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lecture 10: ontology – evolution

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ontology evolutionintroduction

- 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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ontology evolutionintroduction

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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ontology evolutionsix-phase process

1. change capturing2. change representation3. semantics of change4. change implementation5. change propagation6. change validation

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ontology evolutionchange capturing

changes from explicit requirements

changes as results of change discovery – induced from changes in data and usage

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ontology evolutionchange 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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ontology evolutionchange 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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ontology evolutionchange 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the ontology reaches certain levels of size and complexity – decisions about which parts remain relevant and which are outdated is a huge task – usage patterns allow the detection of often or less often used parts, thus reflecting the interests of users in parts of ontologies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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ontology evolutionchange 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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ontology evolutionchange 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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ontology evolutionchange 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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ontology evolutionchange representation (KAON ontology)

elementary changesadd 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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ontology evolutionchange 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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ontology evolutionchange representation (OWL from DL view)

atomic changes – adding or removing axioms

composite changes – a sequence of atomic changes

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ontology evolutionchange representation (RDF)

RDF statements can be only deleted or added, but not modified

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ontology evolutionsemantics of change

change operations need to be managed such that the ontology remains consistent throughout

- preserving constrains

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ontology evolutionsemantics 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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ontology evolutionsemantics 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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ontology evolutionchange 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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ontology evolutionchange 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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ontology evolutionchange implementation (2)

change notificationto 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 applicationapplication of a change should have transactional property – a set of change operations can be easily treated as one atomic transaction

change logginglog to keep information about a type of change, dependencies between changes, as well as the decision-making process

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ontology evolutionchange 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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ontology evolutionchange 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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data-driven ontology changesillustrative 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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data-driven ontology changesillustrative 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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data-driven ontology changesillustrative 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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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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data-driven ontology changes

how ontology should change to what changes to the data

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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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usage-driven ontology changes

analysis of ontology usage is not trivialeven 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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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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usage-driven ontology changes

discovering some anomalies in the ontology design – repairing them improves usabilityoften problem – a hierarchy

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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