Representation of Context-Dependent Relevance Relations with Fuzzy Ontologies

1. Representation of Context-Dependent Relevance Relations with Fuzzy Ontologies Juan Gómez-Romero, Fernando Bobillo, Miguel Delgado University of GranadaDepartment of Computer Science and A.I. ESWC 2008

2. outline • Information Overload • CDR Ontology Design Pattern • Fuzzy extension of the CDR pattern • Reasoning with fCDR • Conclusions and future work

3. information overloadbasics • In general: • People get more information than they can process • In Information Systems: • Users are overwhelmed by the provided information • More information = Less knowledge • In Mobile Systems: • Easier to be “overloaded” with information • Solution: • To provide only relevant information • What is relevant? • User environment, preferences, previous behavior, etc. Context!

4. information overloadexample • A doctor is attending to a patient outside the hospital • The doctor… • …uses a portable device to consult the patient’s clinical history, which is stored in the HIS, in order to prescribe a treatment • …retrieves a bunch of EHRs about the patient • …filters the results manually and grasps interesting information (it may take too long) Overload! • Solution: Use information about the context of use: • If the patient is “unconscious” and has an “hemorrhagic laceration”… • …information about if he has been diagnosed of “bad reactions to procaine” should be taken into account

5. outline • Information Overload • CDR Ontology Design Pattern • Fuzzy extension of the CDR pattern • Reasoning with fCDR • Future work

6. CDR design patterndescription • A design pattern for OWL • Represents which information is relevant in a given context • “Context is any information (implicit or explicit) that can be used to characterize the situation of an entity” (Dey and Abowd, 2000) • Relevance CDR ontology • Imports • Context Ontology: vocabulary to describe context situations • Domain Ontology: ontology to represent domain-specific knowledge • Includes links between context descriptions and domain expressions • Profiles: new concepts that connect contexts and domains

7. CDR design patternformulation Domain Context Reasoning: To obtain the domain information which is relevant in a given context

8. outline • Information Overload • CDR Ontology Design Pattern • Fuzzy extension of the CDR pattern • Reasoning with fCDR • Conclusions and future work

9. fuzzy extension of CDRdescription • With the fuzzy CDR ontology: • Imprecise knowledge can be represented • E.g.: A patient is slightly unconscious • Partial simmilarities between contexts can be represented • E.g.: Anaphylaxis is quite similar to sepsis • Relevance relations hold to a degree • E.g.: Blood-borne diseases are less relevant than drug intollerances • Fuzzy extension of the CDR pattern • The CDR ontology is a fuzzy ontologyfCDR • The rules to create the fCDR ontology are a fuzzy adaptation of crisp CDR rules • fCDR is represented with a fuzzy Description Logic

10. fuzzy extension of CDRfuzzy DLs • Fuzzy DLs extend DLs to the fuzzy case • Concepts are fuzzy sets • Roles are fuzzy relations • Axioms hold to a degree • Interpretation has fuzzy semantics • New reasoners are required • Fuzzy ontologies can be reduced to crisp ontologies (DeLorean) • Fuzzy ALC: • TBox. Fuzzy GCIs: C ⊑≥ α D (↔ <C ⊑ D, ≥ a >) • ABox. Fuzzy assertions; e.g. <a : C, ≥ a> • Interpretation; e.g.(C ⊔ D)I = C(x)I⊕D(x)I • Gödelimplicationfortheinterpretation of GCIs • Zadehfamilyfortheinterpretation of theremainingconstructors

11. fuzzy extension of CDRformulation

12. outline • Information Overload • CDR Ontology Design Pattern • Fuzzy extension of the CDR pattern • Reasoning with the fCDR ontology • Conclusions and future work

13. fuzzy extension of CDRinference • Ranked-restricted domain I of a scenario E • Obtains the domain information which is relevant in a given context and its degree aggregation: min t-norm a  b greatest lower bound: glb = sup{a : K <t ≥ a>}

14. fuzzy extension of CDRproperties • Complete • Computational complexity is upper-limited bythe sum of: • The complexity of reducing the fuzzy CDR ontology to a crisp ontology • Quadratic in space for fALC (at most) • It can be reduced to linear if the number of degrees (n) is fixed • It can performed only once under certain conditions • The complexity of the subsumption tests performed (steps 2, 3, 6): • log(n) subsumption tests for each glb • One subsumption test for each concept of the crisp ontology (ExpTime in ALC)

15. outline • Information Overload • CDR Ontology Design Pattern • Fuzzy extension of the CDR pattern • Reasoning with the fCDR ontology • Conclusions and future work

16. conclusions and future work • Conclusions: • Fuzzy relevance ontology improves the crisp approach • Context descriptions can be fuzzy • Retrieved domain knowledge can be ordered by relevance • Only the top-k most interesting domains can be provided • Future work : • Currently, we are working on the mobile healthcare problem (other domains?) • Offer a visual-edition tool (Protégé plug-in) • Reduce complexity of the reasoning process (other semantics? optimizations?) • Compare the fuzzy pattern with related proposals

17. end questions? comments? thank you! ¡muchas gracias!