=Paper=
{{Paper
|id=Vol-242/paper-1
|storemode=property
|title=Context-aware Applications with Distributed Ontologies
|pdfUrl=https://ceur-ws.org/Vol-242/paper1.pdf
|volume=Vol-242
|authors=Flavio De Paoli,Marco Loregian
|dblpUrl=https://dblp.org/rec/conf/caise/PaoliL06
}}
==Context-aware Applications with Distributed Ontologies==
https://ceur-ws.org/Vol-242/paper1.pdf
UMICS'06 869
Context-aware Applications with Distributed Ontologies
Flavio De Paoli and Marco Loregian
Dipartimento di Informatica, Sistemistica e Comunicazione,
Università degli Studi di Milano Bicocca,
via Bicocca degli Arcimboldi 8,
20126 Milano (Italy),
{loregian,depaoli}@disco.unimib.it
Abstract. This paper presents a framework for context-aware applications, with
a particular focus on collaboration and pervasiveness. The framework relies on
distributed ontologies, which are shared components spread over a network. The
architecture of the framework provides for the coexistence of devices with dif-
ferent nature and computing capabilities. The framework implements a peer-to-
peer model, encompassing three kinds of peer: ontology-management, context-
management, and interaction-management peers. The problem of supporting co-
operative work practices in heterogeneous, pervasive computing environments is
tackled, in particular with respect to the case of hospital settings.
1 Introduction
Context awareness is a central topic for many research communities. In a general sense,
context information concerns “the location, identity and state of people, groups and
computational and physical objects” [1]. The definition given by Abowd et al. focuses
on the relation between users and applications. This is a particularly relevant aspect in
pervasive computing environments where actors (either human or technological) mu-
tually influence their behaviors. In other research areas, such as computer supported
cooperative work (CSCW) and human-computer interaction (HCI), the notion of con-
text includes other dimensions, such as user’s perceptions and experience [2].
Context-aware systems are requested to extract, interpret and use context informa-
tion to adapt their behavior. The first two topics, contextual knowledge extraction and
interpretation, are highly regarded in logics, knowledge representation (KR) and artifi-
cial intelligence (AI) research, whereas adaptive systems are widely treated in the areas
of knowledge management (KM), distributed systems engineering (DSE), multi-agent
systems (MAS), pervasive and ubiquitous computing. This work mostly focuses on the
last issue (adaptation), with minor references to the first two.
Moreover, the problem of context awareness can refer to different levels of abstrac-
tion, from adaptation of system configuration to device characteristics (i.e., presence of
other entities and their characteristics), to access control to knowledge bases according
to environmental features and occurring events. At every level, interoperability among
actors requires a shared perception of the environment: ontologies provide the neces-
sary elements to describe environments and contexts in human-readable and machine-
processable fashion [3]. Ontologies are acknowledged to be useful and powerful tools
870 Ubiquitous Mobile Information and Collaboration Systems
in knowledge management systems, while their application in lightweight pervasive
applications is still matter for advanced research.
The CADO (Context-aware Applications with Distributed Ontologies) framework
has been designed with a twofold aim: (i) to support dynamic configuration of pervasive
computing environments (in a lightweight fashion) and (ii) promote rich, semantic-
enriched interaction for knowledge sharing in collaborative systems.
CADO relies on distributed ontologies (Section 2) that are shared and managed in a
peer-to-peer fashion. The goal is to provide for a comprehensive and rich middleware to
address collaborative multi-channel applications whose participants can join and leave
dynamically, maintain and share their ontologies and occasionally form clusters to es-
tablish a conversation. The architecture of the framework is designed to cope with per-
vasive computing environments that are characterized by devices with different nature
and computing capabilities: for example, we are testing the use of digital pens, which
are simple input devices, as well as large screens, that allow for richer interaction, tablet
PCs, which are powerful portable devices, mobile phones, and so on. The framework is
composed of three layers and designed to support mobility of workers in complex work
settings. The three layers ensure semantic, context and application interoperability.
The next section presents the motivation of this work, rooting in earlier and ongo-
ing projects, and the definition of a model of the problem. Section 3 explains with a
simple scenario how the framework can be applied. Section 4 describes in details the
CADO framework for pervasive computing. Section 5 provides details about knowl-
edge sharing, and ontology merging in particular, that is the mechanisms upon which
the framework relies. Some related works are referred in Section 6 and then some con-
cluding remarks are given.
2 Background and motivation
The work presented in this paper has been developed within the SWIRLS project (“Sup-
porting Wards with Interactive Resources and Logic-based Systems”, [4]), and relying
on ATELIER and MILK projects experience.
