How Can Semantics and Context Awareness Enhance the Composition of

Context-aware Services?

Tarik Fissaa

, Hatim Guermah

, Hatim Haﬁddi

1,2

and Mahmoud Nassar

IMS TEAM, SIME Lab, ENSIAS, Mohammed V Rabat University, BP 713, Agdal Rabat, Morocco

ISL TEAM, STRS Lab, INPT, Rabat, Morocco

Keywords:

Ubiquitous Computing, Semantic Context aware service, Ontology, Service Composition, AI Planning.

Abstract:

The context-aware services refers to applications that use so-called contextual information to provide appro-

priate services or relevant information to the user or other applications to perform a speciﬁc task. An important

challenge in context-aware service oriented systems is the creation of a new service on demand to carry out

more complex tasks through the composition of existing services. In this work, we aim to propose a seman-

tic based architecture for the development of context aware services composition using Artiﬁcial Intelligence

(AI) planning. The straightforward translation between AI planning through PDDL and Semantic web services

via OWL-S allows to automate the composition process. Thus planning based service composition launches a

goal-oriented composition procedure to generate a plan of composite service corresponding to the user request.

1 INTRODUCTION

The pervasive computing vision introduced by Mark

Weiser in the early 90’s, results from the conver-

gence of powerful, small, affordable computing de-

vices with networking technologies that tie them all

together. Thus, it has brought about a new gener-

ation of service oriented architectures, based on the

Context-Aware Service (CAS) paradigm. Context

driven development of service oriented systems en-

ables them to be context-aware and consequently to

provide users with customized and personalized be-

haviors depending on their contexts.

One of the core principles of Service oriented

computing (SOC) is the idea of assembling services

to form a chain by discovering and dynamically

composing those multiple existing services to sat-

isfy a user task rather than building new applica-

tions from ’scratch’. Automation of this process is

emerging as one of the most interesting challenges

facing SOC today. In order to be context-aware,

composite services need to follow some requirements

in order to resolve the challenges brought by the

context-awareness paradigm, thus the composed ser-

vice should be built in automatic and dynamic way

depending on the context of use.

The manual composition of services is complex

and susceptible to errors because of the dynamic be-

havior and ﬂexibility of the context aware services.

AI planning technologies has proven to be useful for

the automation of services composition. By treat-

ing services as an action, planners do various sorts of

reasoning about how to combine services into a plan

responding to the user goal. AI planning based al-

gorithms try to ﬁnd a feasible composition solution

through search of possible services to accomplish a

speciﬁc task.

For the above reasons, CAS development can

beneﬁt from semantic web services, Model Driven

Engineering (MDE) via ODM (Object Management

Group, 2009) and AI planning techniques. Model-

Driven Engineering is a software design and devel-

opment approach that strongly focuses on models

and pretends that these models can be reﬁned and

ﬁnally transformed into a technical implementation.

The purpose of Semantic Web Services(Martin et al.,

2007) is to use semantic speciﬁcation to automate the

discovery, invocation, and composition Web services.

The Ontology Web Language for services (OWL-S) is

the most direct outcome for describing Web services

in the semantic web.

In this work, we aim to propose in ﬁrst stage an

overview of an architecture for the automation of con-

text aware services composition. Our major contribu-

tion in this paper is the proposition of an extension

to OWL-S with context elements to take advantage

of the context awareness, and especially we propose

a tool for automatic services composition using AI

640

Fissaa T., Guermah H., Haﬁddi H. and Nassar M..

How Can Semantics and Context Awareness Enhance the Composition of Context-aware Services?.

DOI: 10.5220/0005381706400647

In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 640-647

ISBN: 978-989-758-097-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

planning as recommanded in semantic web services

OWL-S speciﬁcations (Martin et al., 2007).

The remainder of this paper is structured as fol-

lows. Next we present a motivation scenario that con-

cerns context-aware E-health system. In section 3 we

present some deﬁnitions and background. In section

4, we give an overview of the proposed architecture,

section 5 presents a brief descriptions of our Context

Modeler. In section 6 we present our proposal for se-

mantic CAS by extending OWL-S. Section 7 presents

some related work in context aware service compos-

tion approachs. The ﬁnal section gives rise to some

concluding remarks and plans for future works.

