process / matching Similarity Flooding 1 in SPG:
Start a similarity propagation iteration till its convergence or predefined number of iterations reached.
The intuition is: If two entities are similar, the entities having hypernymy or hyponymy relations with it is may be also similar. C1
map1 S1
map1 C2 S2
map2
map2
1 Melnik, Sergey, Hector Garcia-Molina, and Erhard Rahm. "Similarity flooding: A versatile graph matching algorithm and its application to schema
matching." InProc. 18th ICDE Conf.(Best Student Paper award)

11. Process / Select mappings
Construct the SPG as the previous described steps.
Divide it to several weakly connected components.
For each component, evaluate its size. If it's too large, find the minimum cut edge set and remove these edges to partition this component to more smaller components.

12. Process / select mappings
First consider the component that covers the most nodes in the ontology taxonomy.
selection via
homomorphism
constraint
skeleton mappings
component
remove mappings that have the same
source class node with the skeletons
in all components or have conflicts with them

13. Process / select mappings
We require all ontology mappings have exactly one mapping to a wordnet synset.
After building the skeleton mappings(anchors), we use several rules to select high quality mappings for the nodes haven’t been mapped.

14. Evaluation
We conducted experiments on several popular ontologies, including schema.org, SUMO, DBpedia, UMBEL, GoodRelations, MusicOntology, Yago and several ontologies collected by Falcon in NJVR.
The amount of the mappings are huge. We sampled a part of them to ask human to evaluate the quality of the mappings.

15. Evaluation Example

16. Evaluation Results

17. Thank you.