The SWIRLS project started in 2004 in collaboration with an Italian hospital, aim-
ing at supporting hospital practitioners by means of digitally enhanced artifacts with-
out disrupting habitual work practices. Hospitals consist of several different work sites
(e.g., inpatient rooms, laboratories) that are grouped in departments according to orga-
nizational constraints and specific practices. The analyzed environment is also charac-
terized by a high degree of mobility and heterogeneity of actors.
Mobility in clinical work has been thoroughly analyzed by Bardram and Bossen [5]:
from an “infrastructural” perspective, mobility is driven by the necessity of being in dif-
ferent physical places, e.g., to visit inpatients in their rooms and use specific equipments
in laboratories. Workers need to access shared resources, which can be either perma-
nently located in specific rooms or can be moved towards the patients. From a social
perspective, people move to meet other people that retain useful knowledge to accom-
plish a certain task. People need to contact specific persons, for example specialists in
specific fields. As a consequence, each actor plays different roles (providing or looking
for knowledge) in different situations and different places. For these reasons, digital
UMICS'06 871
devices that surround people and are carried along by workers should exhibit different
behaviors according to the current situation and, if they are used to access some content,
information they present should be modulated according to the context [4], for example
according to privacy policies (Section 3).
Experiments in this field have been carried on in the previous project ATELIER
(“Architecture and Technologies for Inspirational Learning Environments” [6]). The
goal of ATELIER was to enhance the learning experience of architecture and interac-
tion design students. The ATELIER setting was a single room, where a group of students
collaborated on the development of a project (e.g., the design of a building or an arti-
fact), and the adopted software infrastructure was centralized. The main problem was
to enact flexible manipulation policies on a central ontology collecting the knowledge
retained by a whole class of students. In the SWIRLS project some similar manipula-
tion of shared knowledge is needed, in the form of documents or personal information
to be accessed and shared: it happens in a larger setting, more dynamic and technologi-
cally heterogeneous. A distributed infrastructure encompassing mechanisms to quickly
enable the set-up of local contexts (described by ontologies) is required.
In the MILK project (“Multimedia Interaction for learning and Knowing”, [7]), a
centralized framework supporting interactions within three different environments (of-
fice, by means of PCs, social, by means of large interactive screens, and mobile, by
means of cellular phones) has been developed. Ontologies are exploited to capture dif-
ferent kinds of knowledge (e.g., domain, organizational) to support semantic document
management and supply richer presentation and navigation solutions. Information on
people, communities, projects, and documents is visualized and organized by means of
semantic links according to the current situation. Multi-channel communication has
been supported for communities. The CADO framework, exploiting the experience
gained in the MILK project, represents a further step towards the support of larger,
and more dispersed workgroups.
In these projects, ontology dynamics is driven by innovation and discoveries, but
while processes of conflict solving and consensus reaching over a shared resource are
enacted quite naturally in centralized environments, the propagation of knowledge (i.e.,
changes in the conceptualization) in a widespread distributed environment requires tai-
lored strategies and models for reaching shared agreements.
A key point is that CADO is targeted to support organizations and it does not aim at
forming a global consensus. Alignment is a continuous process of mutual agreement in
response to divergence (of information, opinions, and practices) among participants. In
other words, in the SWIRLS project, by means of a CADO-based infrastructure, con-
text and knowledge representations are local and mutually aligned for groups of actors
interacting in a peer-to-peer fashion. From this process stems the reference ontology for
each group, community, and environment.
CADO users and components rely on ontologies covering different aspects of the
domains of interest, and such aspects can sometimes be disjointed (e.g., technological
components of the system, desired behaviors and user expertise).
A topic ontology is the part of an ontology that covers a particular aspect of the
domain, collecting a set of concepts connected with a is-a relation and with other
domain-specific relations (later referred to as intra-topic relations). Since knowledge
872 Ubiquitous Mobile Information and Collaboration Systems
is manifold, users exploit different topic ontologies to cover their entire knowledge and
expertise. A collective vision over a topic within a group of individuals, such as a work
team or a community, can only be achieved by merging (different) topic ontologies.
Ontology merging is the “creation of [a] single coherent ontology that includes the in-
formation from all the sources” [8]. In CADO only virtual merging is employed, in that
no physical ontology is produced by merging topic ontologies, but only a temporary
view over different sources seen as a whole.
A distributed ontology can be built from individual representations: “[a distributed]
ontology is divided into several component ontologies and [. . . ] each ontology [is con-
structed] individually (perhaps in parallel) by different developers in a distributed envi-
ronment” [9]. In other words, distributed ontologies are modular and composed of peer
components spread over a network.
