1. of 33 lecture 10: ontology – evolution

2. of 33 ece 720, winter ‘122 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

3. of 33 ece 720, winter ‘123 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

4. of 33 ece 720, winter ‘124 ontology evolution six-phase process 1. change capturing 2. change representation 3. semantics of change 4. change implementation 5. change propagation 6. change validation

5. of 33 ece 720, winter ‘125 ontology evolution change capturing changes from explicit requirements changes as results of change discovery – induced from changes in data and usage

6. of 33 ece 720, winter ‘126 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

7. of 33 ece 720, winter ‘127 ontology evolution change capturing – change discovery so-called bottom-up changes – discovered only through the analysis of system’s behavior - usage-driven - data-driven

8. of 33 ece 720, winter ‘128 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the ontology reaches certain levels of size and complexity – decisions about which parts remain relevant and which are outdated is a huge task – usage patterns allow the detection of often or less often used parts, thus reflecting the interests of users in parts of ontologies (more on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)

9. of 33 ece 720, winter ‘129 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

10. of 33 ece 720, winter ‘1210 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)

11. of 33 ece 720, winter ‘1211 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)

12. of 33 ece 720, winter ‘1212 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

13. of 33 ece 720, winter ‘1213 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

14. of 33 ece 720, winter ‘1214 ontology evolution change representation (OWL from DL view) atomic changes – adding or removing axioms composite changes – a sequence of atomic changes

15. of 33 ece 720, winter ‘1215 ontology evolution change representation (RDF) RDF statements can be only deleted or added, but not modified

16. of 33 ece 720, winter ‘1216 ontology evolution semantics of change change operations need to be managed such that the ontology remains consistent throughout - preserving constrains

17. of 33 ece 720, winter ‘1217 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

18. of 33 ece 720, winter ‘1218 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

19. of 33 ece 720, winter ‘1219 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

20. of 33 ece 720, winter ‘1220 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

21. of 33 ece 720, winter ‘1221 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

22. of 33 ece 720, winter ‘1222 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

23. of 33 ece 720, winter ‘1223 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

24. of 33 ece 720, winter ‘1224 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

25. of 33 ece 720, winter ‘1225 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

26. of 33 ece 720, winter ‘1226 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”

27. of 33 ece 720, winter ‘1227 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

28. of 33 ece 720, winter ‘1228 data-driven ontology changes how ontology should change to what changes to the data

29. of 33 ece 720, winter ‘1229 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

30. of 33 ece 720, winter ‘1230 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

31. of 33 ece 720, winter ‘1231 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

32. of 33 ece 720, winter ‘1232 usage-driven ontology changes discovering some anomalies in the ontology design – repairing them improves usability often problem – a hierarchy

33. of 33 ece 720, winter ‘1233 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