Effective and Efficient Online Communication
The Channel Model
Anna Fensel, Dieter Fensel, Birgit Leiter and Andreas Thalhammer
Semantic Technology Institute (STI) Innsbruck, University of Innsbruck, Technikerstraße 21a, A-6020 Innsbruck, Austria
Keywords: Social Media, Web 2.0, Semantic Web, Dissemination, Communication, Knowledge Management.
Abstract: We discuss the challenge of scalable dissemination approach in a world where the number of
communication channels and interaction possibilities is growing exponentially, particularly on the Web,
Web 2.0, and semantic channels. Our goal is to enable smaller organizations to fully exploit this potential.
We have developed a new methodology based on distinguishing and explicitly interweaving content and
communication as a central means for achieving content reusability and thereby scalability over various,
heterogeneous channels. Here, we present in detail the communication channel model of our approach.
1 INTRODUCTION
Fax, phone, and later the Internet, have radically
changed our communication possibilities. More and
more communication has been freed from the
geographical barriers that formerly limited their
speed and expansion. Now, it is (in principle)
possible to instantly communicate with a large
portion of the entire human population.
Nevertheless, new means also generate new
challenges.
Take the world of the TV consumer as an
example. Twenty-five years ago, there were around
three channels. Therefore, selecting your program
was a rather trivial task which required no more than
a few seconds. Whilst hundreds of channels have
been added, thousands of channels have been
connected via the Internet, where extremely large
libraries of videos (which go beyond the metaphor
of a ‘channel’), currently define the content. Now
the consumer could spend the rest of his/her life
browsing in search of the program he/she wishes to
watch. Obviously, he/she requires either new skills
or new access means to scale and filter the
exponentially increased information service offer.
Or assume the task of a small hotelier. How can it be
ensured that the hotel is found by potential
customers, i.e., how can he/she find them? It should
have a web site with high visibility by various search
engines, and must be present in a large number of
on-line booking channels, we should find it through
the town's website, and obviously a Facebook site is
a must (with a booking engine included). Bookings
through mobile platforms are significantly
increasing and the hotelier would want to be found
there too. Why not add a video about the hotel on
YouTube, a chat channel for instant communication,
fast email and fax response capabilities, the old-
fashioned telephone, and occasional tweets and
emails that are clearly distinguishable from spam?
Preferably, the communication should be multi-
directional, i.e., the organisation should realize when
one of its posts gets commented on (up to a full-
fledged impact analysis), or even more importantly,
it should be aware when someone talks about the
hotel and how much the costumer liked it. As much
as this is needed, this obviously does not scale,
especially for SMEs with limited marketing
activities budgets, and (Mulpuru et al., 2011) calls it
“the growth of the multichannel monster”.
Therefore, organizations of all sizes, commercial
and not-for-profit, regularly face the challenge of
communicating with their stakeholders using a
multiplicity of channels, e.g. websites, videos, PR
activities, events, email, forums, online
presentations, social media, mobile application, and
now, structured data. The social media revolution
has made this job much more complicated, because:
• the number of channels has grown exponentially,
• the communication changes from a mostly
unilateral "push" mode (one speaker, many listeners)
to an increasingly fully bilateral communication,
209
Fensel A., Fensel D., Leiter B. and Thalhammer A..
Effective and Efficient Online Communication - The Channel Model.
DOI: 10.5220/0004011402090215
In Proceedings of the International Conference on Data Technologies and Applications (DATA-2012), pages 209-215
ISBN: 978-989-8565-18-1
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)
where individual stakeholders (e.g. customers)
expect one-to-one communication with the
organization, and the expected speed of reaction
shrinks to almost real-time, and
• the contents of communication becomes more
and more granular and increasingly dependent on
the identity of the receiver and the context of the
communication.
Organizations need an integrated solution that
provides the management and execution of
communication goals in a mostly automated fashion,
at the costs of mass-media communication, with the
granularity of individual experts, at the pace of real-
time social media. We are aiming to mechanize
important aspects of these tasks, allowing scalable,
cost-sensitive, and effective dissemination for small-
or-medium sized business units and comparable
organizations, for which dissemination is essential
but resources are significantly limited. Additionally,
it may also help intermediaries such as marketing
agencies to extend their business scope by increasing
the cost-effective ratio.
The core idea of our approach is to introduce a
layer on top of the various Internet based
communication channels that is domain specific and
not channel specific. So one has:
• information models (specific for certain
domains), that define the information or knowledge
items;
• a channel model (the communication model),
that describes the various channels and their target
groups; and
• mappings of information items on channels
through weavers.
In this paper we present in detail the channel model
of our approach.
The paper is structured as follows. Section 2
discusses the channel model of the approach.
Section 3 discusses related work, Section 4 sketches
future work and Section 5 provides the conclusions.
