The document presents a framework for analyzing usage of domain ontologies on the semantic web. It proposes metrics to measure ontology usage, including concept richness, concept usage, and relationship and attribute values. The framework was implemented to analyze usage of ontologies in datasets from companies like Google and Yahoo. The analysis provided insights into ontology usage trends and patterns in the knowledge bases. Ontology usage analysis can help ontology engineers understand usage and evolve ontologies, as well as anticipate available knowledge when developing applications.

1. Domain Ontology Usage Analysis Framework Analyzing the Usage of Domain Ontologies on the (Semantic) Web Jamshaid Ashraf, M aja Hadzic Presenter : Dr Omar Khadeer Hussain >> SKG2011 , Beijing, China Oct. 24-26, 2011

2. Measuring the Semantic Web on the Web Web Semantic Web data (Linked data cloud) Structured attribute: http://richard.cyganiak.de/2007/10/lod Q) What and how ontologies are being used on the web? This research attempts to answers What Significant growth in semantic web data RDF  enabling machines to read Imperial analysis by using ontologies

3. Growth of Ontologies Tremendous growth in the use of ontologies Swoogle has an index of 10,000 ontologies PingTheSemanticWeb has listed 1442 known namespaces used in the documents Ontologies are developed based on Ontology development process based on certain methodology

4. What is needed? Evaluate and analyse the usage of an ontology Its adoption and uptake by different users on the semantic web. Provide an insight into the structure, understand the pattern available, actual use and the intended use Understand Ontology Usage in Billions of triples in Linked Open Data (LOD) cloud.

5. Contributions of this paper Ontology Usage Analysis Framework Proposes a set of metrics to measure the ontology usage Understand the depth of ontology adoptions and the structured data patterns available in the web of data

6. Difference of Ontology Usage with Ontology Evaluation and Evolution Ontology Usage analyses the use of ontology on the web by measuring its usage, usefulness and commercial advantages Ontology Evaluation is the analysis of guaranteeing what is built meets the requirements and is error free Ontology Evolution is the timely adoption of ontology to the arisen changes and consistent management of these changes Overlap between ontology usage, evaluation and evolution

7. Why Ontology Usage Analysis ( OUA ) Think Design Develop & evaluate (ontology only) Deploy Evangelize Adoption! Measure and analyze Learn from it to influence future thinking and design Typical ontology Lifecycle Ontology OUA contribution (in red) Why

8. Domain O ntology US age A nalysis F ramework (OUSAF)

9. Metrics Concept related metrics Relationship related metrics Attribute (data properties) related metrics

10. Metrics Concept related metrics > Concept Richness (CR): Describes the relationship with other concepts and the number of attributes to describe the instances CR ( C ) = | P C | + | A C | > Concept Usage (CU): Measures the instantiation of the concept in the knowledge base CU(C) = | {t = (s,p,o) | p= rdf:type, o = C }| > Concept Population (CP): Calculates all the triplets in the KB where concept’s instances is used to either create relationships or provide data descriptions CP(C) = | {t = (s,p,o) | s = C(I) , o= C(I) or L }|

11. Metrics Relationship related metrics > Relationship Value (RV): Reflects the possible role of an object property in creating typed relationship between different concepts RV ( P ) = | dom ( P )| + | range ( P ) | > 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 } |

12. Metrics Attribute (data properties) related metrics > Attribute Value (RV): Reflects the number of concepts that have data properties used to provide values to instances AV ( A ) = | dom ( A ) | > 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. Metrics Domain Ontology Population (DOP) Measures the amount of structured data available in the Knowledge base that is annotated using ontology RDF terms Domain Ontology Usage (DOU) Measures the use of ontology vocabulary in the dataset DOU= DOP =

14. Implementation Domain Ontology = Why? Google Yahoo BestBuy Volkswagen Overstock O’Reilly Sears

15. Dataset Collected via Sindice, Watson, Google, Linked Open Commerce, GR wiki 105 web sources (web sites) Published Company and/or product/service Offering GoodRelations Dataset (GRDS)

16. Analysis Concept Richness (CR) in dataset Several object and data properties available in the conceptual model providing rich set for semantic annotation

17. Analysis Concept Richness with Concept Usage A small part of the ontology is widely used. Concepts with higher richness value also have large instantiations Generalized concepts have fewer instantiations compared with specialized concept

18. Analysis Ontology Usage Analysis Provides an overview of ontology usage, trend and patterns available in the KB

19. Conclusion Ontology Usage Analysis helps is finding the data patterns available in the web of data helps ontology engineers/developer to understand the usage patterns and evolve the ontology accordingly helps is mapping different vocabularies based on their usages helps developers in anticipating the available knowledge (T-Box) in knowledgebase

20. Expand the dataset and include other ontologies Automate the analysis process Instance data quality, consistency Recommendations to publishers and vocabulary designers Future work

21. Thanks! Questions……… Please email: jamshaid.ashraf@gmail.com