1. ece 627 intelligent web: ontology and beyond
lecture 10: ontology – evolution

2. ontology evolution introduction
ontologies enable knowledge to be made explicit and formal, machine processable and interpretable
ontologies offer a prospect of significant improvement to the information retrieval tasks:
classification of documents according to a given topic
semantic annotation of individual documents
semantic user profiles
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3. ontology evolution introduction
ontologies – to be effective – need to change as fast as the parts of the world they describe
- changes in people’s interests
- changes in data
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4. ontology evolution six-phase process
change capturing
change representation
semantics of change
change implementation
change propagation
change validation
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5. ontology evolution change capturing
changes from explicit requirements
changes as results of change discovery – induced from changes in data and usage
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6. ontology evolution change capturing – explicit requirements
generated, for example, by ontology engineers who want to adapt the ontology to new requirements of or by end-users who provide the explicit feedback about the usability of ontology entities
these changes are called – top-down changes
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7. ontology evolution change capturing – change discovery
so-called bottom-up changes – discovered only through the analysis of system’s behavior
usage-driven
data-driven
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8. ontology evolution change capturing – usage-driven discovery
usage patterns created over a period of time
once the ontology reaches certain levels of size and complexity – decisions about which parts remain relevant and which are outdated is a huge task – usage patterns allow the detection of often or less often used parts, thus reflecting the interests of users in parts of ontologies
(more on slides 30-33)
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9. ontology evolution change capturing – data-driven discovery
any change to the underlying data set – a newly added document or changed database entry – might require an update of the ontology
can be defined as the task of deriving ontology changes from modifications to the knowledge from which the ontology has been constructed
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10. ontology evolution change capturing – data-driven discovery (2)
deriving ontological changes from ontology instances by applying techniques of data-mining, formal concept analysis (FCA) or various heuristics
(more on slides 24-29)
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11. ontology evolution change representation
changes have to be represented in a suitable format (for a given ontology model – most popular are object models centered around classes, properties)
changes can be represented on various levels of granularity (elementary vs. general)
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12. ontology evolution change representation (KAON ontology)
elementary changes
add or remove applied to an entity in the ontology model
composite changes
a change that modifies (create, remove or change) one and only one level of neighborhood of entities (neighborhood is defined via structural links between entities) – pull concept up, copy concept, split concept
complex changes
a change that can be decomposed into at least 2 elementary/composite ones
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13. ontology evolution change representation (OWL)
atomic changes – delete, add, modify
composite changes – grouped operations that constitute a logical entity
simple changes – can be detected by analyzing the structure of the ontology only
rich changes – imply operations on the logical model of the ontology
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14. ontology evolution change representation (OWL from DL view)
atomic changes – adding or removing axioms
composite changes – a sequence of atomic changes
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15. ontology evolution change representation (RDF)
RDF statements can be only deleted or added, but not modified
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16. ontology evolution semantics of change
change operations need to be managed such that the ontology remains consistent throughout
- preserving constrains
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17. ontology evolution semantics of change (2)
structural consistency –
ontology obeys the constrains of the ontology language with
the respect to the constructs
logical consistency –
ontology is logically consistent if it is satisfiable, meaning that is
does not contain contradicting information
user-defined consistency –
constrains given by some application or usage context, defined
explicitly by the user
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18. ontology evolution semantics of change (3)
verification of consistency
a posteriori verification – first the changes are executed and then the updated ontology is checked
a priori verification – a set of preconditions for each change is defined, and it has to be proven that the consistency will be maintained
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19. ontology evolution change propagation
to ensure consistency of dependent artefacts
those artefacts may include dependent ontologies, instance, as well as application programs using the ontology
push-based and pull-based approaches for synchronization of dependent ontologies
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20. ontology evolution change implementation
this phase it to:
inform an ontology engineer about all consequences of a change request
apply all the required and derived changes
keep track of performed changes
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21. ontology evolution change implementation (2)
change notification
to avoid undesired changes, a list of all implications should be generated and presented to the ontology engineer, who should then be able to accept or abort these changes
change application
application of a change should have transactional property – a set of change operations can be easily treated as one atomic transaction
change logging
log to keep information about a type of change, dependencies between changes, as well as the decision-making process
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22. ontology evolution change validation
the task of this phase is to recover from “undesired” changes
enables justification of performed changes or undoing them at user’s request
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23. ontology evolution change validation (2)
“undesired” changes
the ontology engineer may fail to understand the actual effect of the change and approve a change that should not be performed
it may be desired to change the ontology for experimental purposes
when working on an ontology collaboratively, different ontology engineers may have different ideas how the ontology should be changed
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24. data-driven ontology changes illustrative example (1)
ontology-based searching:
the user selects a concept from a domain ontology, and searches for an instance of that concept;
the search engine examines the ontological metadata added to the content of each document in order to find documents which are most likely to be relevant to the query
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25. data-driven ontology changes illustrative example (2)
topic/hierarchy browsing:
a hierarchy of topics is used to classify a corpus of documents;
classification can be done automatically based on ontological knowledge extracted from the documents
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26. data-driven ontology changes illustrative example (3)
contextualized search:
the user searches for a keyword and the system concludes from his semantic user profile and his current working context that he is looking for information about a certain “thing”
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27. data-driven ontology changes
a tight relationship between the ontology and the underlying data is required
new documents/texts added -> all ontolgoies have to be adapted to reflect the knowledge gained
annotations of documents have to be updated based “new” ontolgoies
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28. data-driven ontology changes
how ontology should change to
what changes to the data
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29. data-driven ontology changes
what kinds of knowledge in a change discovery system should be generated or represented
generic knowledge
about relationships between data and ontology (for example, heuristics of how to identify concepts and their taxonomic relationships in the data)
concrete knowledge
about relationships between data and ontology concepts, instances and relations – because deleting or modifying information in the data may have an impact on existing elements in ontology
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30. usage-driven ontology changes
analysis of ontology usage is not trivial
even if a meaningful usage pattern is found – the question is how to translate it into a change that leads to the improvement of ontology
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31. usage-driven ontology changes
support for usage-driven changes can be split into two phases:
to help the ontology engineer find changes that should be performed
to help the engineer in performing such changes
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32. usage-driven ontology changes
discovering some anomalies in the ontology design – repairing them improves usability
often problem – a hierarchy
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33. usage-driven ontology changes
hierarchy problem:
the concept X has ten sub-concepts, the usage showed that 95% of users are interested in only three of those sub-concepts
expansion
to move all seven ‘irrelevant’ sub-concepts down by grouping them under a new sub-concept
reduction
to remove all seven sub-concepts, and move their instances into the remaining sub-concepts or the parent concept
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