2 E-HEALTH MOTIVATING

SCENARIO

In this scenario, the underlying idea is to allow elderly

people, chronic patients to stay at home and to bene-

ﬁt from a remote and automated medical supervision.

Let’s take the case of a patient with a cardiac arrhyth-

mia. The patient’s situation requires monitoring to

detect crises or problems that may be caused by the

increase or decrease in his heart rate. Thus in the case

of a problem the patient should be informed of a criti-

cal condition, and take the necessary precautions. The

role of the context-aware system is to ﬁnd the near-

est caregiver (nurse or available physician) to help the

patient depending on the severity of his condition. In

parallel, depending on the current situation of the pa-

tient, the system must ﬁnd an ambulance to transport

the patient to the hospital. The aim is to provide the

patient with both higher levels of security and inde-

pendence that allows him to live a normal life despite

his illness. This e-health scenario highlights the fun-

damental challenges for the composition of context-

aware services. Having regard to the need to use the

patient context, the e-health system should be context

aware. Therefore, different types of services can be

deﬁned (see Figure 1).

Figure 1: E-health Scenario.

3 BACKGROUND

3.1 Context

The notion of ’context’ is vague to deﬁne, many re-

searchers working on context had given a variety

of different deﬁnitions. However, a universally ac-

cepted deﬁnition is yet to be agreed. Schilit (Schilit

et al., 1994) deﬁned context to be: location, identi-

ties of nearby people, objects and changes to these

objects. Other deﬁnitions had been proposed, Brezil-

lon (Br

ezillon, 2003) deﬁne context as an information

that characterizes the interactions between humans,

applications and the environment. Dey et al.(Dey and

Abowd, 2000) discuss that the important aspects of

context cannot be enumerated, as they differ from sit-

uation to situation and depend on the purpose of the

application, furthermore they formally deﬁned con-

text as:”... any information that can be used to char-

acterize the situation of an entity. An entity is a per-

son, place or object that is considered relevant to the

interaction between a user and an application, in-

cluding the user and applications themselves.” In our

work we will adopt this deﬁnition because it remains

the most generic.

3.2 Context Awareness

Having deﬁned the concept of context, we still need

to deﬁne the usage of context through context aware-

ness, context awareness refers to the ability of an ap-

plication to discover and take advantage of contextual

information, such as user location and nearby devices.

Shilit (Schilit et al., 1994) was the ﬁrst to introduce

the concept of context-awareness and have deﬁned it

as the ability of an application to discover and react to

changes according to the user’s environment. Brown

(Brown et al., 1997) deﬁnes it as applications whose

behavior can change depending on the user’s context.

Dey(Dey and Abowd, 2000) considers an application

as context aware if it uses contextual information to

provide relevant information and services to the user,

where relevance depends on the user’s task.

3.3 AI Planning and PDDL

Planning is a problem solving technique, where

knowledge about available actions and their conse-

quences is used to identify a sequence of actions,

which, when applied in a given initial state, satisfy

a desired goal. In general, a planning problem has the

following components:

• A description of the possible actions which may

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641

be executed (a domain theory) in some formal lan-

guage.

• A description of the initial state of the world

• A description of the desired goal

Thus Service composition can be modeled as a AI

planning problem. Based on the initial state, the de-

sired goal and context information, planning-based

service composition launches a goal-oriented compo-

sition procedure to generate a plan with respect to

users context.

PDDL: The Planning Domain Deﬁnition Lan-

guage (PDDL) proposed by (Mcdermott et al., 1998)

is a standard encoding language for planning tasks.

The PDDL is inspired by well-known STRIPS (Stan-

ford Research Institute Planning System) (Fikes and

Nilsson, 1972) and it is an action-centered language

as well as STRIPS. It uses precondition and post-

condition to describe the applicability and effects of

actions. Despite some argument in the some features

of PDDL, the language has been wildly accepted in

AI community, since it standardizes the domain de-

scription and the problem description in planning re-

search. Then we can compare the AI planner and

share planning resources. The introduction of PDDL

has facilitated the scientiﬁc development of planning.

Planning tasks speciﬁed in PDDL are separated

into two ﬁles:

• A domain ﬁle: composed of the predicates which

are used to describe the knowledge in the world

and the actions which can change the world states.