In the following section a simple scenario is used to explain how the framework can
be applied and to which extent knowledge sharing is facilitated. Ontologies are used to
describe both human (domain) and technological components, as well as some descrip-
tion of desired behaviors and objectives, expressed in terms of operational constraints.
Concepts and relations are emphasized using a different text style.
3 Scenario
In a healthcare scenario, when physicians gather in a room or in a laboratory to dis-
cuss and work together their individual knowledge has to be integrated for the scope of
the meeting. This means that each of them concurs in the creation of a broader knowl-
edge base with the knowledge stored in local (e.g., laptops) and remote resources (e.g.,
digital libraries, DBs, etc.). This knowledge base is then accessed by the various de-
vices available to users: either personal, such as PDAs, or shared, such as wall-mounted
displays.
In such a case, since actors refer to the same domain (i.e., healthcare), it is reason-
able to assume that there is a substantial lexical and conceptual overlapping between
actors, that can form a common background to support interaction, that makes the so-
lution of ambiguities simpler [8, 10]. This assumption has been exploited in CADO to
facilitate semantic interoperability (Section 5.1).
The shared knowledge base is composed of the documents every participant wants
to share and the relations defined in a common ontology that represents the merge of
the expertise of the group members. Documents can be passed along participants and
personal ontologies can be enriched by collecting information from other participants to
facilitate human-to-human interaction and enrich the sharing experience by supporting
knowledge discovery, at least for the span of a conversation.
From a technological perspective, the integration of devices with different charac-
teristics is achieved by referring to a common model of the context that also encom-
passes domain-dependent behavioral rules. Device capabilities and relations with other
elements (devices, documents, users, etc.) are included in a specific topic ontology.
Other implicit relations emerge by merging ontology modules and by checking explicit
domain constraints (axioms, Fig. 2).
UMICS'06 873
The context of the following example is given by a SharedRoom hosting some
Devices. The scenario involves ontologies of different topics: technological aspects,
organizational roles and structures, type of documents and medical contents.
In a SharedRoom of an Hospital, where medical Consultations usually
take place, there are different kinds of Screens, some are large and public (e.g.,
Whiteboards), others are small and private (e.g., PDAs). Employees, Doctors
or Nurses, are sensed by RFID technology to discover when they are in the room.
Documents can be displayed on shared Whiteboards, but if two or more people are
present in the room, only public documents (e.g., Notices or Articles) can be dis-
played while personal Documents (e.g., Emails, personal Notes) have to be hidden
from others’ sight.
Medical Domain (Content) Technological (Device) Ontology
Ontology Context
Molecule
Technological
Constraints
Device
has-Part Hospital Situation
Inorganic Organic Output Portable
Molecule Molecule Ident. Shared Consultation Legend:
Device Device
Device Room
Lipid Protein has-
Screen Part PDA
Environment
Axioms
is-a
works-at relation
Apoliprotein has-Part
Glyceride A-V Whiteboard Laptop RFID is-populated-by works-at
Topic Ontology
identifies
has-Influence-On Context Ontology
is-About is-Author-Of
Axioms
Organization
Triglyceride Document Employee Axioms
cause Nurse
Patient Article Notice Email Note Doctor
Pancreatitis
Record
Domain Oncologist Genetist
Axioms
Document Classification Ontology Organization Role Ontology
Fig. 1. The (simplified) distributed ontology underlying the example and showing the relations
between concepts.
Dealing with ontologies, attributes and relations associated with concepts and ax-
ioms supply the context manager with information on specific situations. So for exam-
ple, a Document has a boolean attribute (slot) that states whether a document is public
or private, and RFID has-Influence-On Employee, since the RFID carried with a
badge influences the behavior of the sensed employee. Axioms define the system con-
straints. For example, the constraint in Fig. 2 states that only public documents can be
displayed on large screens if more than one person is present in the room.
A deeper insight of the interaction and reasoning mechanisms can be achieved by
the following scenario, which is a realistic example even if not referred to any real
hospital situation.
Dr. Dorian, an Hematologist, is evaluating the anomalous production of
Lipids in the blood of a patient, but he is not a specialist on the particu-
lar subject of such diseases. He is then reading on the Whiteboard some
Articles, checking the PatientRecord and jotting some Notes on the
screen about how to interpret the data. Not being able to solve the problem, Dr.
