context representation and reasoning with formal ontologies juan gómez-romero 1,2, university...

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

Modeling context knowledge with ontologiesContext representationRepresent context information with standard ontologies

Context-based reasoningReduce the knowledge search space according to current context

Extensions to non-classical ontologiesRepresentation 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 ontologies 2Aug, 7th 2011

Outline1. A unified view of context (?)2. Ontologies for context representation3. Reasoning with context ontologies4. Extending ontologies to the fuzzy case5. Conclusions and future work

Aug, 7th 2011 Context representation and reasoning with formal ontologies 3

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



• Set of constraints to a reasoning processSoft: Delimit relevant informationHard: Check consistency of world interpretation

• Influence behavior of the agentAdapt system functioning to the environmentAvoid information overload Augment or embellish system resultsModify acquired data and acquisition procedures

• Cognitive processUse of formal specifications vs. ad hoc specificationsContext is “first-level” knowledge

Characteristics



Nomadic Access to Healthcare InformationA physicist wants to prescribe a treatment for a patientThe 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 lacerationAllergies to procaine should be taken into account

The example can be extended to other Semantic Web scenariosKeyword-indexed documents

Query expansion, query restriction

Data visualizationhttp://ecolexicon.ugr.es/visual/index_en.html (Java required)

Example case


http://ecolexicon.ugr.es/visual/index_en.html

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


2. Ontologies for context representation

Concepts (classes, types)Set of objects with common featuresFOL unary predicates

Instances (individuals)Objects belonging to a classFOL constants

Relations (properties, roles)Binary associations between two instances or an instance and a data type value (integers, strings, etc.)FOL binary predicates

AxiomsRestrictions defining concept, instance and relation featuresFOL formulas

Elements


2. Ontologies for context representationElements


Context vocabulary

Context description

3. Reasoning with context ontologies

Automatic procedure to obtain implicit axioms from explicit axioms

modus ponensAA → BB

Tableaux algorithmsReasoning algorithms for DLsImplemented by inference engines (HermiT, RACER, Pellet)Theoretical efficiency is high, but worst cases are not frequent

Ontology reasoning


Resolution (propositional logic)


Concept axiomsSatisfiability / Consistency

A concept is satisfiable if it is not a contradiction to the remaining axioms

SubsumptionA (super-)concept includes a (sub-)concept

EquivalenceTwo concepts include the same instances

DisjointnessTwo concepts do not have any common instance

Instance axiomsSatisfiability / Consistency

An instance assertion is satisfiable if it is not a contradiction to the remaining axioms

Instance checkingAn instance belongs to a class

EntailmentAn axiom is a logical consequence of a set of axioms

Standard reasoning tasks



Context representation and reasoningExploitation of ontologies in context-aware ubiquitous computing

Interpreting the current user situationUsing contextual knowledge to improve the performance of the system

Contextualization of ontologiesHow external or additional knowledge influences the interpretation of an ontology: consistency, validity, partitioning

Non-monotonic models vs monotonic DLsExtend the OWL language with non-monotonic features

Ontology design patternsRecipes 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



ProposalMeta-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 ALCDirectly translatable into OWL (≈ SHOIN(D))

In several cases, fuzzy knowledge must be consideredExtension of the pattern using fuzzy DLs

CDS pattern



Base ontologiesContext 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,j or Pi,j): A concept linking a complex context Ci and a complex domain Dj

Denotes that Dj is significant in situation Ci

CDS pattern



18Aug, 7th 2011Context representation and reasoning with formal ontologies

Hospital Information System

Emergency Situation Description Model

HIS Abstract Model

Context-Domain Significance Model

Dom

ain

onto

logy

Contextontology

3. Reasoning with context ontologiesReasoning with the CDS pattern


Domain Ontology Context Ontology


CnDm Pn,m

I

EI

Domain knowledge I significant in a scenario E• Algorithm (implemented in the CDS API):

1. Retrieve the complex contexts Cn more general than E

2. Retrieve the s-connections Pn,m involving Cn

3. Retrieve the complex domains Dm involved in Pn,m

4. Retrieve the concepts I of the domain more specific than Dm

Complete and decidableComplexity is determined by Ci and Dj (EXPTIME-complete for ALC)

4. Extending ontologies to the fuzzy case

Imprecise knowledge cannot be representedE.g.: A patient is slightly unconscious

Partial similarities between contexts cannot be representedE.g.: Anaphylaxis is quite similar to sepsis

Relevance relations cannot hold to a degreeE.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



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 fALCFuzzy 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



23Aug, 7th 2011Context representation and reasoning with formal ontologies

Hospital Information System

Emergency Situation Description Model

HIS Abstract Model


4. Extending ontologies to the fuzzy caseReasoning with the fuzzy CDS pattern

24

Domain knowledge I a-significant in a scenario EKnowledge significant and degree of significance

Domain Ontology Context Ontology


Cn

E

Dm

I

Pn,m

I

knl

k,l

i,j

aggregation: min t-norm a ⊗ b

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

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

p

m

5. Conclusions and future work

Advantages of using ontologies to manage context knowledgeExpressivenessFormal representation and reasoningStandard languages and toolsAppropriate to deal with information overloadExtensions 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!


Thank you!

Questions, comments?


context representation and reasoning with formal ontologies juan gómez-romero 1,2, university...

Documents

context ontologies

context information

specifications context

context schmidt

unified view of context

modeling context knowledge

current context extensions

context nomadic access