2 CHANNEL MODEL
According to Wikipedia, “in telecommunications
and computer networking, a communication
channel, or channel, refers either to a physical
transmission medium such as a wire or to a logical
connection over a multiplexed medium such as a
radio channel.” In online communication, we take a
broad definition of a channel. A channel is a means
of exchanging information in the on-line space.
There is a close relationship between URIs and
channels as each URI can be used as a channel to
spread or access information. However, not each
channel directly refers to an URI. For example,
Facebook provides around forty different methods of
spreading information not distinguished by a URI.
Additionally, individual information items spread
through Facebook are not distinguished by URIs. In
general, a channel can be interpreted as a “place”
where one can find or leave information, whether it
is unanimously referred by a URI or addressed
through a service. However, even this is not broad
enough. As described previously, a channel can also
be the URI of a vocabulary (or the formalisms such
as RDFa or microformats) that are used to publish
the information. Through use of this URI, only
humans or software agents that “speak” this dialect
are able to access this information. Here, the
communication channel cannot be interpreted as a
place, but rather as a way to express or refer to the
information. In the following, we want to distinguish
channels by the communication mode they support.
Communication is based on the broadcasting of
information. Therefore, we define the first category
of our channel classification system as channels used
for broadcasting. Here we make a distinction
between the publication of mostly static information
and dynamic contents that express the timeliness of
an information item. One way of spreading
information is to invite other people to use it.
Therefore, sharing is another category we have
identified. It reflects the insight that others are not
passive consumers of our information but active
prosumers that should be helped and supported in
their information processing activities. Sharing is the
first form of cooperation. Explicit collaboration
through a shared information space is the next
cooperation category we have identified.
Collaboration between individuals leads to groups of
people actively organizing their communication and
cooperation. Social networking sites that support
groups of people in their information needs are
instances of this next category we have identified.
Obviously, the boundaries between these categories
are fluid and many channel providers try
intentionally to establish services covering several of
them. Still, it is often possible to identify a major
category for them, often based on the major usage
patterns of their users. An important approach to
broaden the scope of a dissemination activity is to
add machine-processable semantics to the
information. With this approach, search and
aggregation engines can provide a much better
service in finding and retrieving this information. A
DATA2012-InternationalConferenceonDataTechnologiesandApplications
210
means of adding machine-processable semantics to
information is our final channel category.
Broadcasting Static Information. Websites are an
established means of providing (mostly) static
information. Information that reflects the structure of
the contents is provided through websites and they
offer a smooth way for users to access this content.
An important addition beyond the dissemination
through an owned website is an entry on other sites
such as Wikipedia, the world’s leading
encyclopedia.
Broadcasting Dynamic Information. With Web
2.0 technologies, dedicated means for publishing
streams and interacting with information prosumers
have been added. A first step in this direction is the
inclusion of a News section in a website using
blogging tools such as Wordpress. Good practices
for a news section on a website are:
• Each news item has its own URL, so that they
can be returned in search results, bookmarked,
shared etc.;
• News should contain a pointer to a more detailed
description about the information items they
describe;
• each news item is archived;
• each news item can be indexed by search
engines;
• each news item is typed (through use of the
information model);
• each news item is categorized (through use of a
folksonomy);
• each post can be directly shared, emailed, added
to favorites, and liked;
• news can be searched, sorted, and filtered; and
• important news items stay at the top to highlight
main announcements.
Such news can be further spread through a news
ticker such as RSS feeds and Twitter. An RSS feed is
used to broadcast news. Its purpose is to regularly
remind the user of the existence of a particular
activity and the fact that it is producing interesting
results. Twitter is a widely used means of
disseminating news, however, significantly limits
the length of it. Finally, Email and Email lists are
also well established means for news dissemination.
Especially the latter are a proven means of
broadcasting information and facilitating group
discussions. Other ways of spreading news are
through social networking sites, which will be
discussed below. Chatting is another form of
instantly communicating and disseminating
information, and a blog could be used to inform
partners and members of recent trends in the field of
semantic technologies.
Sharing. There are a large number of Web 2.0
websites that support the sharing of information
items such as: bookmarks, images, slides, and
videos, etc.
Collaboration. A wiki is primarily a means for
project internal collaboration. However, it also
becomes a dissemination channel if external visitors
have read access. They may then follow the
intensive internal interaction that can help to gain a
better and more detailed understanding of externally
published results and achievements.
Group Communication. Facebook as a social
networking site provides an additional community
aspect, i.e., it forms a community that multi-
directionally shares news, photos, opinions, and
other important aspects. Notice that Facebook is
actually not only one, but several channels. It offers
more than 40 possibilities through which to
disseminate information.
These can also be tightly
integrated into Web 1.0 pages, such as that of the
New York Times.
Google+ may have the potential
to become a major competitor of Facebook.
