=Paper=
{{Paper
|id=Vol-175/paper-13
|storemode=property
|title=SAM: Semantics Aware Instant Messaging for the Networked Semantic Desktop
|pdfUrl=https://ceur-ws.org/Vol-175/11_franzstaab_sam_final.pdf
|volume=Vol-175
|dblpUrl=https://dblp.org/rec/conf/semweb/FranzS05
}}
==SAM: Semantics Aware Instant Messaging for the Networked Semantic Desktop==
https://ceur-ws.org/Vol-175/11_franzstaab_sam_final.pdf
SAM: Semantics Aware Instant Messaging
for the
Networked Semantic Desktop
Thomas Franz and Steffen Staab
ISWeb, University of Koblenz-Landau, Germany
{franz,staab}@uni-koblenz.de
Abstract. While instant messaging (IM) became a mature communi-
cation means in business organizations over the last years, IM systems
did not follow this evolution comparably. Communicated content is often
stored insufficiently and hard to recall, integration into other desktop ap-
plications impossible. In this paper, we address these shortcomings and
provide concepts for novel instant messaging. In contrast to prior work
such as the Haystack system, which integrates IM data into a personal
information management application, we enhance IM based on a ready
to integrate ontological meta model that introduces semantics to instant
messaging and its content to foster advanced management. In particular,
we address networked exchange of semantic meta information to inte-
grate IM into the Networked Semantic Desktop. The Semantics Aware
Messenger (SAM) is a prototypical implementation of the concepts pre-
sented in this paper.
1 Introduction
The objective of the Semantic Desktop is to improve personal information man-
agement (PIM) by combining all content available on the desktop and relevant
to the user to i) easily manage that content, regardless of which type, and to ii)
simplify utilization of it.
The Networked Semantic Desktop as envisioned in [4] describes a networked
infrastructure that combines the Semantic Desktop with Social Networking and
P2P systems to benefit novel applications such as group collaboration.
In this paper, we address instant messaging (IM) on the Networked Seman-
tic Desktop. In contrast to prior work such as the Haystack system [10], which
integrates various desktop sources into a consistent, meta data driven personal
information management application, we enhance IM based on a ready to inte-
grate ontological meta model that introduces semantics to instant messaging and
its content to foster advanced management. In particular, we address networked
exchange of semantic meta information to integrate IM into the Networked Se-
mantic Desktop.
Today, communicating by instant messaging mainly comprises typing mes-
sages and viewing incoming messages, while most of the time, no further pro-
cessing of messages is done or offered so that message content gets lost in plain
text communication logs that are more or less accessible depending on the client
application. Despite poor traceability, recent studies claim that IM usage has
matured and IM is employed for miscellaneous tasks including complex con-
versation [11, 9]. Accordingly, we consider the content communicated via this
media as of increasing value that should be recallable and integrated into the
Networked Semantic Desktop.
The objective of this paper is to tackle traceability shortcomings of IM, im-
prove management of IM content, and move IM towards the Networked Semantic
Desktop. We are proposing i) an ontological meta model for instant messaging
which ii) supports integration into the Networked Semantic Desktop, and iii)
introduces meta data and semantics for IM to enable iv) sophisticated reutiliza-
tion of instant messaging data. Based on the meta model and an v) identification
scheme for IM data including meta information and semantics we vi) enable net-
worked exchange of such information via IM to vii) ground novel applications as
envisioned in [4].
In Sect. 2, we sketch a typical IM scenario to indicate shortcomings of current
IM systems (Sect. 3), explain our concepts to overcome these shortcomings (Sect.
4), and illustrate the implementation of these concepts by examples of that
scenario in Sect. 6. We give a detailed overview of the ontological meta model
in Sect. 5, and contrast our work with related work to provide a conclusion in
Sect. 7. In Sect. 8, we suggest future research and give an outlook.
2 Scenario
The extracts of chat conversations in this section render a common instant mes-
saging scenario and indicate different functions and particularities of IM.
The scenario: Steffen, being the lecturer of the Semantic Web lecture uses
IM to get some quick responses concerning organizational issues from Thomas,
who held the last exercise session for the lecture:
[09:29:06] S t e f f e n : how was t h e e x e r c i s e s e s s i o n ?
[09:29:33] S t e f f e n : d i d you t e l l them t h e d a t e o f t h e exam ?