Dorian calls for help Dr. Reid, a senior Genetist who has just participated
in the last International Conference on Very Rare Genetic Diseases. When
874 Ubiquitous Mobile Information and Collaboration Systems
(defrange ?screen :FRAME Whiteboard)
(defrange ?room :FRAME SharedRoom)
(defrange ?doc :FRAME Document)
(forall ?room
(forall ?screen
(=>
(and
(Screens ?room ?screen)
(>(number-of-slot-values People ?room) 1))
(forall ?doc
(and
(Display ?screen ?doc)
(not(private ?doc))
)))))
Fig. 2. Sample axiom, written in Protégé Axiom Language (PAL) stating that when more than
one person is in the room, a whiteboard, which is a particular wall-mounted screen, should not
display personal documents. Private documents have an attribute called private set to true.
Dr. Reid enters the SharedRoom with her own RFID badge and Laptop,
the system detects the new presence and hides from the Whiteboard the
PatientRecord and the private Notes of the Hematologist, to pre-
vent Dr. Reid from reading them. This situation has been recognized by the
system as a Consultation, as the result of an axiom in the ontology that
defines Consultation as a situation with two or more Doctors nearby
a Screen in a SharedRoom. The Documents stored in the Genetist’s
Laptop are organized according to a highly specialized ontology on the topic,
including detailed information (i.e., a very fine-grained taxonomy and sev-
eral specific relations among concepts) on genetic diseases and of the kinds
of involved Molecules. Moreover, Dr. Reid owns an electronic version of
the proceedings of the conference she attended. The partial knowledge (on-
tology) Dr. Dorian was relying on can be now completed with the richer on-
tology brought in by Dr. Reid. The Whiteboard then displays Documents
related to the Articles Dr. Dorian was reading as a relation between the
two doctors’ knowledge is created. For example, a has-Influence-On relation
between Apoliprotein A-V and Triglyceride is discovered in Dr.
Reid’s ontology (and later propagated to Dr. Dorian’s). When a diagnosis is
accomplished, after the two practitioners have discussed the patient’s anamne-
sis, Dr. Reid leaves the room and Dorian’s personal Notes re-appear on the
Whiteboard. As a result of the interaction, Dr. Dorian’s ontology is now
enriched with the relations they discussed.
4 The CADO architecture
To support the described scenario a framework encompassing various kinds of func-
tionalities is required. A flexible and scalable peer-to-peer architecture allowing for the
dynamic discovery of the available components and for the quick reconfiguration of the
environment is the most suitable to the model.
UMICS'06 875
In CADO terminology, a peer is a software component that can be connected and
cooperate with other peers, to form a cluster; a node is a set of peers that are running
on the same hardware. A cluster of nodes may be defined by analogy as a set of com-
municating nodes, that are connected by some of their peers by means of a network.
Peers initially form clusters by means of “physical visibility” (e.g., devices in WiFi
range). Only peers that have the possibility to connect with one another may share a per-
ception of the same environment and, therefore, define a common context. At the same
time, different contexts can co-exist in the same environment, especially if the connec-
tion possibilities are manifold. For example two discussions regarding different topics
may occur in the same laboratory, involving different groups of people and different
devices. Different context-management services might be needed in such a situation,
even in the same local network. On the other hand, this solution does not exclude the
possibility of defining a common objective at the application level: a “virtual room” can
be created to let people closely interoperate even if they do not share the same physical
location.
Fig. 3(a) outlines the CADO architecture layers by showing two nodes. Physical in-
teroperability layer lets nodes communicate by addressing issues such as message dis-
patching, fault tolerance, user authentication. Semantic interoperability layer lets user
agents process data, understanding their meaning by providing an integrated view over
the different ontologies. The context interoperability layer lets user agents select data
and behavior according to specific situations. The context is defined by the technologi-
cal and social environment, and application purposes. Every time a cluster with a shared
context is formed, a context manager is locally delegated to coordinate the activities of
the participants. The interaction layer handles interaction with user interfaces. Interac-
tion managers select the base elements to define the common context, acting according
to internal (default) logics or enabling user direct interaction.
Fig. 3(b) shows, with a sample situation similar to that described in Section 3, con-
nected peers and involved layers. The interaction layer is composed of various devices
(some with high computing capabilities, like PCs and laptops, and with low capabilities,
like digital pens and mobile phones). Interaction managers are connected to context-
management peers, which may communicate to each other. Ontology-management peers
are of two different kinds: adapters and managers. Oval lines indicate clusters of peers
forming (virtual) rooms. In room 1 four devices (a laptop, a mobile phone, a Smart
Node 1 Node 2
Application Interface (UI) Application Interface (UI)
Interaction
Interaction Management Interaction Management Management
Context
Context Management Management
Semantic
Semantic Interoperability Interoperability
Physical Interoperability room 1 room 2
(a) (b)
Fig. 3. The layers of the architecture, and an example of setup.
