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A Framework for Ontology Usage Analysis


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Paper received Best PhD Symposium Paper Award at 9th Extended Semantic Web Conference, 2012, Crete, Greece

Paper received Best PhD Symposium Paper Award at 9th Extended Semantic Web Conference, 2012, Crete, Greece

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  • Exploratory data analysis: s an approach to analyzing data sets to summarize their main characteristics in easy-to-understand form, often with visual graphs, without using a statistical model or having formulated a hypothesis
  • What are we trying to achieve in this research? We have seen tremendous growth in the semantic web data (web-of-data) on the web. As a result of it now we have “structured data” on the web in the form of RDF, enabling “ machines ” to automatically understand the data and process it. Now, we have reached to the point where, the availability of semantic data on the web is enabling the possibility of conducting imperial analysis about the data, use of ontologies .
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    • 1. A Framework for Ontology Usage AnalysisJamshaid Ashrafjamshaid.ashraf@gmail.comSupervisor : Dr Omar HussainSchool of Information Systems, Curtin University, Perth, Western AustraliaPhD symposiumESWC 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 ofontologies 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 datasetInvestigation (analysing the use of ontology) - Usage/population/instantiation - Co-usability/schema-link graphRepresentation (represent the usage analysis ) - Conceptual model to represent ontology usage - Ontology Usage CatalogueUtilization (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 otherconcepts and the number of attributes to describe theinstances>Relationship Value (RV): Reflects the possible role of anobject property in creating typed relationship betweendifferent concepts>Attribute Value (RV): Reflects the number of concepts thathave data properties used to provide values to instances
    • 12. Metrics for measuring usage>Concept Usage (CU): Measures the instantiation of theconcept 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 oftriplets in a dataset in which object property is used tocreate relationships between different concept’s instances RU(P) = | { t:=(s,p,o) | p= P} |>Attribute Usage (RU): Measures how much data descriptionis 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 caseWeb Schema construction based on Ontology Usage AnalysisDomain : eCommerceDataset : 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] 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:
    • 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………