This document proposes a relationship-based framework for retrieving top-k concepts from ontologies in response to a keyword query. The framework uses a dual walk ranking (DWRank) model that considers both the semantic similarity and centrality/authoritativeness of concepts. It retrieves concepts with the highest DWRank scores and further filters results by intended type to improve precision. An evaluation on sample queries demonstrates the effectiveness of the DWRank model and additional gains from the intended type filter.

1. Relationship-based Top-K
Concept Retrieval for Ontology
Search
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TThhee AAuussttrraalliiaann NNaattiioonnaall UUnniivveerrssiittyy
ffiirrssttnnaammee..llaassttnnaammee@@aannuu..eedduu..aauu

2. 2
Motivation – Ontology Search
“An ontology is a formal, explicit specification of a
shared conceptualization.” [Gruber 1992]
A central ingredient for effective
ontology re-use is the discovery of the
“right” ontology or ontological term for a
use case

3. Motivation – Ontology Search
• Ontology Search
– Matching a search term with a more
expressive class description
• Matching terms are defined with
differing
– Perspectives
– Levels of detail
– Reuse and Extensions
3

4. How to rank the similar concepts
with different levels of modelling
detail?
4

5. Relationship-based Top-k
Concept Retrieval
• The framework retrieves top-k concepts for
keyword query
 DWRank - Ranking Model
 Top-k Filter
5

6. DWRank – Dual Walk Ranking
Model
6

7. DWRank – Dual Walk Ranking Model
For a simple keyword query:
 Rank of a concept
 Semantic Similarity: Text relevancy of the concept
 Coverage : Centrality of the concept
 Reuse : Authoritativeness of the Ontology

8. DWRank – Dual Walk Ranking Model
1. Query independent scores for each concept of
ontologies based on their importance
 Centrality of the concept - HubScore
 Authoritativeness of the Ontology - AuthScore
1. Relevance score of a concept to a query:
• DWRank Function: Linear model combines
– Text relevancy of the concept description to a query
– HubScore and AuthScore
8

9. HubScore – Centrality of a Concept
Connectivity :
Relations starting from the
concept
Neighbourhood :
Relations starting from the
concept to another central
concept
@prefix a: http://example.org/def/people#
9
a:Person
a:Organization
a:Project
0.14
0.46
0.26 0.38
0.14 0.14
0.14
0.14
Reverse PageRank

10. AuthScore – Authoritativeness of an
Ontology
Reuse : Relations ending at the ontology
Neighbourhood : Relations starting from another authoritative
ontology to the ontology
10
PageRank
:Location :Restaurant :People
0.10 0.145 0.471

11. = +
R (v, O) F v (v,Q)* [w 1 * h(v,O) w 2 *
a(O)]
F (v,Q) f (q,φ(q ))
Zubeida Zubeida
v ss v
Zubei
da
Zubei
da
11
DWRank Function
• The Ranking model is function of:
• Concept Text Relevancy
• HubScore
• AuthScore
åÎ
=
q Q

12. 12
DWRank Score
Query: Person
o Fv(v,Q) = 1
o h(v,O) = 0.46
o a(O) = 0.471
R(v, O) = Fv(v,Q)* [w1 * h(v,O) + w2 * a(O)]
@prefix a: http://example.org/def/people#
a:Person
a:Project
0.14
0.46
0.26 0.38
0.14 0.14
0.14
0.14
0.471
= +
1* [0.5* 0.46 0.5* 0.471]
=
0.466

13. 13
DWRank vs. Linked-based
Ranking Models
1. Direction of the walk varies based on the
link type
 Intra-ontology links: Reverse PageRank
 Inter-ontology links: PageRank

14. 14
DWRank vs. Linked-based
Ranking Models (cont’d)
2. Linked Analysis :
 HubScore – Concept
o Independently on each ontology
 AuthScore – Ontology
o Ontology Corpus

15. Top-K Filter
15

16. 16
Intended Type Filter
• Intended Type vs. Context Resource
 Name of the Person
o Intended Type: Name
o Context Resource: Person

17. Relationship-based Top-k
Concept Retrieval Phases
17

18. Relationship-based Top-k
Concept Retrieval
• The framework retrieves top-k concepts for
keyword query
– Offline Ranking and Index Construction
– Online Query Processing
18

19. Offline Ranking Index Construction
19

20. Offline Ranking Index Construction
20

21. Offline Ranking Index Construction
21

22. Offline Ranking Index Construction
22

23. Online Query Processing
23
Candidate Result-set
Candidate Result-set
Selection
Selection
HubScore and
HubScore and
AuthScore Selection
AuthScore Selection
Relevance Score of
CandidateList and
Relevance Score of
CandidateList and
Ordering
Ordering
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Ontology
Corpus
Ontology
Corpus
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AAuutthhOOnnttIIddxx
User
Query
Results

24. Evaluation
• Effectiveness of the approach
– Two versions of framework
• DWRank
• DWRank + Filter
• CBRBench – CanBeRra Ontology Benchmark
– Ten sample queries
– Human evaluated gold standard
– Baseline Ranking models
24

25. Evaluation (cont’d)
• Effectiveness metrics
– Precision @ k
– Mean Average Precision @ k
– Discounted Cumulative Gain @ k
– Normalized Discounted Cumulative Gain @ k
25

26. DWRank Effectiveness

27. Intended Type Semantics Filter
Effectiveness

28. Conclusion & Future Work
• We presented
– Ontology Search
– Framework for top-k concept retrieval
– DWRank- Dual Walk Ranking Model
– Experimental Evaluation
• Ranking ontologies for compound concepts
28