876 Ubiquitous Mobile Information and Collaboration Systems
whiteboard, and a digital pen) share the same context and hence are coordinated by the
same context manager. This could be the situation in a meeting room where workers,
carrying some personal devices, are discussing in front of a whiteboard. Room 2 is an
example of virtual clusters among remote peers.
Interaction Managers (IM) are user agents with application-specific or device-
specific capabilities that provide users with interaction features and perform cluster dis-
covery and set up. An IM controls the user interface layout and the content information
displayed according to context information supplied by context managers. In a (virtual)
room cluster, IMs behave according to technological perspective and a social perspec-
tive. For example, users in room 1 (Fig. 3(b)) can participate in a face-to-face meeting
exploiting the whiteboard that acts as a shared display. Connection and cluster capabili-
ties are constrained by device capabilities. Devices with advanced connectivity support
and high performance computing power (e.g., PCs, laptops) will take care of simpler
devices (e.g., sensors, RFID readers, digital pens, interactive screens) by driving their
behavior to supply users with richer interactive environments.
Context Managers (CM) enact clustering mechanisms and define specific “poli-
cies” to drive the behavior of the participants. In the example of Fig. 3(b), the four
devices in room 1 are coordinated by the central CM according to its perception of the
environment. CMs are contacted by IMs when they fall in range according to casual
discovery protocol or when two or more persons decide to cluster. IMs and CMs com-
municate to each other to establish the context for the cluster, and elect a coordinating
CM that is in charge of routing information among participating IMs.
Context managers can accomplish their tasks thanks to a set of topic ontologies that
describe the different aspects to deal with. In principle, topic ontologies can cover the
definition of concepts and relations among them in any domain. In CADO, we mainly
deal with the classification of devices and their features, the definition of user roles and
preferences, the identification of situations, and finally the reference domain description
(e.g., fig. 1). Ontologies are independent resources that are actively published (they are
not just mere chunks of information that can be downloaded and edited as it often
occurs) and are remotely accessible.
Two kinds of ontologies are foreseen: normative and organization-wide ontologies
that are unmodifiable by regular users, and personal ontologies that are selected and
evolve according to users preferences and will. The former are often huge and complex,
the latter are often simple taxonomies (with a few other relations) that grow over time. In
an organization, CADO allows for personal ontology alignment, so to reach a common
and shared understanding.
In CADO, two kinds of peers handle ontologies: Ontology Adapters (OAs) and
Ontology Managers (OMs). OAs are in charge of managing a single ontology and
ensuring access to it. OMs have the task of merging ontologies to supply their clients
(i.e., context managers and ontology managers) with comprehensive (integrated) view
and navigation capabilities. These features are at the core of CADO, since, for example,
they allow context managers to process the device descriptions supplied by interaction
managers and set up a technological profile of the clustering peers.
UMICS'06 877
Node 1 Node 2 Scenario:
Connection between Node 1 and Node 2,
UI UI
CM of Node 2 is chosen as cluster center
3: choose CM (man. or auto)
Initial setup:
Node 1 (undetermined computing power)
IM 1: advertisement IM
UI, IM, (CM, (MMS, (OM, 2 x OA)))
2: CM description Node 2 (hi-computing power) composed by:
4: connect (send credentials) (UI, IM,) CM, MMS, OM, 3 x OA
6: CM requests IM config
7a Clustering (steps):
2 7c : IM sends OM and PKO ref to CM
1 : IM multicasts advertisement
9a : add/remove topic from context
2 : CMs response to IMs
10a: IM context message
7b 10b : context message 3 : IM selects one CM
11a : request for context information - manually, via UI
11b: context information - automatically, e.g., if running on a specific platform
12a : semantic query for document 4 : IM connects to CM (send user's credentials)
5 : CM contacts MMS and authenticates IM via LDAP
CM 12c : document query reults CM
6 : (if authenticated) CM requests IM (Node 1) configuration
7 : IM sends CM ref to its OM (if any)
8a : connect OMs by querying its local CM (if any)
12b : req/res on doc
5: Authenticate IM 8 : CM connects to Node 1 MMS and connects OMs
8c: connect PKO - if already Node 2 is provided with an OM, it keeps it and
makes it connect to the other OM
9b
MMS MMS - if CM is not provided with an OM then take IM's (if any)
- If no OM available in the cluster, search one outside
LDAP authenticate
- If no OM available at all, create one
9 : IM asks to connect/disconnect OAs (topics),
OM 8b OM - disconnection allowed only on its own
10: IM sends message to CM,
9c CM dispatches to affected IMs in the cluster
11: IM requests the current context
OA OA OA OA OA 12: IM requests for a document
- CM routes contextual request to MMSs
Fig. 4. Component communication protocol.