[09:30:33] Thomas : s o l u t i o n s were ok , p a r t i c i p a t i o n was weak
[09:30:56] Thomas : y e s , i g u e s s a b o u t 20 w i l l s i g n up f o r i t
Listing 1.1. Exercise Session
At a later time, Thomas informs Steffen about his work on a paper he is writing
for the Semantic Desktop Workshop.
[ 1 2 : 0 7 : 4 5 ] Thomas : i w i l l p u t new v e r s i o n s o f t h e p a p e r f o r
t h e sdws a t h t t p : / / i s w e b . p a p e r s . x . y /sam . t e x
[ 1 2 : 0 8 : 1 8 ] S t e f f e n : ok , what i s g o i n g t o c h a n g e ?
[ 1 2 : 0 8 : 3 5 ] Thomas : d e s c r i b e an IM s c e n a r i o t o i n d i c a t e c u r r e n t
s h o r t c o m i n g s , p r o p o s e i m p r o v e m e n t s , and d e m o n s t r a t e SAM
i n terms of the s c e n a r i o
Listing 1.2. Semantic Desktop Paper
After lunch, Thomas contacts Steffen about the scenario he mentioned in List-
ing 1.2.
[ 1 3 : 5 5 : 0 4 ] Thomas : any i d e a s f o r a s u i t a b l e s c e n a r i o ?
[ 1 3 : 5 5 : 2 4 ] S t e f f e n : why don ’ t u s e t h i s c o n v e r s a t i o n ?
[ 1 3 : 5 5 : 4 3 ] Thomas : r i g h t ! i t s a s u f f i c i e n t e x a m p l e o f a
work r e l a t e d c h a t
[ 1 3 : 5 6 : 1 5 ] Thomas : i ’ l l u s e o u r today ’ s e a r l i e r c h a t s
a s w e l l . t h e y n i c e l y i n d i c a t e d i f f e r e n t f u n c t i o n s o f IM
Listing 1.3. Scenario for the Paper
Later on, Steffen talks to Bernhard, a co-worker in project X:
[ 1 7 : 1 5 : 4 2 ] S t e f f e n : w r t t h e p r o j e c t you m i g h t be
i n t e r e s t e d i n what thomas i s c u r r e n t l y d o i n g ; i ’ l l s e n d
you what thomas t o l d me a b o u t t h a t s o f a r
[ 1 7 : 1 6 : 0 3 ] B e r n h a r d : t h a n k s , i ’ l l c o n t a c t him when
i ’ ve read i t
Listing 1.4. Project X Work
2.1 Terminology and Observations
Isaacs et al. [9] discovered that – in professional environments – IM messages
mostly are work-related (61.8%), followed by scheduling and coordinating ones
(30.8%) and those that resemble simple questions and information (27.8%).1
Based on that terminology, we classify the chat excerpts (Listing 1.1 to 1.4)
as follows: Listing 1.1 is a sample of simple questions and information, while
Listings 1.2, 1.3, 1.4 represent work-related messages. In the given scenario, we
excluded scheduling/coordinating conversations, as they resemble a typical IM
function, but do not contribute much here.
2.2 Use Cases
Due to the fact that most IM conversations are about work, the content of
such conversations needs to be available for later reuse as illustrated by the two
following use cases.
Use Case 1: About one week after the day when the listed conversations took
place, Steffen wants to check where Thomas stored that file on the server, and
what exactly he stated about his current work. As Thomas is not available he
cannot ask him again.
Use Case 2: In order to track project development, and summarize the cur-
rent stage of project X, Steffen wants to compile all project X related content,
including messages that deal with the project.
1
Messages could be classified for more than one category.
3 Accomplishing the Use Cases Today
Today’s instant messengers usually store messages in plain text logs and provide
a user interface to view the logs, sometimes ordered by message date or filtered
by user. More sophisticated messengers may supply an additional search over
the message logs. Accomplishing the use cases with current systems reveals the
following shortcomings:
1. Weak Message Classification:
Finding appropriate messages by browsing the message logs requires high
user effort as the given classifications (by user, by date) do not narrow the
search space enough to easily find messages: Given that Steffen does not
recall the exact day when Thomas told him about his current work, he has
to read all the messages from several days to find the one he seeks.
2. Keyword search is unsuitable due to missing content semantics and particu-
larities of chat conversation style:
(a) A term denoting the subject of a message, or significantly distinguishing
a message from others is not necessarily contained in a message so that
creating efficient search strings is delicate. Entering a query that finds
the message Steffen looks for in use case 1 may be difficult as Thomas
did not use keywords like ”store”, ”server”, or ”file” that directly relate
to the semantics of his message in Listing 1.2.
