The document discusses a framework for ontology usage analysis, addressing the increase in ontology use and the need for better visibility into how they are utilized in the semantic web. It presents the Ontology Usage Analysis Framework (OUSAF), which includes methods for identifying ontologies, analyzing their usage, and implementing metrics to evaluate concept, relationship, and attribute usage. The conclusion emphasizes the importance of understanding ontologies' roles in the linked data cloud and suggests future work to enhance datasets and automate the ontology usage catalog publication.

1. A Framework for Ontology Usage Analysis
Jamshaid Ashraf
jamshaid.ashraf@gmail.com
Supervisor : Dr Omar Hussain
School of Information Systems, Curtin University, Perth, Western Australia
PhD symposium
ESWC 2012, Heraklion, Crete, Greece (27- 31 May 2012)

2. Knowledge focused
[1999 – 2006] - ONTOLOGY
Instance data
Ontologies
Knowledge Focused
•Ontology Languages
•Ontology authoring tools
•Reasoning
•Ontology evaluation
•Ontology evolution

3. (Structured) Data focused
Ontologies
[2006 – to data] - LINKED DATA
Linked Data
Data Focused
•Linked Data principles
•Linked Open Data project
•LOD cloud
•RDFa
•RDF data analysis

4. Current state
Ontol
og y
ata
Li n ked d
….. searching less and using more

5. Increase in the use of ontologies
21 May 2012

6. Lack of visibility
- Index such as PingTheSemanticWeb does not provide a detailed
view of ontology usage
- In order to make effective and efficient use of semantic web
data, we need to know which concepts and relationships and
how are being used?
- An insight into the structure, understand the pattern available,
actual use and the intended use

7. Ontology life cycle
Ontology
Ontology Dev. Lifecycle
•Think
•Design
•Develop & evaluate
•Deploy
•Evangelize
•Adoption!
• Measure and analyze
• Learn from it to influence
future thinking and design

8. Evaluate, measure and analyse the use of
ontologies on the Web

9. Benefits of Usage Analysis
(1) Helps in providing usage-based feedback loop to the ontology
maintenance process for a pragmatic conceptual model update
(2) Assist in building data rich interfaces, exploratory search and
exploratory data analysis
(3) Provides erudite insight on the state of semantic structured data based
on prevalent knowledge patterns for the consuming applications

10. Ontology Usage Analysis Framework (OUSAF)
Identification (selection of ontologies)
- Domain Ontology
- Identify candidate ontology(ies) from dataset
Investigation (analysing the use of ontology)
- Usage/population/instantiation
- Co-usability/schema-link graph
Representation (represent the usage analysis )
- Conceptual model to represent ontology usage
- Ontology Usage Catalogue
Utilization (making use of usage analysis )
- Use case implementation
- Publication of ontology usage analysis

11. Metrics for measuring richness
>Concept Richness (CR): Describes the relationship with other
concepts and the number of attributes to describe the
instances
>Relationship Value (RV): Reflects the possible role of an
object property in creating typed relationship between
different concepts
>Attribute Value (RV): Reflects the number of concepts that
have data properties used to provide values to instances

12. Metrics for measuring usage
>Concept Usage (CU): Measures the instantiation of the
concept in the knowledge base
CU(C) = |{t = (s, p, o)| p = rdf:type, o = C}|1
CUH(C) = |{t = (s, p, o)| p = rdf:type, o entailrdfs9(C)}|
>Relationship Usage (RU): Calculates the number of
triplets in a dataset in which object property is used to
create relationships between different concept’s instances
RU(P) = | { t:=(s,p,o) | p= P} |
>Attribute Usage (RU): Measures how much data description
is available in the knowledge base for a concept instance
AU(A) = | { t:=(s,p,o) | p A, o L) |

13. Structural properties
Represent ontology usage as a bipartite network
-Hidden properties in ontology usage network to identify
cohesive groups and measure semanticity.
-Study structural properties such as centrality, reciprocity,
density and reachability
Capture the knowledge patterns
-Schema level patterns Hidden properties in ontology usage
network to identify cohesive groups and measure
semanticity.
-Study structural properties such as centrality, reciprocity,
density and reachability

14. Initial Results – domain ontology usage
GR data coverage

15. Initial Results – use case
Web Schema construction based on Ontology Usage Analysis
Domain : eCommerce
Dataset : 305 data sources (pay-level domains published ecommerce data)
Ranking the terms

16. U Ontology
Ontology Usage Ontology (U Ontology)
Goal : Capture the detail of ontologies and their usage
Use cases :
- publish the ontology usage details on the web.
- generate prototypical SPARQL queries
Reusing existing ontologies
-Ontology Metadata Vocabulary (OMV) [1]
-Ontology Application Framework (OAF) [2]
-FOAF, DC
[1] Hartmann, J., Palma, R., Sure, Y., Suárez-Figueroa, M.C., Haase P.: OMV– Ontology Metadata Vocabulary. In: The
Ontology Patterns for the Semantic Web (OPSW) Workshop at ISWC 2005, Galway, Ireland (2005)
[2] http://ontolog.cim3.net/file/work/OntologySummit2011/ApplicationFramework/OWL-Ontology/BenefitsAndTechniques-
WithDocumentation.pdf

17. Conclusion
What and how Semantic Web data
Web (Linked data cloud) Structured
ontologies are being
used on the web?
Ontology Usage Catalogue
(Michael Uschold) attribute: http://richard.cyganiak.de/2007/10/lod
http://www.cs.vu.nl/~frankh/spool/ISWC2011Keynote/

18. Future work
• Build industry specific datasets to understand the ontology
usage, data and knowledge patterns.
• Automate the population of U Ontology
• Publication of Ontology Usage catalogue
• Recommendations to publishers and vocabulary designers

19. Thanks!
Questions………