The set of CADO peers is completed by additional peers that supply specific ser-
vices to users such as Metadata-Management-System (MMS [7]) peers, and Lightweight-
Directory-Access-Protocol (LDAP) peers.
4.1 Current implementation
A prototype of the CADO framework has been implemented in JavaTM and relies on
JXTATM technology. The JXTA protocol is network agnostic, meaning that no assump-
tion is made regarding the network underlying the application. Therefore, it is well-
suited to support different networks, including wi-fi, bluetooth and wired LAN.
System components are aware of each other as a result of JXTA discovery mecha-
nisms that allow peers to look for other peers providing the needed service. Peer types
(i.e., IM, OM, CM, OA) are visible and recognizable by explicit XML advertisements
that express the services each peer offers. CMs are connected with each other as to build
a widespread CADO infrastructure, as for ultrapeer nodes in Gnutella networks, upon
which peers can form articulated clusters.
Peer clustering is managed by exploiting the JXTA peer-grouping mechanism. IMs
connect to CMs to share a view of a certain context with other IMs, by applying knowl-
edge representation published by an OM, around which OAs form another cluster for
semantic interoperability. Clustering occurs according to specific requirements, e.g.,
application behavior, client identity (authentication), common interests. To explain the
details, consider the typical interaction session in the configuration phase described in
Fig. 4. The example shows a node with advanced computational capabilities (e.g., lap-
top) that enters a cluster managed by a context manager running in another node. When
a node wants to participate in a CADO network (e.g., node1 “meeting” node2), the
configuration steps are the following:
878 Ubiquitous Mobile Information and Collaboration Systems
1-2. The new-comer IM (node1) sends (multicast) advertisement over JXTA network,
CMs (that of node2 as well as other CM peers reachable over the network) reply
with a message containing descriptions of the services they can provide (including
information on IMs already connected and OMs upon which they are relying).
3. The IM selects a CM according to application policies (or under explicit direction
of the user). An IM can be instructed to follow criteria such as connect to any CM
available, connect to a CM providing a certain service, connect to a CM running on
specific platforms. Assume that CM of node2 is elected as coordinator.
4-7. The configuration process occurs. The IM connects to the selected CM and sends
user credentials. The CM authenticates the user via LDAP, requests the IM config-
uration to collect information on the CM in use, exploited OMs and OAs.
8-9. The connections to support semantic interoperability are established. The elected
CM selects and connect to the OMs according to its policies. Polices can be en-
forced by the application or included in the CM configuration. IMs controls the
configuration phase by adding/removing specific topic ontologies. Moreover, the
CM connects to the participating MMSs.
10-12. The system is configured and the applications can start interacting. IMs can send
messages to a CM to:
1. provide context information: the CM collects it to compute the current context;
2. request the current context: the CM answers with updated and detailed infor-
mation on the cluster;
3. search documents according to semantic queries: the CM will manage the re-
quest to provide for access to connected MMSs.
5 Building semantic interoperability
In order to provide context-aware services to IMs, CMs have to manipulate a unified
view of the environment as supplied by OMs. In fact the role of OMs is to merge
the pieces of ontology (topics) that are exposed by reachable peers (OAs and other
OMs). OAs play the role of wrappers for ontology modules, directly attached as local
resources, i.e., ontologies expressed in a particular language, or created by a particular
software tool. OAs have the task of ensuring interoperability by exposing ontologies in
a homogeneous format.
OAs are in charge of managing local ontology evolution according to user-defined
policies or default policies that cover the most common situations. Default policies are:
– Normative policy: an ontology can only be modified by authorized (usually hu-
man) administrators. This policy is usually adopted for reference ontologies (e.g.,
an organization’s ontology) that are maintained off-line.
– Selective policy: an ontology can be modified on external requests (from OMs), but
only by authorized users and possibly with explicit authorization from the owner.
This policy is adopted to deal with sensitive ontologies that reflect user’s knowledge
representation. The owner might be prompted before a change can take effect.
– Plastic policy: an ontology can be freely modified by OMs. This policy is adopted
by flexible nodes that need to reflect external events. For example an ontology re-
lated to the social interactions should adopt such a policy.
UMICS'06 879
Consider Fig. 5 that shows a sample situation resembling the scenario in Section 3: a
physician attended a conference where a paper proposing a new treatment for a pathol-
ogy was proposed. Her personal ontology was updated to encompass the findings. Later,
she attends a meeting and decides to share her personal notes with other physicians. As
a result, another physician that was unaware of the new finding, is prompted to update
her knowledge base with the new information. This is due to the integration process
occurring when the two personal ontologies get in touch by means of an OM.
