A Framework for Ontology Usage AnalysisA 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)

OntologiesOntologies

Instance dataInstance data[1999 – 2006]

Knowledge Focused•Ontology Languages•Ontology authoring tools•Reasoning•Ontology evaluation •Ontology evolution

Knowledge Focused•Ontology Languages•Ontology authoring tools•Reasoning•Ontology evaluation •Ontology evolution

Knowledge focused Knowledge focused

- ONTOLOGYONTOLOGY

OntologiesOntologies

Linked DataLinked Data

Data Focused•Linked Data principles•Linked Open Data project •LOD cloud •RDFa •RDF data analysis

Data Focused•Linked Data principles•Linked Open Data project •LOD cloud •RDFa •RDF data analysis

(Structured) Data focused(Structured) Data focused

[2006 – to data] - LINKED DATALINKED DATA

OntologyOntology

Linked dataLinked data

Current stateCurrent state

…….. searching less and using more .. searching less and using more

Increase in the use of ontologiesIncrease in the use of ontologies

21 May 2012

Lack of visibility 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

Ontology life cycle Ontology life cycle

Ontology Dev. LifecycleOntology Dev. Lifecycle•Think•Design•Develop & evaluate•Deploy•Evangelize•Adoption!

Ontology

• Measure and analyze• Learn from it to influence

future thinking and design

EvaluateEvaluate, measuremeasure and analyseanalyse the use of ontologies on the Web

Benefits of Usage Analysis 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

Ontology Usage Analysis Framework (OUSAF)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

>>Relationship Value Value (RV): Reflects the possible role of an object property in creating typed relationship between different concepts

>>Concept Richness Richness (CR): Describes the relationship with other concepts and the number of attributes to describe the instances

>>Attribute Value Value (RV): Reflects the number of concepts that have data properties used to provide values to instances

Metrics for measuring richnessMetrics for measuring richness

>>Relationship Usage 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} |

CU(C) = |{t = (s, p, o)| p = rdf:type, o = C}|1

>>Concept Usage Usage (CU): Measures the instantiation of the concept in the knowledge base

CUH(C) = |{t = (s, p, o)| p = rdf:type, o entailrdfs9(C)}|

>>Attribute Usage 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) |

Metrics for measuring usage Metrics for measuring usage

Structural propertiesStructural 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

GR data coverage

Initial Results – domain ontology usageInitial Results – domain ontology usage

Initial Results – use case Initial Results – use case

Web Schema construction based on Ontology Usage Analysis

Domain : eCommerceDataset : 305 data sources (pay-level domains published ecommerce data)

Ranking the terms

U Ontology U Ontology

Reusing existing ontologies-Ontology Metadata Vocabulary (OMV) [1]-Ontology Application Framework (OAF) [2]-FOAF, DC

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

[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

WebWebSemantic Web data

(Linked data cloud) StructuredSemantic Web data

(Linked data cloud) Structured

attribute: http://richard.cyganiak.de/2007/10/lod

Conclusion Conclusion

http://www.cs.vu.nl/~frankh/spool/ISWC2011Keynote/

What and how ontologies are being used on the web?

Ontology Usage Catalogue (Michael Uschold)

Future workFuture 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

Thanks! Thanks!

Questions………Questions………