relationship-based top-k concept retrieval for ontology search
TRANSCRIPT
Relationship-based Top-K Concept Retrieval for Ontology
Search
Anila Sahar ButtAnila Sahar Butt, Armin Haller, Lexing Xie, Armin Haller, Lexing Xie
The Australian National UniversityThe Australian National University
[email protected]@anu.edu.au
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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
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
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Relationship-based Top-k Concept Retrieval
• The framework retrieves top-k concepts for keyword query
DWRank - Ranking Model Top-k Filter
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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
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
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HubScore – Centrality of a Concept
Connectivity : Relations starting from the concept
Neighbourhood :
Relations starting from the concept to another central concept
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@prefix a: http://example.org/def/people#
a:Person
a:Organization a:Project
0.14
0.46
0.380.26
0.140.14
0.14
0.14
Reverse PageRank
AuthScore – Authoritativeness of an OntologyReuse : Relations ending at the ontology
Neighbourhood : Relations starting from another authoritative ontology to the ontology
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:People:Restaurant:Location
0.4710.1450.10
PageRank
Zubeida
Zubeida Zubeida
Zubeida
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DWRank Function
• The Ranking model is function of:• Concept Text Relevancy• HubScore• AuthScore
∑∈
=+=
vssv
*2 *1vO)(v,
))φ(q(q,fQ)(v,F
a(O)]w O)h(v,[w *Q)(v,FR
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DWRank Score
Query: Persono Fv(v,Q) = 1o h(v,O) = 0.46o a(O) = 0.471
a(O)]w O)h(v,[w *Q)(v,FR *2 *1vO)(v, +=
@prefix a: http://example.org/def/people#
a:Person
a:Project
0.14
0.46
0.380.26
0.140.14
0.14
0.14
0.471
0.466
0.471]0.5 0.46[0.5 *1 * *
=+=
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DWRank vs. Linked-based Ranking Models1. Direction of the walk varies based on the
link type Intra-ontology links: Reverse PageRank Inter-ontology links: PageRank
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DWRank vs. Linked-based Ranking Models (cont’d)
2. Linked Analysis : HubScore – Concept
o Independently on each ontology AuthScore – Ontology
o Ontology Corpus
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Intended Type Filter
• Intended Type vs. Context Resource Name of the Person
o Intended Type: Nameo Context Resource: Person
Relationship-based Top-k Concept Retrieval
• The framework retrieves top-k concepts for keyword query
– Offline Ranking and Index Construction– Online Query Processing
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Online Query Processing
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Candidate Result-set Selection
Candidate Result-set Selection
HubScore and AuthScore Selection
HubScore and AuthScore Selection
Relevance Score of CandidateList and
Ordering
Relevance Score of CandidateList and
Ordering
Intended Type FilterIntended Type Filter
Ontology Corpus
Ontology Corpus
IdxIdx
HubConIdxHubConIdx
AuthOntIdxAuthOntIdx
UserQuery
Results
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
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Evaluation (cont’d)
• Effectiveness metrics – Precision @ k – Mean Average Precision @ k – Discounted Cumulative Gain @ k – Normalized Discounted Cumulative Gain @ k
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