5.1 Merging of ontologies
Given two (or more) ontologies to be merged, corresponding concepts in the taxonomies
of the ontologies have to be found: this requires a similarity measure [11–14] for tax-
onomies (i.e., a matching criterion). When similarities are found, corresponding con-
cepts have to be merged preserving relations — taxonomy is-a, concept-specific at-
tributes, . . . — and origins have to be tracked, in order to remember to which ontology
concepts and relations belong.
In summary, according to literature (Section 6), ontology merging consists of (1)
finding places in the ontologies where they semantically overlap — either extension-
ally, i.e., concepts with the same label or synonyms, or intensionally, i.e., concepts
with the same place in the taxonomy — (2) check the consistency, coherency and non-
redundancy of the result. Iteration of such steps might be required.
In the current implementation, the default approach starts with the merging of topic
ontologies. For every topic ontology the activities are: (1) visit the taxonomy to iden-
tify overlapping concepts; (2) build the new taxonomy including all the concepts in the
original taxonomies; (3) add any other intra-topic relation to deliver the merged topic
ontology. The ontology merging is completed by adding inter-topic relations according
to the original ontologies. During the merging, simple checks are performed to avoid
cycles and replications. Note that tackling the merging problems by considering topic
ontologies ensure an higher degree of confidence, than considering generic taxonomies.
CADO supports merging customization by overriding similarity evaluation and check
procedures with more sophisticated matching criteria (e.g., translations, synonym anal-
ysis, WordNet-mediated mapping).
To illustrate the mechanism with an example, consider the merging of a new module
(OA), as in Fig. 5. Fig. 5.1 shows the initial situation, with three modules (b, c, and d)
already merged, and a that is still outside the cluster. When the ontology a enters in the
a b c d a b c d a b c d a b c d
(1) (2) (3) (4)
Fig. 5. Example of ontology merging taking place in four steps. Straight lines represent physical
connections between components; arrows show the direction of messages being exchanged.
880 Ubiquitous Mobile Information and Collaboration Systems
cluster of ontologies coordinated by an OM (Fig. 5.2, and Fig. 4 step 9), the merging
process starts by identifying homonyms and merging them in the OM’s ontology in a
concept containing the relations of both versions.
Through the merging of concepts and exploitation of relations, the new ontology
can be merged with the existing one by adding the new discovered links. However, since
the ontology exposed by the OM is only a “virtual” merging of the underlying ones (we
could even say that it is an alignment more than a merging [8]), the modules (OAs)
are notified with the newly discovered connections to let them learn new concepts and
relations (Fig. 5.3). Each OA can, in response to the notification, autonomously decide
whether to adopt or reject the change. As a result, every module that has accepted the
proposed changes is complete and sound, since all relations and axioms refer to known
concepts.
In the example, OAs b, c, and d exhibit different behaviors in response to the arrival
of a. Ontology d is affected and changed by the alignment with a, that is intuitively
depicted by a graphical change in the taxonomy. If a is the ontology adopted by a
certain user in a shared environment to index some contents, d may be the ontology
adopted by another user in a shared environment. Module c is not affected by the arrival
of a, it might be the case of the ontology of another user with different interests than
a’s, so that there is no overlapping or relation between their ontologies. Module b could
be affected by the knowledge retained in a, and that is depicted by the arrow coming
from the OM, but it is not accepting any change. This might be the case of a node with
a normative role in the organization, such as an enterprise server or a shared standard
categorization. Note that a is not only supplying new information, but it is also receiving
some, as a result of the alignment with other modules. When an ontology module leaves
a cluster (e.g., d in Fig. 5.4), the description of the part of the context it represents has
to be removed from the “virtual” ontology managed by the OM. Anyway, concepts and
references that were accepted (i.e., learned) by the other modules still exist in the global
ontology. Moreover, the departing module will bring around the acquired knowledge to
“contaminate” other peers in the system. In such a way, the knowledge diffusion and
long-term convergence is ensured.
5.2 Knowledge sharing
The CADO framework provides the facilities to create networks to share and exchange
knowledge among actors in heterogeneous environments, by means of fixed and mobile
devices.
The process of knowledge transfer can take place at different levels: between in-
dividuals, from individuals to groups, between groups, and from group to the organi-
zation [15]. Depending on the kind of knowledge transfer, pieces of information can
gain different importance. The problem of exploiting “hidden” knowledge, which is
not public and is retained by individuals in their own repositories, if not in their minds
(tacit knowledge), can only be overcome by fostering communication, collaboration,
and thus knowledge sharing. Knowledge networks are constituted by individuals with
similar interests, or that have the need to cooperate to pursue a common goal.
