Smart-Context: A Context Ontology for Pervasive Mobile Computing

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  • Smart-Context: A Context Ontology

    for Pervasive Mobile Computing


    Department of Computing, Birmingham City University, Birmingham B42 2SU, UK

    *Corresponding author:

    This paper addresses context in intelligent context-aware systems to support personalised service

    provision and cooperative computing. Context processing, context modelling, ontology, and

    OWL are introduced and a context reasoning ontology presented. Context implementation

    reduces to a decision problem which is characterised as one of selecting from a number of potential

    options based on the relationship between the values that describe the input and the solution, the

    modelling school of decision analysis attempts to construct an explicit model of such relationships,

    usually in the form of decision trees. An overview of decision trees with parametric design consider-

    ations is presented. Comparisons with related research are drawn and an evaluation and simulation

    of Smart-Context is presented. RDF/S with OWL and Jena provide an effective basis for auton-

    omous decision making using processing rules, and the issue is one of implementation in adaptable

    and tractable solutions. A conclusion with open research questions is presented with consideration

    of potential directions for future research.

    Keywords: context; context-awareness; pervasive computing

    Received 21 February 2007; revised 06 July 2007


    This paper addresses the issue of personalised service

    provision and cooperative computing based on defined need

    and situated role, in mobile computing a users situated role

    describes the current situation users experience during their

    interaction with the system. Personalisation requires that an

    individuals profile (termed context) is created. The definition

    of context presents many difficulties including identifying

    individual users, accommodating users evolving preferences,

    device and location sensing and managing the heterogeneous

    nature of mobile and computational devices with their associ-

    ated connectivity constraints and service infrastructure.

    Accommodating these diverse demands requires an overall

    context definition made up of sub-contextsthat describe and

    define entities, an entity being defined in [1] as: a person,

    place or physical or computational object.

    In posing the question mobile, pervasive, ubiquitous or

    wireless? [2], the observation has been made that the term

    mobile wireless communication is often used in conjunction

    with or interchangeably with pervasive computing; however,

    the two are not necessarily synonymous.

    Pervasive computing describes a paradigm whose primary

    characteristics are (generally) ubiquity and invisibility such

    that the opportunity for computation and connectivity are

    constantly available while masking the presence of the

    system from the user [3]. Pervasive computing is addressed

    in [4], the anytime anywhere paradigm being extended to all

    the time everywhere.

    Currently, pervasive computing fails to realise the objectives

    of the pervasive computing paradigm and is limited in its

    capacity to implement the systemic requirements of flexibility,

    autonomy and adaptability. Developments in hardware and

    software have brought pervasive computing close to technical

    and economic viability with the basic component technologies

    all currently existing [4]. The challenges lie in the development

    of intelligent agents implemented in intelligent systems [5] and

    the integration of existing component technologies [4].

    Pervasive computing must address a number of key issues

    including: (1) accommodating a diverse range of applications,

    (2) service provision based on relevance, (3) service provision

    compliant with device constraints and users situated roles,

    and (4) meeting users needs, objectives and beliefs, desires

    and intentions (BDI). BDI represents mental attitudes repre-

    senting, respectively, information, motivational and delibera-

    tive states [6]. Accommodating these requirements demands

    that users are identified based on their context (discussed in

    Section 2.1), to enable pervasive computing to effectively

    address the issues identified.

    THE COMPUTER JOURNAL, Vol. 53 No. 2, 2010

    # The Author 2008. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.For Permissions, please email:

    Advance Access publication on March 4, 2008 doi:10.1093/comjnl/bxm104

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  • This paper introduces Smart-Context, an autonomous intelli-

    gent context-aware pervasive system designed to enable

    context matching between inputs (information, resources or

    queries) and outputs (potential solutions, in this case suitable

    users) based on contextual information. Implementation of

    context matching essentially reduces to a decision problem,

    the modelling school of decision analysis attempts to construct

    an explicit model of the relationships between the input and

    potential solutions, usually in the form of decision trees.