• A problem ﬁle: The problem ﬁle includes objects

in the world, initial state and goal speciﬁcation.

4 ARCHITECTURE OVERVIEW

Our approach for context-aware service Composition

is based on an integrated environment based on the

synergie between Semantic Web Ontology and Con-

text Awareness. Figure 2 presents an architectural

overview of all the system’s component aimed at pro-

viding personalized services according to the user

context. There are the main components that we dis-

tinguish in the architecture:

• Context Ontology Modeler: it deﬁnes the capture

phase and representation of context. It is com-

posed of two components.

– A model of context in speciﬁc domain (e-health

in our case), conformity with the proposed

metamodel. This metamodel is deﬁned in a

generic and abstract way to be used indepen-

dently from the application area.

– OWL context ontology generated from a model

of context using models transformation based

on ODM the new OMG standard dedicated to

ontology.

• Discovery and Reasoning: allows reasoning and

deducing new situations from OWL ontology of

context and inference rules that well help in deﬁn-

ing the necessary adaptations in the ﬁnal compos-

ite service. The discovery is the process of ﬁnding

appropriate services semantically to avoid prob-

lems occured in syntactical matching. Let’s men-

tion that we don’t deal in this work with discov-

ery and reasoning our focus is on the composition

phase.

• Composition Planner: To build context aware ser-

vices, we need to deﬁne mechanisms for the com-

position of appropriate services and to adapt their

behavior according to the current context. Such

mechanisms will favorite loosely coupling be-

tween the core service and its adaptations seen as

transversal preoccupations. The adaptations are

eventually conditioned by the existence of rele-

vant situations to the current context. Therefore

following a users request, and a set of services

described as actions, a planner would ﬁnd a col-

lection of services automatically that achieves the

request to build an adapted composite service.

5 CONTEXT ONTOLOGY

MODELER

To model the context in generic and abstract way, we

propose a metamodel that deﬁnes the context and its

sub context, context property, validity, and speciﬁca-

tions of each context property (see Figure 3). This

metamodel is based on the following speciﬁcations:

• A context decomposes into sub contexts.

• A sub context can be, recursively, decomposed

into categories for its structuring.

• A context, a sub context and a category are con-

stituted of context properties.

• A context property is gathered by sensors:

SensedCtxProperty, or derived from other context

properties: DerivedCtxProperty, or stored in the

database: StoredCtxProperty.

• Each property has a context validity.

• A derivedCtxProperty is obtained by derivation

from a set of properties based on derivationFunc-

tion.

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Figure 2: Overview of the proposed architecture.

Figure 3: Context metamodel.

• A StoredCtxProperty is obtained through the re-

covery features of stored properties (e.g. Recov-

eryFunction).

• A sensedCxtProperty is obtained and character-

ized by the context source SourceCxtProperty and

the acceptance value of context property Quality-

ofProperty.

• Each sub context has a speciﬁc type of access.

To illustrate our metamodel, let’s project it on the case

of ﬁgure of the E-health system. The context for this

system in particular and context-aware computing in

general is composed mainly of the following sub con-

texts:

• environment: represent user’s location,time...etc.

• Medical Information: contains user’s health prop-

erties (in our case Blood Sugar, HRV, and Blood

pressure).

• User: contains properties describing the user

(Blood groupe, age, preference).

• Device: contains parameters that describe the en-

tity Device (e.g. medicals device, mobile phones,

PDA...etc).

5.1 Context Metamodel Transformation

to OWL

Models transformations provide a mechanism for au-

tomatically creating or updating target models based

on information contained in existing source models.

Formally, a simple model transformation has to de-

ﬁne the way for generating a model Mb, conforming

to a metamodel MMb, from a model Ma conforming

to a metamodel MMa.In our case, the source meta-

model corresponds to a view from our context meta-

model and the target metamodel corresponds to the

OWL metamodel.

As a result we obtain an OWL ontology based on

our context model, the resulting ontology consists of

a set of classes, individuals, properties and relations

that describe the various context properties (see Fig-

ure 4). This OWL ontology can be used for reasoning,

discovering and composing.

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Figure 4: excerpt of the E-health ontology.