Sharing knowledge means more than providing shared access to information, since
learning and expertise transfer have also to be fostered. Even if knowledge networks
UMICS'06 881
system designers are aware of the issue, very often those same systems fail because
they require additional effort to users and also lack in pervasiveness and proactivity
(i.e., knowledge discovery functionalities). The additional amount of time each user has
to spend to nurture his/her knowledge network is very often the first cause of “death”,
or dismantlement for knowledge network systems. Aim of the CADO framework is to
enable the creation of systems that do not require such an extra effort. Applications de-
signed, and implemented, in the framework exploit contextual information to complete
the perception users have of the environment, by enriching their knowledge bases with
relevant information without explicit search operations on focus topics.
Furthermore new knowledge acquired should not be dispersed once a conversation
is concluded. Even if the (network) connections dissolve, the achieved consensus should
be propagated in space (i.e., by people moving to other locations with new documents
stored in their personal devices) and time (i.e., knowledge will persist in the place in a
integrated form until some of the devices are connected: local repositories are updated
by meeting participants).
In other words, in CADO systems, knowledge propagation occurs via direct ex-
change when people get in touch and their devices can communicate with each other.
Moreover, what has been learned during a conversation can also become persistent at
statically located peers (if they act as servers) so to propagate knowledge to other people
entering the same location. It is then a knowledge propagation model that works both
for static and mobile (nomadic) actors. Similar models have already been presented in
literature under the name of epidemic [16] or gossip-style [17] models.
6 Related works
Building ontologies by merging means to find similarities and mappings, and merge
concepts, relations, axioms, from existing ontologies to build a new ontology on the
same subject [18]. We defined a subject, or a context, as a set of related topics, and
described the general steps by which merging can be achieved. General mechanisms
have been presented for the merging of ontologies, to extend an existing ontology and
keep different versions of an ontology aligned (and to support replication) [19, 20].
A recent proposal presented a machine-learning approach to semantic alignment
of ontologies, namely APFEL. The general observation on which APFEL is based is
that alignment methods can be mapped onto a generic process, that is described with
details in [21]. APFEL method aims at improving existing alignment strategies, also by
involving users in critical steps (including the selection of an alignment strategy among
the existing ones).
Noy and Musen [8] presented PROMPT, a tool that provides semi-automatic fa-
cilities for merging and aligning ontologies. The tool is independent from the specific
ontology representation (i.e., syntax, language) and is based on Protégé, an open-source
ontology editor and knowledge acquisition systems that we have adopted in our experi-
ments. Chimaera [10,22] is another example of interactive environment for discovering
mappings between ontologies, and to merge ontologies. Both PROMPT and Chimaera
rely on lexical overlapping of concepts (matching terms). Feature-based approaches
have also been presented, meaning that two concepts are considered to be matching if
882 Ubiquitous Mobile Information and Collaboration Systems
they have common features (functions, parts, and attributes) [23]. Using an approach of
this kind, also the degree of matching can be assessed and expressed.
7 Concluding remarks
One of the major problems in pervasive computing environments is the capability of
systems to understand the context and to change their behavior accordingly. The frame-
work presented in this paper addresses the issue from two perspectives: clustering to-
gether actors to support collaboration via shared interfaces and interactions; and merg-
ing shared ontologies to form short-term and long-term consensus. The former perspec-
tive is supported by Context Managers, which can understand the context and drive ac-
tors’ devices accordingly, the latter is supported by Ontology Managers, which provide
for dynamic merge of personal knowledge bases.
Knowledge management issues are tackled from different perspectives: (a) techno-
logical, since dynamic automatic configurability of environments is achieved, (b) orga-
nizational, since the framework allows the integration of normative knowledge sources
with subjective conceptualizations of users, (c) social, since it is possible for users with
different roles to interact in a facilitated way, (d) personal, since it is possible for users
to play different roles and yet not lose their identity and knowledge.
Future activities will deal mainly with extensive testing and validation of the pro-
posed solution. In fact, the adoption of new technologies changes the personal and or-
ganizational practices and promotes new needs that should be taken into account to
propose effective solutions. The use of familiar devices, such as cellular phones, lap-
tops and digital pens, is crucial to address the problem of making people accept inno-
vative solutions, according to pervasive-computing paradigm, that are not disruptive of
acquired habits.
Acknowledgements
The work presented in this paper was partially supported by the ATELIER project (Ar-
chitecture and Technologies for Inspirational Learning Environments, IST-2001-33064)
and by the MILK project (Multimedia Interaction for Learning and Knowing, IST-2001-
33165).
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