1. Context Representation and Reasoning with Formal Ontologies Juan Gómez-Romero 1,2, University Carlos III of Madrid (Spain) Fernando Bobillo 2, University of Zaragoza (Spain) Miguel Delgado 2, University of Granada (Spain) Activity Context Workshop, AAAI’11, August, 2011 (1) Applied Artificial Intelligence Group (2) Approximate Reasoning and A.I. Group

2. Modeling context knowledge with ontologies Context representation Represent context information with standard ontologies Context-based reasoning Reduce the knowledge search space according to current context Extensions to non-classical ontologies Representation of vague, imprecise and uncertain knowledge Representation of context knowledge to reason what is significant and summarize available knowledge Context representation and reasoning with formal ontologies2Aug, 7th 2011

3. Outline 1.A unified view of context (?) 2.Ontologies for context representation 3.Reasoning with context ontologies 4.Extending ontologies to the fuzzy case 5.Conclusions and future work Aug, 7th 2011Context representation and reasoning with formal ontologies3

4. Outline 1.A unified view of context (?) 2.Ontologies for context representation 3.Reasoning with context ontologies 4.Extending ontologies to the fuzzy case 5.Conclusions and future work Aug, 7th 2011Context representation and reasoning with formal ontologies4

5. 1. An unified view on context Schmidt, Beigl and Gellersen (1999): Mix of geo-spatial data, ambient sensor inputs, user profiles (preferences, intentions, history, etc.), and service descriptions Dey and Abowd (2001): Any information (either implicit or explicit) that can be used to characterize the situation of an entity Henricksen (2003): The context of a task is the set of circumstances surrounding it that are potentially of relevance to its completion Kandefer and Shapiro (2008): The structured set of variable, external constraints to some (natural or artificial) cognitive process that influences the behavior of that process in the agent(s) under consideration Gomez-Romero et al. (2011): Any information of interest to the application not directly obtained by the domain data acquisition sensors: common-sense, human feedback, external or a priori resources, etc. Definitions Aug, 7th 2011Context representation and reasoning with formal ontologies5

6. 1. An unified view on context Set of constraints to a reasoning process Soft: Delimit relevant information Hard: Check consistency of world interpretation Influence behavior of the agent Adapt system functioning to the environment Avoid information overload Augment or embellish system results Modify acquired data and acquisition procedures Cognitive process Use of formal specifications vs. ad hoc specifications Context is “first-level” knowledge Characteristics Aug, 7th 2011Context representation and reasoning with formal ontologies6

7. 1. An unified view on context Nomadic Access to Healthcare Information A physicist wants to prescribe a treatment for a patient The HIS provide a report of the patient’s clinical history Information overload : Include only information relevant to the patient’s state, the diagnosis, and clinical procedure that is being carried out Patient is unconscious and has a hemorrhagic laceration Allergies to procaine should be taken into account The example can be extended to other Semantic Web scenarios Keyword-indexed documents Query expansion, query restriction Data visualization http://ecolexicon.ugr.es/visual/index_en.htmlhttp://ecolexicon.ugr.es/visual/index_en.html (Java required) Example case Aug, 7th 2011Context representation and reasoning with formal ontologies7

8. Outline 1.A unified view of context (?) 2.Ontologies for context representation 3.Reasoning with context ontologies 4.Extending ontologies to the fuzzy case 5.Conclusions and future work Aug, 7th 2011Context representation and reasoning with formal ontologies8

9. 2. Ontologies for context representation Representation of the mereological aspects of a reality created from a common perspective and expressed in a formal language Representation formalism that promotes knowledge integration, sharing and reuse Based on Description Logics (DLs), a family of logics with well-defined semantics specially designed to represent structured knowledge DLs are classified in levels (and named) according to their expressivity, which determines the computational complexity of reasoning with the logic (in general DLs, NE XPTIME - COMPLETE ) The Semantic Web uses ontologies to represent metadata and offers several supporting tools, such as the standard OWL language Ontologies Aug, 7th 2011Context representation and reasoning with formal ontologies9

10. 2. Ontologies for context representation Concepts (classes, types) Set of objects with common features FOL unary predicates Instances (individuals) Objects belonging to a class FOL constants Relations (properties, roles) Binary associations between two instances or an instance and a data type value (integers, strings, etc.) FOL binary predicates Axioms Restrictions defining concept, instance and relation features FOL formulas Elements Aug, 7th 2011Context representation and reasoning with formal ontologies10

11. 2. Ontologies for context representation Elements Aug, 7th 2011Context representation and reasoning with formal ontologies11 Context vocabulary Context description

12. Outline 1.A unified view of context (?) 2.Ontologies for context representation 3.Reasoning with context ontologies 4.Extending ontologies to the fuzzy case 5.Conclusions and future work Aug, 7th 2011Context representation and reasoning with formal ontologies12

13. 3. Reasoning with context ontologies Automatic procedure to obtain implicit axioms from explicit axioms modus ponens A A → B B Tableaux algorithms Reasoning algorithms for DLs Implemented by inference engines (HermiT, RACER, Pellet) Theoretical efficiency is high, but worst cases are not frequent Ontology reasoning Aug, 7th 2011Context representation and reasoning with formal ontologies13 Resolution (propositional logic)

14. 3. Reasoning with context ontologies Concept axioms Satisfiability / Consistency A concept is satisfiable if it is not a contradiction to the remaining axioms Subsumption A (super-)concept includes a (sub-)concept Equivalence Two concepts include the same instances Disjointness Two concepts do not have any common instance Instance axioms Satisfiability / Consistency An instance assertion is satisfiable if it is not a contradiction to the remaining axioms Instance checking An instance belongs to a class Entailment An axiom is a logical consequence of a set of axioms Standard reasoning tasks Aug, 7th 2011Context representation and reasoning with formal ontologies14

