Heraclitus II: A Framework for Ontology Management and EvolutionAlexander Mikroyannidis and Babis Theodoulidis

Manchester Business School, United Kingdom

Introduction

• Ontologies are a key factor in Information Management (IM) since they offer information a common representation and semantics.

• The knowledge that ontologies represent is not static, but evolves over time, thus requiring appropriate ontology management and evolution mechanisms.

• However, the approach adopted in most IM systems regarding ontology management is simplistic.

• The Heraclitus framework addressed the evolution of Semantic Web ontologies from a user-oriented perspective.

• Heraclitus II extends this approach to address ontology management and evolution in IM systems.

Ontology model

• The ontology model used in Heraclitus II is based on the TAU object model.

• The TAU model is an extended version of ODMG that supports modelling and reasoning about time and evolution. TAU adopts a discrete model of time and supports multiple granularities.

• Facts can be associated with transaction time, valid time, both (bitemporal) or none (static).

• The valid time of a fact is defined as the time when that fact is true in the modelled reality.

• The transaction time of a fact is defined as the time when that fact is current in the knowledge base of the IM system and may be retrieved.

Ontology layering

Source editors

Lexical Ontology

Domain Ontology

Data Source Ontology

Application Ontology

Intra & inter-layer ontology mapping

Ontology authors

Ontology layering (contd.)

• The lower layers represent more generic and all-purpose ontologies, while the upper layers are customized for certain uses in an IM system. Each layer reuses and extends the ones below it.

• Each layer is maintained by a different group of ontology authors, depending on the expertise that each layer requires.

• The integration of the pyramid layers is achieved with the use of ontology mapping between ontologies belonging to the same layer (intra-layer), or different ones (inter-layer).

Ontology layering (contd.)

• Lexical Ontology layer: Domain-independent ontologies of a purely lexicographical nature.

– Author group: Lexicographers.

– Example: WordNet.

• Domain Ontology layer: Modelling of a certain domain.

– Author group: Domain experts, e.g. biologists for a biology-related ontology.

– Example: The Gene Ontology (GO).

Ontology layering (contd.)

• Data Source Ontology layer: Organization of information in data sources of IM systems (e.g. news portals, corporate databases).

– Author group: Authors and editors of data sources.

– Example: The web site ontology of the Heraclitus framework.

• Application Ontology layer: Software development ontologies that represent the internal organization of an IM application.

– Author group: Software developers.

– Example: The GATE ontology.

Ontology evolution requirements

• Bitemporal evolution: Evolution of ontology objects over two dimensions of time, namely valid and transaction time. Retro-active and pro-active changes captured and represented in the knowledge base.

• Consistency preservation: Preservation of the structural and semantic consistency of ontologies.

• Change propagation: Performed in two levels: internally (inside the changed ontology) and externally (in depending ontologies).

• Transparency: Human authors involved in the construction and evolution of ontologies.

Ontology evolution• Change capture:

– Usage-driven

– Data-driven

• Atomic changes:

– Insertion

– Removal

– Modification

• Composite changes:

– Merging

– Split

– Transfer

Changecapture

Structural inconsistencies

detection & resolution

Semantic inconsistencies

detection & resolution

Ontology author review

Change application & propagation

Conclusion

• Highlights of Heraclitus II:

– Temporal ontology model

– Multi-layer architecture for use in IM systems

– Transparency, consistency preservation and bitemporal modelling in evolution

• For more info see:

– A. Mikroyannidis and B. Theodoulidis, “Heraclitus II: A Framework for Ontology Management and Evolution,” in Proc. 2006 IEEE /WIC/ACM International Conference on Web Intelligence (WI 2006), Hong Kong, China, 2006, pp. 514-521. [IEEE Xplore]