This talk was presented in Leipzig, during the SEMANTiCS '2014 Conference, in September. It basically gives an overview of how Information Content Theory metrics can be applied to Semantic Web, and especially to vocabularies. The results of the proposed ranking metrics can be applied in three areas: (1) vocabulary life-cycle management, (ii) semantic web visualizations and (iii) Interlinking process.

1. Information Content based
Ranking Metric for Linked Open
Vocabularies
Ghislain A. Atemezing (@gatemezing)
Raphaël Troncy (@rtroncy)

2. Goal and Agenda
 Goal: Present a new ranking metric for reusing
vocabularies
 Motivation
 Combine Information Theory with metadata information
 Find new assessment metric for vocabularies
 Current situation
 Unicity of popularity based-metric (e.g. prefix.cc or lodstats)
 Only ONE dimension used for assessing vocabularies
 Proposal: compute informativeness of LOV terms
 Experiments and Results
 Applications
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3. Vocabulary Purpose
 Model to understand a domain’s semantics
 Vocabulary terms contain information
 A term = Class, Object Property, Data Property
 Essential for publishing data on the Web
 How to quantify value of a term?
 Informativeness value = negative relation with
probability
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4. Existing catalogs of vocabularies
Some catalogs of vocabularies
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5. Linked Open Vocabularies (LOV)
 A curated list of vocabularies
 More than 420 vocabularies
Each of them described by the vocabulary-of-a-friend
(voaf) schema
 Track the (temporal) evolution of vocabularies
 Some related services
SPARQL endpoint: http://lov.okfn.org/endpoint/lov
Search function: http://lov.okfn.org/dataset/lov/search
An Aggregator endpoint:
http://lov.okfn.org/endpoint/lov_aggregator
An intelligent bot agent for updates:
http://lov.okfn.org/dataset/lov/bot
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6. LOV DESCRIPTION: http://lov.okfn.org/dataset/lov/
CORE FEATURES OF THE FRAMEWORK
Domain Intended Use Collection Gatekeeping
Number of
Ontologies
Dynamics
Search
metadata
Search
within
ontology
Search across
ontologies
Navigation
criteria
General
Promote and
facilitate the
reuse of
vocabularies in
the linked data
ecosystem.
Submitted by any
user via LOV-Suggest
tool.
Manual
curation and
automatic URI
validation
450+ Growing
Yes, with
visual
depiction
Yes
Keyword-based;
structured
search (query-based)
Ordered by
prefix,
namespace,
title and
visual links
navigation
CORE FEATURES OF THE FRAMEWORK
Metrics
Comments
and review
Ranking
Web
service
access
SPARQL
endpoint
Content
available
Read/
Write
Ontology
directory
Ontology
registry
Applicatio
n platform
Reuse
popularity on
the LOD
Cloud
N/A - Only by
the curators
Metric-based
API Yes
Ontology
metadata
, URI
Read Yes Yes Yes
LOV DESCRIPTION WITH THE FRAMEWORK OF [d’Aquin-Noy2012-Survey]
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7. LOV Evolution since March, 2011
Quasi linearity of the growth,
started with 75 vocabularies
The glitch in 2012
corresponds to the
migration to OKFN
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8. Proposal: Metrics for Ranking LOV
 Metrics
Information Content Metric (IC): value of
information associated with a given entity
Partition Information Content Metric (PIC)
Proposed a ranking based on IC and PIC
 Method
Adapt IC and PIC function on semantics
Select candidate vocabularies in LOV catalog
Compute the scores
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9. Information Content Metrics for LOV
 Information Content
 Formula:
 N = MAX value of term
occurrence in LOV
 φ(t)=occurrence of
term in LOV
 Partitioned IC
 LOV is a semantic
network of resources
 Formula:
 wf= weight for vocab f
+objectURI+ =
owl:ObjectProperty/Datatyp
eProperty; rdfs:Property
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10. Information Content Metrics for LOV
 (Light)weighting
scheme
 wf=2 if datasets are using
vocabulary
 wf=1 if vocabulary reused
other vocabularies.
 wf=3 if vocabulary reused
elsewhere
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11. Ranking Algorithm
1- Candidate terms selection in LOV
2- Grouping terms by namespace &
weight assignment
3- Compute IC score
4- Compute PIC score
Output ranking
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12. Running Example: dcterms vs foaf
 dcterms:
http://purl.org/dc/terms/
 Candidate terms: 53 (39
properties + 14 classes)
 wf = 1+ 2+3 = 6
 PIC = 1724.844
 foaf:
http://xmlns.com/foaf/0.1/
 Candidate terms: 35 (26
properties + 9 classes)
 wf = 1+ 2+ 3 = 6
 PIC = 1033.197
PIC(dcterms) > PIC(foaf)
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13. Results on Ranking
Top-15 terms (IC value) Top-15 vocabs (PIC value)
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14. Comparison
 Relative stable position of foaf in prefix.cc,
vocab.cc and lodstats catalogues.
 LOV-PIC/LODstats: skos, dcterms
with “relative” stable raking.
 List of “most popular”
vocabularies: foaf, skos,
dcterms, time, dce, prov.
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15. Applications of the Ranking Metrics
 Vocabulary life-cycle management
Help assessing the use of terms and vocabulary updates
 Monitoring the use of http://www.w3.org/2003/06/sw-vocab-
status/ns#:term_status or owl:deprecated
 Semantic Web applications
Vocabularies with higher PIC might be proposed to a
user as much as possible, e.g. for choosing properties to
display in a facetted browsing interface
 Interlinking datasets
 Generate sameAs links with data based on vocabularies
terms with lower IC value
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16. Conclusion and Future Work
 We have presented new metrics for ranking
vocabularies
 By applying Information Content concept to LOV
 By taking more dimensions in the ranking metrics
 The metrics can be applied to vocabulary
reused, ontology modelling and visualizations
 Future work
Add equivalence axioms in the ranking model
Compare (P)IC with other graph-based ranking
(e.g. pagerank)
 Investigate the dependency ranking between vocabularies
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17. Thanks for your attention!
Q/A Session