(b) Ambiguity of search terms further decreases the average relevance of
search results. If Steffen searches for paper, he may receive messages
that deal with different concepts of paper such as writing paper, abrasive
paper, and research paper while only the latter is relevant for him.
3. Missing Context:
(a) Instant messages are rather short, and informal [7, 6, 12] therefore be-
come meaningless without context. In Listing 1.1, Thomas said ”the
solutions were ok, participation was weak”. Without the message’s con-
text it is hard to predict which solutions Thomas points at. Current
IM systems do not provide message context so that identifying relevant
messages is difficult.
(b) Topic switching and interleaving messages are particularities of IM con-
versation. Listing 1.1 has interleaving messages, as Thomas’ first message
replies to Steffen’s first message although it appears after Steffen’s sec-
ond message. The context of interleaving messages is not based on the
sequence in which they appear in time so that even browsing message
logs ordered by time does not necessarily provide relevant context.
4. Missing Messaging Semantics:
Current IM clients do not identify message properties, e.g. the creation date,
or sender of a message. Consequently, relations between them cannot be
exploited:
(a) Missing messaging semantics inhibit integration into the Networked Se-
mantic Desktop.
(b) Information exchange is of low value as just meaningless plain text can
be exchanged.
(c) Semantic querying using restrictions on properties is impossible, e.g.
querying for messages within a date range, sent by a certain user et
cetera.
4 Improvements by SAM
4.1 Message Classification for Message Semantics
The first shortcoming mentioned in Sect. 3 denotes weak message classifications
provided by current IM clients. SAM offers a user-definable taxonomy that is
used to add semantics to messages by annotating them with entries from the
taxonomy. For instance, Steffen might define the category work with two sub-
categories teaching and projectX. If he annotates any message related to project
X with the corresponding entry in the taxonomy, accomplishing the second use
case is as easy as browsing for all messages annotated with projectX. Message
classification also benefits search, as queries can restrict search results to be an-
notated with certain taxonomy entries. How annotations and the taxonomy are
designed is detailed in Sect. 5, how the user annotates with SAM is explained in
Sect. 6.2.
The main drawback of message classification is the user effort required to
annotate messages appropriately. This effort is lowered by automatic annota-
tion exchange between conversation partners as detailed in Sect. 4.3 and 6.4,
however, manual annotation still has to be done by at least one of a conversa-
tion’s participants in order to gain benefits. The user interface of SAM tries to
minimize this effort as much as possible (see Sect. 6.2) and for future work we
propose to integrate automatic message classification based on machine learning
technologies.
4.2 Ontological Meta Model
We employ a meta model for instant messaging in form of a unified messaging
ontology (cf. Sect. 5) that tackles many of the shortcomings listed in Sect. 3.
The ontological meta model provides semantics for IM entities such as per-
sons, messages, conversations, annotations, and message texts as it identifies and
relates such entities to each other by meaningful properties. This permits several
enhancements as detailed in the following:
Message Context: Any message is accompanied by its context, i.e. messages link
to their following message, their sender and recipient and so on. Accordingly,
messages displayed while browsing or in search results are much more informative
thus reducing the user effort of determining whether or not they are relevant.
Semantic Querying: Querying becomes more powerful as the ontological meta
model permits to define what to query for, e.g. one can not only query for mes-
sages but also for users or taxonomy entries. Moreover, restrictions on properties
can be defined, e.g. Steffen can request messages sent by Thomas within a cer-
tain date range, including the keyword ”paper” in their message text. Resulting
messages will directly link to related messaging entities to provide context.
Integration: As the ontology unambiguously defines messaging entities it inte-
grates IM into the Networked Semantic Desktop by providing interoperability
between applications. For instance, the sender of a message in Steffen’s store
can be identified as the author of a document on his hard disk, or the sender
of an email in his email client. Such features require, however, that applications
commit to the same ontology. Thus, SAM does not employ a proprietary repre-
sentation of persons, but integrates the Friend-of-a-Friend (FOAF2 ) ontology as
it is widely recognized for expressing identity.
The ontology abstracts the concept of a message considering interoperability
of different message channels as proposed in [13]. A unified view of messaging
aims at seamless integration between different messaging applications as it al-
lows to track conversations that comprise different message types and message
channels, e.g. receiving an email message and answering with an instant message.
