Ontology Change: Classification and Survey
Reference: Flouris, Manakanatas, Kondylakis, Plexousakis, Antoniou (2008). The Knowledge Engineering Review, Cambridge University Press. Source file: Ontology_Change_Classification_and_Survey.pdf. URL
Summary
This survey tackles the terminological confusion surrounding ontology change in the Semantic Web era. The authors argue that many overlapping terms — ontology evolution, versioning, merging, mapping, matching, articulation, translation, debugging, integration, morphism — are used inconsistently across the literature, creating a major bottleneck for research. They propose a unifying terminology and taxonomy, fixing precise definitions and identifying the boundaries between ten subfields of ontology change plus ontology alignment.
The paper organizes these subfields into four groups: heterogeneity resolution (mapping/matching/articulation/morphism/translation), modification (evolution, debugging/diagnosis/repair), fusion (integration, merging), and versioning. Each field is characterized by its purpose, inputs, outputs, and properties, and the authors review representative algorithms and systems, including a detailed classification of matching approaches (instance vs. schema, element vs. structure, language vs. constraint, matching cardinality, auxiliary information).
Key Ideas
- Ontology change is the generic process of adapting an ontology to a need for change.
- Heterogeneity resolution is a prerequisite for any successful ontology change.
- Formal pair <S, A> defines an ontology by signature and axioms.
- Ten interlinked subfields are identified and disambiguated.
- Ontology evolution is closely tied to belief revision.
Connections
Conceptual Contribution
- Claim: Research on ontology change is fragmented by inconsistent terminology; a precise, unified taxonomy is needed before the field’s problems can be compared, composed, or solved.
- Mechanism: The authors define an ontology formally as a signature–axiom pair <S,A>, then enumerate ten subfields (mapping, matching, morphism, articulation, translation, evolution, debugging, versioning, integration, merging) and characterise each by inputs/outputs/properties. Representative algorithms and systems are then slotted into the taxonomy, with matching approaches classified by dimension (schema vs. instance, element vs. structure, cardinality, auxiliary info).
- Concepts introduced/used: Ontology Change, Ontology Evolution, Ontology Mapping, Ontology Merging, Ontology Alignment, Belief Revision, Semantic Web, Heterogeneity Resolution
- Stance: survey
- Relates to: Provides the vocabulary implicitly assumed by ACL work on shared ontologies (The State of the Art in Agent Communication Languages, Trends in Agent Communication Language) and complements the handbook-level treatment in Handbook On Ontologies. Its belief-revision framing links to epistemic semantics used in Agent-Oriented Programming.