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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.

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context representation and reasoning with formal ontologies

Context Representation and Reasoning with Formal Ontologies

Juan Gómez-Romero1,2, University Carlos III of Madrid (Spain)

Fernando Bobillo2, University of Zaragoza (Spain)

Miguel Delgado2, University of Granada (Spain)

Activity Context Workshop, AAAI’11, August, 2011

(1) Applied Artificial Intelligence Group

(2) Approximate Reasoning and A.I. Group

slide2
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 knowledgeto reason what is

significantandsummarizeavailable knowledge

Context representation and reasoning with formal ontologies

slide3
Outline
  • A unified view of context (?)
  • Ontologies for context representation
  • Reasoning with context ontologies
  • Extending ontologies to the fuzzy case
  • Conclusions and future work

Context representation and reasoning with formal ontologies

slide4
Outline
  • A unified view of context (?)
  • Ontologies for context representation
  • Reasoning with context ontologies
  • Extending ontologies to the fuzzy case
  • Conclusions and future work

Context representation and reasoning with formal ontologies

1 an unified view on context
1. An unified view on context

Definitions

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.

Context representation and reasoning with formal ontologies

1 an unified view on context1
1. An unified view on context

Characteristics

  • 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

Context representation and reasoning with formal ontologies

1 an unified view on context2
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.html (Java required)

Example case

Context representation and reasoning with formal ontologies

slide8
Outline
  • A unified view of context (?)
  • Ontologies for context representation
  • Reasoning with context ontologies
  • Extending ontologies to the fuzzy case
  • Conclusions and future work

Context representation and reasoning with formal ontologies

2 ontologies for context representation
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, NExptime-complete)

The Semantic Web uses ontologies to represent metadata and offers several supporting tools, such as the standard OWL language

Ontologies

Context representation and reasoning with formal ontologies

2 ontologies for context representation1
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

Context representation and reasoning with formal ontologies

2 ontologies for context representation2
2. Ontologies for context representation

Elements

Context vocabulary

Context description

Context representation and reasoning with formal ontologies

slide12
Outline
  • A unified view of context (?)
  • Ontologies for context representation
  • Reasoning with context ontologies
  • Extending ontologies to the fuzzy case
  • Conclusions and future work

Context representation and reasoning with formal ontologies

3 reasoning with context ontologies
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

Resolution(propositionallogic)

Context representation and reasoning with formal ontologies

3 reasoning with context ontologies1
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

Context representation and reasoning with formal ontologies

3 reasoning with context ontologies2
3. Reasoning with context ontologies

Context representation and reasoning

  • Exploitation of ontologiesin 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

  • Recipesto 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

Context representation and reasoning with formal ontologies

3 reasoning with context ontologies3
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

Context representation and reasoning with formal ontologies

3 reasoning with context ontologies4
3. Reasoning with context ontologies

Base ontologies

  • Context ontology(KC): vocabulary to describe context situations.
  • Domain ontology(KD): ontology to represent domain-specific knowledge.

New significance ontology:CDS ontology (KS)

  • Complex contexts (Ci):
    • Concepts created using terms of KC.
  • Complex domains (Dj):
    • Concepts created using terms of KD.
  • s-connection (si,jor Pi,j):
    • A concept linking a complex context Ci and a complex domain Dj
    • Denotes that Dj is significant in situation Ci

CDS pattern

Context representation and reasoning with formal ontologies

3 reasoning with context ontologies5
3. Reasoning with context ontologies

Context

ontology

Domain

ontology

Context representation and reasoning with formal ontologies

3 reasoning with context ontologies6
3. Reasoning with context ontologies

Reasoning with the CDS pattern

  • Domain knowledgeIsignificantin a scenarioE
  • Algorithm (implemented in the CDS API):
    • Retrieve the complex contexts Cn more general than E
    • Retrieve the s-connections Pn,m involving Cn
    • Retrieve the complex domains Dminvolved in Pn,m
    • Retrieve the concepts I of the domain more specific than Dm

Complete and decidable

Complexity is determined by CiandDj(ExpTime-complete for ALC)

Context representation and reasoning with formal ontologies

slide20
Outline
  • A unified view of context (?)
  • Ontologies for context representation
  • Reasoning with context ontologies
  • Extending ontologies to the fuzzy case
  • Conclusions and future work

Context representation and reasoning with formal ontologies

4 extending ontologies to the fuzzy case
4. Extending ontologies to the fuzzy case

Impreciseknowledge 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

Context representation and reasoning with formal ontologies

4 extending ontologies to the fuzzy case1
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 fALC

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

Context representation and reasoning with formal ontologies

4 extending ontologies to the fuzzy case2
4. Extending ontologies to the fuzzy case

Context representation and reasoning with formal ontologies

4 extending ontologies to the fuzzy case3
4. Extending ontologies to the fuzzy case

Reasoning with the fuzzy CDS pattern

  • Domain knowledge I a-significantin a scenario E
    • Knowledge significant and degree of significance

Complete and decidable

Complexity is determined by Ci, Dj, and the glbs to be calculated

aggregation: min t-norm a ⊗b

greatest lower bound: glb = sup{a : K <t ≥ a>}

slide25
Outline
  • A unified view of context (?)
  • Ontologies for context representation
  • Reasoning with context ontologies
  • Extending ontologies to the fuzzy case
  • Conclusions and future work

Context representation and reasoning with formal ontologies

5 conclusions and future work
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.
  • Privacyissues
  • Study the applicability of full-fledged reasoningin real-world applications
  • Relation with context acquisition and interpretationtechniques
  • Are fuzzy extensions necessary/convenient?

Notice!

Context representation and reasoning with formal ontologies

slide27
Thank you!

Questions, comments?

Context representation and reasoning with formal ontologies