6 CONTEXT AWARE SEMANTIC

COMPOSER

6.1 Context Aware Semantic Services

OWL-S is an ontology for the description of seman-

tic Web services expressed in the Web Ontology Lan-

guage (OWL). OWL-S deﬁnes an upper ontology

for semantically describing Web services along three

main aspects: The Service Proﬁle describes what the

service does in terms of inputs, outputs, preconditions

and effects (IOPEs). The Service Model describes

how a service works in terms of a process model

that may describe a complex behaviour over under-

lying services. The Service Grounding describes how

the service can be accessed, usually by grounding to

WSDL. But to take advantage of context-awareness,

it’s important to have an efﬁcient mechanism to adapt

services (composite or single ones) according to the

context which is not supported by the current OWL-S.

Therefore we propose an extension to OWL-S called

OWL-SC (C stand for context) based on context ele-

ments to detect the necessary adaptations. Figure 5 il-

lustrate our Semantic Context Aware Service (SCAS).

The SCAS is based on the following speciﬁcations:

• The SCAS has a ServiceModele, ServiceProﬁle

and ServiceGrounding.

• The service model can be viewed as a process.

• The Process containts AtomicProcess or Compos-

iteProcess.

• The ServiceProﬁle is related to a context Property.

• Each ContextProperty contains Contextual At-

tributes: Attribute.

• Each AtomicProcess contains an Adaptation.

• For a given AtomicProcess a set of Adaptation-

Rule is associated.

• An AdaptationRule can involve the execution of

an ordered set of Adaptations.

• An Adaptation is related to a relevent context

property.

6.2 The Composition Phase

To automate the composition of context-aware ser-

vices, we have to resort a strategy for composing

and adapting context aware services. Figure 6 shows

the process of composing our semantic context aware

services using AI planning. The relevant extended

OWL-S services description is acquired by semantic

services discovery. The key features of the composer

are:

• Translate the Web services description of domain

and problem written in OWL-S to the planning

problems described in PDDL.

• Input the result generated by the converting pro-

cess into AI planner.

• Translate back the actions sequence that AI plan-

ner got in the precedent step into a composite Web

service.

6.2.1 Translation OWL-SC to PDDL

The purpose of the OWLS2PDDL converter is to

translate a given OWL-DL expression in OWL-S ser-

vice descriptions and a given service composition

problem into an equivalent PDDL planning problem

which can be understood by AI planners. Due to

space limitations, we only describe the essential trans-

lation process: an operator of the planning domain

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Figure 5: Semantic context aware service by extending OWL-S.

corresponds to a service proﬁle in OWL-S: both oper-

ator and proﬁle describe a pattern of how an action or

web service as an instance should look like. A method

is a special type of operator, that allows the user to

describe composed web services. Any OWL-S ser-

vice proﬁle input parameter correlates with an equally

named one of an PDDL action, and the hasPrecondi-

tion service parameter can directly be transformed to

the precondition of the action by use of predicates.

The same holds for the hasEffect condition parame-

ter. If the service exist then it’s translation is part of

the planning domain generation and if the service is

requested (goal) then it’s part of the problem descrip-

tion.

6.2.2 Planning

Planning focuses on selecting suitable actions and

ordering them in an appropriate sequence so as to

achieve some goal automatically. In general a clas-

sical AI planning problem can be described as a quin-

tuple < S, S

, G, A, Γ >, where:

• S is the set of all possible states of the world.

• S

⊂ S denotes the initial state of the world.

• G ⊂ S denotes the goal state of the world the plan-

ning system attempts to reach.

• A is the set of actions the planner can perform in

attempt to reach a desire goal.

• The translation relation Γ ⊆ SxAxS deﬁnes the

precondition and effects for the execution of each

action.

In terms of Web services S

and G represent the initial

state and the goal state respectively, speciﬁed by the

service requestors. A is a set of available services and

Γ denotes the current states of each service.

So far we have presented available Web Services

and initial and goal state of the problem domain in

PDDL. The domain and problem ﬁles can be sent to

any PDDL based planner to generate a valid compo-

sition plan. The generated plan provides the sequence

of the context aware service composition.

6.2.3 Translation PDDL to OWL-SC

After the acquisition of solutions, a reverse transla-

tion process has to take place, in order to provide the

resulting composite web service to the original OWL-

S standard and the initial web services domain. This

reverse translation accommodates composite service

deployment and execution monitoring.