4.3 Meta Data Exchange
All participants of a conversation deal with the same set of messages. As each
user decides how to annotate a message and which concepts to have in his tax-
onomy, there are cases where annotations differ between users, and where one
user annotated a message while the other one did not. A common meta model
on each peer, unique identification of IM entities, and provenance information
established by the messaging ontology enables automatic annotation exchange
between peers to either add further message semantics through additional an-
notations, or add annotations for not yet annotated messages. The latter case is
especially important to reduce annotation effort for the user. As each user main-
tains his own taxonomy, annotation exchange may also introduce new taxonomy
entries. SAM offers different user options to deal with incoming annotations as
explained in Sect. 6.2. Technical aspects of meta data transfer are mentioned in
Sect. 6.4.
Meta data exchange is not only useful to decrease annotation effort, it per-
mits several novel applications. In Listing 1.4, Steffen tells Bernhard to send
him, what Thomas told him. Meta data exchange as proposed by SAM allows to
automatically integrate messages sent between Thomas and Steffen into Bern-
hard’s data store so that Bernhard can utilize all features of SAM to access these
messages.
5 The Ontology
Figure 1 depicts the ontology and defines the namespaces used for the follow-
ing textual explanation of the ontology. A conversation is modeled by the class
2
http://www.foaf-project.org/
Fig. 1. Unified Messaging Ontology of SAM
m:Conversation, which relates to messages exchanged within a conversation by
the m:hasMessage property. A message is a subclass of foaf:Document and is
associated to its content by the m:hasText and m:hasBinary properties. The
m:follows property and its inverse, m:precedes, track the chronological order
in which messages appear, while the m:repliesTo property records further valu-
able context information that goes beyond chronological ordering: It relates a
message to the message it replies to thus relating these messages based on the
semantics of their content. This property is significant to store appropriate con-
text information for interleaving messages as illustrated in Listing 1.1. Section
6.2 explains how this property is set using SAM.
Persons are represented by foaf:human as defined in the FOAF ontology,
which already features messaging relations, including instant messaging proper-
ties such as foaf:jabberID.
In order to add semantics to messages and conversations, they are anno-
tated with entries of a taxonomy. The taxonomy is defined using the Sim-
ple Knowledge Organization System (SKOS3 ), an ontology to describe concept
schemes providing several predefined classes and properties for this purpose. The
skos:narrower and skos:broader properties are used to build a skos:Concept
hierarchy, while the skos:subject property is used to associate things - in our
case messages and conversations - with concepts.
Employing a standard meta ontology for knowledge representation fosters
integration of ontologies that are based on the same meta ontology. However, as
the hierarchical structure is established by only two relations, namely broader
3
http://www.w3.org/2004/02/skos/
and narrower, transforming existing taxonomies or lexica defined with other
meta ontologies to a SKOS representation is straightforward as well. As an ex-
ample, Wordnet4 can be transformed to a concept hierarchy defined with SKOS
by interpreting the hypernym and hyponym relations of Wordnet as narrower
and broader relations of SKOS.
Provenance data for annotations that allows to track who annotated what
and when is established by individuals of m:AnnotionStatement that references
the creator (m:annotator) and creation date of an annotation. Any such an-
notation is a reified statement that points at the resources representing the
annotation.
Provenance information is also kept for messages and taxonomy entries by
the m:sender, and m:conceptCreator properties as illustrated in Fig. 1.
6 SAM
6.1 Technologies Enabling SAM
SAM builds upon the instant messaging client BuddySpace5 [17], which was
developed during research on online presence in instant messaging at Open Uni-
versity. BuddySpace is a client for the Jabber6 network which we extended to
use the ontology depicted in Sect. 5. A programming interface was developed
that encapsulates the ontological model and provides methods to write to it and
read from it, such as adding an annotation, or retrieving messages annotated
with a given concept.
The messaging ontology is defined using the Web Ontology Language (OWL)[1].
It defines the properties and classes as explained in Sect. 5, including appropriate
restrictions for them (range, domain, cardinality, functional, inverse, et cetera).
Instances of the classes defined in the ontology are represented as RDF to sup-
port integration with the Networked Semantic Desktop and to establish a well
structured and easy to access data store that simplifies incorporation of meta
information, interlinking of resources, and exchange. The Jena7 RDF API for
Java is used to access the store.
The communication protocol used by the Jabber network is the Extensible
Messaging and Presence Protocol (XMPP)[15], an XML-based protocol that is
well supported by multiple open source programming libraries.
6.2 Annotations and Context
In contrast to common IM clients, the chat window of SAM contains an addi-
tional taxonomy panel (cf. Fig. 2). The chat window permits message annotation,
taxonomy management, and the addition of context information while chatting.