    This paper presents a novel context process model to meet

    the context processing needs with a rule-based semantic

    context reasoning ontology to provide the basis upon which

    effective intelligent context reasoning can be realised. A

    brief illustrative location-based on-line cooperative computing

    scenario is shown to demonstrate the reasoning process.

    Implementation issues are considered with parametric design

    considerations and decision trees are proposed as a potential

    solution to enable effective intelligent context implemen-

    tation. Smart-Context provides the basis upon which effective

    implementation of context in intelligent systems can be


    The remainder of the paper is structured as follows: Section

    2 considers the background to the study with a discussion on

    context, ontologies, the Semantic Web and the Web Ontology

    Language (OWL). Section 3 discusses context modelling, the

    context process model is presented and the application of

    context is addressed with typical examples. Section 4

    addresses Smart-Context, which with the context process

    model forms the core concept of the paper. The context

    factors, their identification and evaluation are discussed with

    the RDF properties, the class structure, and the semantic

    context reasoning ontology. An example of a reasoned scen-

    ario presented. Section 5 considers related research with a

    simulation and evaluation of the Smart-Context concept pre-

    sented in Section 6. Section 7 looks at implementation

    issues and considers decision trees (DT). The paper concludes

    with conclusions and a discussion on future work in Section 8

    and an appendix.


    This Section introduces context with consideration of the

    types of context and the elements that combine to create a

    context definition. Ontologies and the issues that impact

    their use are discussed with examples. There is a brief intro-

    duction to the Semantic Web technologies and consideration

    of the Web Ontology Language (OWL) from a context reason-

    ing perspective.

    2.1. Context

    The concept of context is generally agreed, however, a

    common definition is not [1], considerable confusion

    surrounding the notion of context, its meaning and the role it

    plays in interactive systems [7]. Context is purpose and appli-

    cation specific requiring the identification of the function(s)

    and properties (context factors) specific to each domain [8].

    This is exemplified in the application of context-awareness

    applied to mobile learning [9] where the starting point in the

    definition of context is to identify the purpose of the context

    we are interested in.

    Contexts are variable, the degree of variability being

    reflected in the classification applied to a context. There are

    two general classifications [10]:

    A static context (termed customisation) describes a statein which the look-and-feel and content provision is

    essentially user-driven, the user having an element of


    A dynamic context (termed personalisation) describes astate in which the user is passive, or at least somewhat

    less in control, system functions monitoring analysing

    and reacting to the users situated role [10], actions and

    BDI [6].

    The two types of context are reflected in the two principal

    ways context is used, these are: (1) as a retrieval clue

    (a static context) and (2) to tailor system behaviour to match

    users system usage patterns (a dynamic context).

    A context consists of properties (context factors) that

    describe and define an entity in computer-readable form [8],

    the properties describing any information that can be used to

    characterise an entity [5]. A context definition must define

    and describe a number of elements that combine to create an

    overall context definition, these elements include: (1) spatio-

    temporal, (2) personal, (3) device(s), (4) infrastructure and

    connectivity constraints, and (5) resource(s). A context is

    created by combining property values that describe a diverse

    range of elements [4] [10] [11] [12] including: (1) the variable

    tasks demanded by users, (2) the mobile and computational

    devices, (3) the service infrastructure, (4) the physical situ-

    ation including location, and (5) the social setting.

    There are three primary types of contextual information

    (referred to as context dimensions in [1]): (1) spatio-temporal,

    (2) identity, and (3) activity. Primary contextual information is

    used to derive secondary contextual information [1]. Second-

    ary contextual information relates to property values that

    describe context factors such as location, device character-

    istics and identity information. Contextual information can

    be derived from an array of diverse sources including location,

    weather and traffic sensors, sensors monitoring computer net-

    works and status sensors for human users or computing

    devices in ad hoc networks.

    2.2. Ontologies

    The use of ontology in computer science can be traced to

    Artificial Intelligence (AI) research in the 1960s [13] [14].