15. 3. Reasoning with context ontologies Context representation and reasoning Exploitation of ontologies in context-aware ubiquitous computing Interpreting the current user situation Using contextual knowledge to improve the performance of the system Contextualization of ontologies How external or additional knowledge influences the interpretation of an ontology: consistency, validity, partitioning Non-monotonic models vs monotonic DLs Extend the OWL language with non-monotonic features Ontology design patterns Recipes to help ontology developers to capture aspects of the application domain and represent them with existing languages from a common and well-understood perspective No specific pattern aimed to the representation of context knowledge, either for specific or general domains Dealing with context in ontologies Aug, 7th 2011Context representation and reasoning with formal ontologies15

16. 3. Reasoning with context ontologies Proposal Meta-model: design pattern to create context-aware ontologies that avoid information overload. Significance ontologies to represent which information of the domain is relevant in a given context CDS (Context-Domain Significance) pattern formulated in the basic DL ALC Directly translatable into OWL (≈ SHOIN(D)) In several cases, fuzzy knowledge must be considered Extension of the pattern using fuzzy DLs CDS pattern Aug, 7th 2011Context representation and reasoning with formal ontologies16

17. 3. Reasoning with context ontologies Base ontologies Context ontology ( K C ): vocabulary to describe context situations. Domain ontology ( K D ): ontology to represent domain-specific knowledge. New significance ontology : CDS ontology ( K S ) Complex contexts ( C i ): Concepts created using terms of K C. Complex domains ( D j ): Concepts created using terms of K D.  -connection (  i,j or P i,j ): A concept linking a complex context C i and a complex domain D j Denotes that D j is significant in situation C i CDS pattern Aug, 7th 2011Context representation and reasoning with formal ontologies17

18. 3. Reasoning with context ontologies 18Aug, 7th 2011Context representation and reasoning with formal ontologies Domain ontology Context ontology

19. 3. Reasoning with context ontologies Reasoning with the CDS pattern Aug, 7th 2011Context representation and reasoning with formal ontologies19 Domain knowledge I significant in a scenario E Algorithm (implemented in the CDS API) : 1.Retrieve the complex contexts C n more general than E 2.Retrieve the  -connections P n,m involving C n 3.Retrieve the complex domains D m involved in P n,m 4.Retrieve the concepts I of the domain more specific than D m Complete and decidable Complexity is determined by C i and D j (E XP T IME -complete for ALC )

20. Outline 1.A unified view of context (?) 2.Ontologies for context representation 3.Reasoning with context ontologies 4.Extending ontologies to the fuzzy case 5.Conclusions and future work Aug, 7th 2011Context representation and reasoning with formal ontologies20

21. 4. Extending ontologies to the fuzzy case Imprecise knowledge cannot be represented E.g.: A patient is slightly unconscious Partial similarities between contexts cannot be represented E.g.: Anaphylaxis is quite similar to sepsis Relevance relations cannot hold to a degree E.g.: Blood-borne diseases are less relevant than drug intolerances Fuzzy extension of the crisp meta-model, i.e. a design pattern to create fuzzy context-aware ontologies that avoid information overload and allow vague knowledge to be managed Limitations of CDS to manage context knowledge Aug, 7th 2011Context representation and reasoning with formal ontologies21

22. 4. Extending ontologies to the fuzzy case The significance ontology is a fuzzy ontology (fCDS) created with an adaptation of the crisp rules of the CDS pattern The fuzzy significance ontology is expressed with the fuzzy Description Logic f ALC Fuzzy DLs extends DLs to the fuzzy case – Concepts are fuzzy sets– Axioms hold to a degree (inclusion!) – Roles are fuzzy relations– Interpretation has fuzzy semantics Reasoning can be performed with a fuzzy DL reasoner or by reducing the fuzzy ontology to an equivalent crisp DL ontology and using a crisp inference engine (Bobillo, Delgado & Gómez- Romero, 2009) Fuzzy CDS pattern Aug, 7th 2011Context representation and reasoning with formal ontologies22

23. 4. Extending ontologies to the fuzzy case 23Aug, 7th 2011Context representation and reasoning with formal ontologies

24. 4. Extending ontologies to the fuzzy case Reasoning with the fuzzy CDS pattern 24 Domain knowledge I  -significant in a scenario E Knowledge significant and degree of significance aggregation: min t-norm  ⊗  greatest lower bound: glb = sup {  : K  } Complete and decidable Complexity is determined by C i, D j, and the glbs to be calculated

25. Outline 1.A unified view of context (?) 2.Ontologies for context representation 3.Reasoning with context ontologies 4.Extending ontologies to the fuzzy case 5.Conclusions and future work Aug, 7th 2011Context representation and reasoning with formal ontologies25

26. 5. Conclusions and future work Advantages of using ontologies to manage context knowledge Expressiveness Formal representation and reasoning Standard languages and tools Appropriate to deal with information overload Extensions are being studied Future research Standard specification of common context dimensions : location, time, preferences, etc. Privacy issues Study the applicability of full-fledged reasoning in real-world applications Relation with context acquisition and interpretation techniques Are fuzzy extensions necessary/convenient ? Notice! Aug, 7th 2011Context representation and reasoning with formal ontologies26

27. Thank you! Questions, comments? Aug, 7th 2011Context representation and reasoning with formal ontologies27