domain ontology usage analysis framework (ousaf)

Domain Ontology Usage Analysis Framework Analyzing the Usage of Domain Ontologies on the (Semantic) Web

Jamshaid Ashraf, Maja Hadzicaja Hadzic

Domain Ontology Usage Analysis Framework Analyzing the Usage of Domain Ontologies on the (Semantic) Web

Jamshaid Ashraf, Maja Hadzicaja Hadzic

Presenter : Presenter : Dr Omar Khadeer HussainDr Omar Khadeer Hussain

>> SKG2011 , Beijing, China       Oct. 24-26, 2011

Measuring the Semantic Web on the WebWebWeb

Semantic Web data (Linked data cloud)

Structured

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 What

Significant growth in semantic web dataRDF enabling machines to read

Imperial analysis by using ontologies

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

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.

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

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

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 LifecycleTypical ontology Lifecycle

Ontology

OUA contribution (in red)OUA contribution (in red)

Why Why

Domain Domain OOntology ntology USUSage age AAnalysis nalysis FFramework ramework (OUSAF) (OUSAF)

Metrics Metrics

ConceptConcept related metrics

RelationshipRelationship related metrics

AttributeAttribute (data properties) related metrics

Metrics Metrics

ConceptConcept related metrics

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

CR (C) = |PC| + |AC|

>>Concept Usage 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 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 }|

Metrics Metrics

RelationshipRelationship related metrics

>>Relationship Value 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 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} |

Metrics Metrics

Attribute (data properties)Attribute (data properties) related metrics

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

AV (A) = | dom(A) |

>>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 Metrics

Domain Ontology Population (DOP)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)Domain Ontology Usage (DOU)

Measures the use of ontology vocabulary in the dataset

DOU= KBS

DOP

DOP =

Implementation Implementation

Domain Ontology = Domain Ontology =

Why?

GoogleYahooBestBuyVolkswagenOverstockO’ReillySears

Dataset 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)(GRDS)

AnalysisAnalysis

Concept Richness (CR) in dataset

Several object and data properties available in the conceptual model providing rich set for semantic annotation

AnalysisAnalysis Concept Richness with Concept Usage

A small part of the ontology is widely used.Concepts with higher richness value also have large instantiationsGeneralized concepts have fewer instantiations compared with specialized concept

AnalysisAnalysis

Ontology Usage Analysis

Provides an overview of ontology usage, trend and patterns available in the KB

Conclusion 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

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

Future workFuture work

Thanks! Thanks!

Questions……… Please email: jamshaid.ashraf@gmail.com Questions……… Please email: jamshaid.ashraf@gmail.com