6.2.4 Adaptation Tool

The adaptations are eventually conditioned by the ex-

istence of relevant situations to the current context.

Thus, the composite service must be able in our case

to dynamically adapt its behavior to different context

changes. It consists in weaving the required Adapta-

tion Aspects, following a set of AdaptationRules into

the core service to produce the corresponding Context

aware service.

6.3 Running Example

Let’s take the case when the system detect a critical

condition that require a medical intervention. The pa-

tient receives an alarm service and recommendations

provided by the system and the physician and a trans-

portation service is triggred taken into account the lo-

calisation and the nearest hospital. This case require

a context driven composition. Figure 7 depicts an ex-

ample of a plan generated in this cas.

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Figure 6: Semantic Composition Tool.

Figure 7: Generated plan.

7 RELATED WORK

Context modeling is of vital importance for Context-

aware applications developers and architects, since

it provides dynamic service behaviors, content adap-

tation and pertinent usage for end-users. For the

above reasons, Strang and Linnhoff-Popien (Strang

and Linnhoff-Popien, 2004) present a survey of

six context modeling approaches: Key-value mod-

eling(Schilit et al., 1994), markup scheme model-

ing (Nilsson et al., 2000), object oriented modeling

(Schmidt and Van Laerhoven, 2001), graphical mod-

eling (Henricksen et al., 2003), logic based modeling

(Ghidini and Giunchiglia, 2001) and ontology based

modeling approaches. Their analysis favors ontology

based context modeling.

SHOP-2 (Sirin et al., 2004) uses services descrip-

tions in DAML-S, the predecessor of OWL-S, and

performs Hierarchical Task Network (HTN) planning

to solve the problem. The main disadvantage of this

approach lies in the fact that the planning process, due

to its hierarchical nature, requires the speciﬁcation of

certain decomposition rules.

OWLS-XPlan (Klusch et al., 2005) uses the se-

mantic descriptions of atomic web services in OWL-

S to derive planning domains and problems, and in-

vokes a planning module called XPlan to generate

the composite services. The system is PDDL com-

pliant, as the authors have developed an XML dialect

of PDDL called PDDXML. Although the system im-

ports semantic descriptions, the planning module re-

quires exact matching for service inputs and outputs.

Authors in(Hatzi et al., 2013) presents PORSCE

II, an integrated system that performs automatic se-

mantic web service composition exploiting AI plan-

ning. The main advantages of the proposed frame-

work the extended utilization of semantic informa-

tion, in order to perform planning under semantic

awareness and relaxation. In our point of view this is

an interesting approach to deal with semantic compo-

sition however the context dimension is ignored and

not taken into account.

The presented approaches don’t take into account

the notion of context awareness.

Authors in (Li et al., 2011) present an approach

to support context-aware semantic service composi-

tion, by weaving context aspects deﬁned by means of

ontology concepts, within plan compositions. Weav-

ing is performed statically, before starting the execu-

tion of the main service. However, they don’t deal

with context modeling and reasoning also automatic

composition is not considered. Authors in (Mrissa

et al., 2007) trie to solve IO compatibility issues be-

tween services within a composition of services by

integrating the notion of context and service media-

tor. This concept of context they use does not take

into account the underlying deﬁnitions of the context

relative to ubiquitous environments and the fact to in-

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646

sert a mediator service within each pair of services in

the composition process may, from our point of view,

signiﬁcantly increase response time and system per-

formance.

The main advantage of our approach is the use of

metamodel and a model transformation into an on-

tology to facilitate the context modeling phase, thus

we propose a synergy beetwen context awareness and

semantics in order to enhance and automate the com-

position of services in ubiquitous environment.

8 CONCLUSIONS

In this paper we present a semantic based architecture

for the composition of context aware services.Thus,

following MDE speciﬁcations we propose an ontol-

ogy based context modeler in order to give support to

translation from context models into an OWL context

ontology. Next step is reasoning and discovering ser-

vices to extract high level informations. Finally, our

main proposition is a semantic context-aware service,

represented by extending OWL-S. Therefore services

will be used for composition by our AI planning based

tool in order to generate a composite context aware

service.

We project to provide an applicative layer of our

tool for service composition in order to automate the

whole process of the composition. We also plan

to evaluate more composition mechanisms such as

heuristics and other problem solving algorithms.

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