Both, the message panel and the taxonomy panel allow to accomplish multiple
4
http://wordnet.princeton.edu
5
http://kmi.open.ac.uk/projects/buddyspace/
6
http://www.jabber.org
7
http://jena.sourceforge.net/
(a) Selecting multiple messages. (b) Annotating with a taxonomy entry.
Fig. 2. Annotating Multiple Messages
annotations at once to reduce user effort. Annotations are made either by double-
clicking on a particular message that automatically annotates that message with
all taxonomy entries that are currently selected, or by double-clicking a taxon-
omy entry which automatically annotates all selected messages with that entry
as illustrated in Fig. 2. To further minimize user effort, if no message is selected,
double-clicking on a concept contained in the taxonomy automatically annotates
the last displayed message. As direct visual feedback, annotated messages are
displayed as child nodes in the taxonomy (cf. Fig. 2b).
New annotations are automatically sent to the conversation partner to further
reduce annotation effort and gain additional message semantics. We propose
different policies (cf. Table 1) that define how new annotations that potentially
introduce new taxonomy entries are handled based on how much trust is given
to the creator of an incoming annotation.
Table 1. Policies for handling incoming annotations.
Trust Level New Annotation New Taxonomy Entry
Low require user confirmation require user confirmation
Medium automatically add annotation require user confirmation
High automatically add annotation automatically add entry
For any created message, the m:follows, m:precedes, m:sender, m:recipient,
m:hasText, and m:hasConversation properties are automatically set by SAM
to establish context information. The m:repliesTo property can be set through
the message panel of the chat window as illustrated in Fig. 3: Selecting a message
with a right-click automatically sets the m:repliesTo property of the next sent
message to the selected one. Messages that have this property set are automati-
cally displayed underneath the message they reply to. As IM conversations often
have interleaving messages (cf. Listing 1.1) with different topics, this feature does
not only provide additional message context, but also eases IM conversation as it
assists the user in identifying related messages. All context information created
for a message on one client is automatically transferred to the recipient when
that message is sent to provide as much meta information as possible on both
sides of a conversation. Section 6.4 describes in more detail how the transfer of
such information is implemented.
(a) Selecting message to reply to. (b) After sending the message.
Fig. 3. Replying with interleaving messages
6.3 Semantic Search and Semantic Browsing
SAM allows to combine full-text search in message texts with semantic search
features as illustrated in Fig. 4a. The user can restrict a search by specifying
a date range for the message creation time, require specific persons to be the
sender and the recipient, and restrict search results to be associated with certain
taxonomy entries. Resulting messages are displayed with their context available
for further exploration through the property explorer that opens by clicking on
non-literal objects such as persons and taxonomy entries (cf. Fig. 4b).
The semantic browser (cf. Fig. 5) allows to view messages classified by the
individual taxonomy. Non-annotated messages are associated with an additional
taxonomy entry so that the user can still access them. As for search results,
object properties (displayed underlined) can be further examined (cf. Fig. 4b).
6.4 Meta Data Transfer
Every messaging entity (e.g. person, message) is identified by its uniform resource
identifier (URI) to support global identification and thus exchange of such enti-
ties. For example, each new instance of m:Message needs to be available for the
sender and the recipient as both may want to reutilize it.
SAM exploits the extension mechanism of the XMPP to transfer messaging
entities between different SAM clients, and to support automatic meta data ex-
change. Different extension types, namely message, annotation, resourceRequest,
(a) Semantic search. (b) Property Explorer.
Fig. 4. Semantic Search and Property Explorer
and resourceResponse are defined for this purpose. A message extension contains
the RDF representation of a message while an annotation extension contains an
instance of a m:AnnotationStatement. The two other extensions enable to re-
quest and retrieve one or multiple RDF resources with all their properties. The
following two use cases exemplify how the extensions are used:
1. When sending a chat message, SAM automatically creates a new instance of
m:Message with corresponding properties, and attaches its RDF represen-
tation in a message extension to the XMPP packet that sends the message.
The receiving SAM client extracts the RDF data contained in the packet’s
extension and adds it to its own store.
2. When a client receives an annotation with a taxonomy entry that is not
contained in its RDF store, the client repeatedly requests more general
(skos:broader) taxonomy entries from the sender until a retrieved entry
matches an entry in the local taxonomy so that the new taxonomy entry
can be correctly inserted into the taxonomy and the annotation becomes
effective.