    192 P. MOORE et al.

    THE COMPUTER JOURNAL, Vol. 53 No. 2, 2010

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  • A formal dictionary definition of the term ontology1 when

    used in AI is: An explicit formal specification of how to rep-

    resent objects, concepts and other entities that are assumed

    to exist in some area of interest and the relationships that

    hold among them . . . for AI systems, what exists is thatwhich can be represented. Ontology is defined in [15] as: A

    conceptualisation of a domain into a human-understandable

    but machine-readable format consisting of entities, attributes,

    relationships and axioms.

    The ability of ontologies to represent entities has recognised

    benefits; these are the capacity to communicate information

    and the ability to name concepts in machine-readable form

    [15]. There are however disadvantages [16], and these include:

    (i) The potential size and scale of an ontology

    (ii) Ontologies are based on well-defined concepts and

    logic making concept definition difficult

    In considering (i), adding concepts and facts can result in

    ontologies growing very large very quickly resulting in pos-

    sible tractability issues. In considering (ii), humans (generally)

    understand what is meant by different concepts such as

    at_home or at_work. Computationally it is necessary to

    define such concepts using inference rules on an ontology

    which can present difficulties.

    Ontology languages allow users to write explicit formal

    conceptualisations of domain models. The main requirements

    of such models are [17]:

    Well-designed syntax Well-designed semantics Efficient reasoning support Sufficient expressive power Convenience of expressionThere have been a number of ontology languages devel-

    oped, and the most widely known is the Web Ontology

    Language OWL2 (a component in the W3c Semantic Web),

    which is an extension of RDF/S (this is a generalisation as

    there are 3 OWL sub-languagessee Section 2.4). OWL is

    (partially) mapped onto a description logic [17] which is a

    subset of predicate logic for which efficient reasoning

    support is possible [18].

    2.3. The Semantic Web

    The Semantic Web is a concept predicated on the goal of trans-

    forming the world wide web (www) into . . . a set of con-nected applications . . . forming a consistent logical web ofdata . . . [19] realised by adding semantic annotations tothe XML/RDF tagging schema to impart meaning to web

    content [20]. In the Semantic Web information has explicit

    meaning enabling easier automated processing (as opposed

    to merely presenting content to users).

    The Semantic Web functions using Semantic Metadata and

    is predicated on the idea that data can be arranged in the form

    of an ontology and can be viewed in terms of a set of logical

    assertions about specific things and their relationships [17,

    21]. Ontologies establish a common conceptual description

    and a joint terminology between members of communities

    of interest (human and autonomous software agents) [22].

    An example of a semantic ontology constructed to represent

    an animal kingdom built using logical assertions is presented

    in [17].

    The Semantic Web consists of four W3C recommendations:

    The Extensible Markup Language (XML) XML Schema The Resource Description Framework (RDF/S) The Web Ontology Language (OWL)XML, XML Schema and RDF/S3 are well-understood con-

    cepts. OWL is addressed in the following Section.

    2.4. The Web Ontology Language (OWL)

    The expessivity of RDF and RDF Schema is (deliberately)

    very limited [17]. RDF is (generally) limited to binary

    ground predicates, RDF Schema being (again generally)

    limited to subclass and property hierarchy with domain and

    range definitions of these properties [17]. OWL extends the

    expressive power of RDF and RDF Schema, adding additional

    vocabulary enabling constraints including: (1) relationships,

    (2) equality, (3) richer typing of properties, (4) characteristics

    of properties, and (5) enumerated classes. OWL provides three

    sub-languages [17]:

    OWL Full: uses all OWL language primitives enablingtheir use with RDF and RDF Schema. The disadvantage

    is that OWL Full is undecidable, making effective

    reasoning support at best impractical and at worst


    OWL DL (Description Logic): to enable effectivereasoning OWL DL restricts the use of OWL and RDF

    constructors, thus ensuring that the language corresponds

    to a description logic. The disadvantage is that full com-

    patibility with RDF is lost, RDF documents requiring

    extending or restricting before they are legal OWL DL

    documents. Conversely, every OWL DL document is a

    legal RDF document.

    OWL Lite: represents a further restriction in limitingOWL to a subset of the language constructors. OWL

    Lite exclusions include: (1) enumerated classes, (2) dis-

    joint statements, and (3) cardinality. The 3W3c: http://w...


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