7 Conclusion & Related Work
This paper presents concepts and an implementation of enhanced IM with re-
spect to the Social/Networked Semantic Desktop. SAM introduces rich meta
data, including semantics, to instant messaging and its content to provide en-
hanced management features that exploit such additional information. The main
achievement, distinguishing SAM from existing systems, is the establishment of
an IM infrastructure to globally exchange content and its semantic meta data in
order to gain knowledge. This ability grounds several novel applications such as
knowledge collaboration.
Fig. 5. The Semantic Browser of SAM
Zhang et al. present the Small World Instant Messenger in [18]. They build
user profiles based on users’ bookmarks or homepages, which are then used for
expertise search. In contrast to our approach they rather exploit the infrastruc-
ture provided by instant messaging without addressing any issues of instant
messaging itself. Consequently, they disregard management, reusability, and in-
tegration issues while establishing a service on top of instant messaging.
The Haystack system [14] comes with a general notion of messaging including
a unified messaging ontology [13] similar to the ontology presented in Sect. 5.
While the Haystack system focuses on integrating messaging into a personal in-
formation manager, SAM considers the networked exchange of meta information
and is ready to integrate with other applications on the Semantic Desktop.
Chirita et al. explain how to use activity based semantic meta data [3] in their
desktop search prototype. Exemplarily, they deal with email, file system, and web
cache meta data and have developed an architecture that combines such meta
data with standard full text search. While our work also combines full-text search
and meta data to improve management, we address different enhancements and
options for exploitation that are specific to the instant messaging context.
Vogiazou et al. established enhanced symbolic presence for instant messaging
[17]. One outcome of this research is the BuddySpace instant messaging client
and server component that allow to automatically group buddies and visualize
their location and presence information respectively. SAM extends BuddySpace
by semantic annotations, semantic search, semantic browsing, and (semantic)
meta data communication.
The CoAKTinG (Collaborative Advanced Knowledge Technologies in the
Grid) project [2] developed a meeting ontology to summarize content of different
collaborative technologies. The summarized content is used to provide meeting
replays that span content communicated via multiple channels, such as instant
messaging, or video conferencing. While CoAKTinG imports BuddySpace com-
munication logs into the meeting ontology, SAM contributes to CoAKTinG by
providing already well structured additional (semantic) meta data.
The Gnowsis system [16] provides an architecture and server component for
integrating arbitrary applications on the Semantic Desktop. Applications are re-
quired to describe their data by ontologies and are connected to the Gnowsis
desktop service by plugins. As a result, different data from various desktop ap-
plications is unified through a single Gnowsis user interface. As SAM already
employs ontologies to represent all its data, integration into the Gnowsis system
is at hand.
8 Outlook
Meta data exchange as explained in Sect. 4.3 and 6.4 can enrich knowledge
bases but also institutes several applications that go beyond that scope. Taxon-
omy overlappings between different communication partners represent a shared
view, naturally established based on communication of taxonomy entries and
their relations. Accordingly, rejecting and accepting incoming taxonomic data
is a simplistic example of online collaboration on a concept hierarchy. Further
work on generalizing the process model will allow online collaboration that is
independent of a specific problem domain.
While IM is employed by business organizations, improving company wide
knowledge management through expertise search might be a welcomed feature
in businesses. The Bibster project [8] establishes semantic routing based on the
expertise of peers. In Bibster, expertise is computed from annotations of bibli-
ographic data with topics from the ACM topic hierarchy. The knowledge base
provided by SAM can be exploited similarly, however, not to implement semantic
routing but to compute the expertise of users and provide an expertise search.
As mentioned in Sect. 4, we consider automatic message classification as a
future improvement. As instant messages differ from other text documents [9,
12], we consider classification of such messages as a challenging task. However,
as SAM provides rich message context, any message is usually related to several
other messages that may be exploited to improve classification. Moreover, if
each SAM client runs a classifier that works on a potentially different knowledge
base, we may investigate how to combine different classifiers and their results to
improve overall classification quality.
A very significant open issue is how to incorporate security and privacy issues,
especially trust as defined in [5] as credibility and reliability of resources.
Application oriented visions include the integration with existing software
for the Semantic Desktop such as the Haystack or the Gnowsis systems.
Acknowledgments
We would like to thank Arup Malakar for his contributions to the development of
SAM. This work is conducted with respect to the upcoming project Knowledge
Sharing and Reuse across Media (X-Media), funded by the Information Society
Technologies (IST) programme of the 6th Framework Programme.
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