Therefore, it should also be included in a social
networking site strategy. LinkedIn and Xing are
focused on professional use and perfectly fit the
purpose of research organizations.
Semantic-based Dissemination. An important
approach to broaden the scope of a dissemination
activity is to add machine-processable semantics to
the information. With this approach, search and
aggregation engines can provide much better service
in finding and retrieving this information. Semantic
annotations injected in websites are used by search
engines such as Google to provide a structured
presentation of the contents of websites. “This data
may be embedded within enhanced search engine
results, exposed to users through browser
extensions, aggregated across websites or used by
scripts running within those HTML pages.”
(Tennison, 2012).
There are various formats of adding machine-
processable semantics to data. First, there are three
competing means of including semantics directly in
HTML/XML files: (1) RDFa adds a set of attribute-
level extensions to XHTML enabling the embedding
of RDF triples; (2) Microformats directly use meta
tags of XHTML to embed semantic information in
web documents; (3) Microdata use HTML5
elements to include semantic descriptions into web
documents aiming to replace RDFa and
EffectiveandEfficientOnlineCommunication-TheChannelModel
211
Microformats. For the moment, we have three
competing proposals that should be supported in
parallel until one of them can take a dominant role
on the web. RDFa integrates best with the W3C
metadata stack built on top of RDF. However, this
also seems to hamper the uptake of this technology
by many webmasters that are not familiar with this
technology stack. Therefore, Microformats were
developed as a competing approach directly using
some existing HTML tags to include meta data in
HTML documents. Actually, they overload the class
tag which causes problems for some parsers as it
makes semantic information and styling markup
hard to differentiate. Therefore, Microdata instead
introduce new tag attributes to include semantic data
into HTML. As the use of RDFa has increased
rapidly, whereas the deployment of microformats in
the same period has not advanced remarkably, we
are focusing on RDFa and Microdata in our
dissemination approach.
Instead of including semantic annotations in
XHTML documents, i.e., injecting machine-readable
contents into content that is meant for direct human
consumption, they can also be provided for direct
machine consumption. A straight-forward way is to
publish an RDF file containing the machine readable
data. Instead of directly publishing an RDF file you
can also provide a SPARQL endpoint allowing the
querying RDF information. Instead of retrieving the
entire RDF file, directed queries can be supported
with this approach
In addition to predefined formats and technical
means, we need to reuse predefined LOD
vocabularies to describe our data to enable
semantic-based retrieval of information.
Currently,
we use Dublin Core, FOAF, GoodRelations, and
schema.org.
3 RELATED WORK
Here we relate and compare to the two closest sub-
areas of semantic technologies: ontology-based
content management systems (CMSs) for web sites
and semantic matchmaking of content sender and
receiver.
The field of semantics-based or enhanced CMSs
has already been quite thoroughly explored. One of
the earlier approaches to ontology-based website
management is the OntoWebber system described in
(Jin et al., 2001). The proposed three-way approach
of “explicit modeling of different aspects of
websites", “the use of ontologies as foundation for
web portal design", and “semi-structured data
technology for data integration and website
modeling" presents an early but comprehensive
approach to semantifying CMSs. OntoWebber
introduces an integration layer which adapts to
different data sources. In contrast, our approach
adapts to different channels rather than to different
information sources. A year later, Sheth et al. (Sheth
et al., 2002) introduced the SCORE system which
defines four key features: semantic organization and
use of metadata, semantic normalization, semantic
search, and semantic association. Although written
in the early days of the Semantic Web, the paper
covers topics such as metadata extraction from
unstructured text and automatic classification that
may also become relevant to our approach.
Continuing their earlier work on the Knowledge
Web portal, (Garcia et al. 2008) introduce “The
Rhizomer Semantic Content Management System”
which integrates services with metadata browsing,
editing, and uploading. (Corlosquet et al, 2009)
proposes a Linked Data extension for Drupal that
enables content annotation with RDFa and provides
a SPARQL endpoint. The British national
broadcaster BBC started to integrate semantic
technologies (i.e. Linked Data) in 2009 in order to
integrate various data and content sources
distributed throughout the enterprise (Kobilarov et
al., 2009). As a result, as reported in (Bishop et al.,
2010), BBC's World Cup 2010 site is based on
semantic repositories that enable the publishing of
metadata about content rather than publishing the
content itself. While the data input is fixed, different
schemas for the output are defined. However, as
only one channel for output is considered, the
mapping performed is quite straightforward. In
contrast, our system accounts for different
information needs of various and heterogeneous
channels and therefore enables the distribution of
content through different portals.
Semi-automatic matchmaking is a well studied
field in Artificial Intelligence and related areas.
Obviously, we can only select a small sample of
approaches in this area, which focus on
matchmaking in regard to content. (Katzagiannaki &
Plexousakis, 2003) presents a selective information
dissemination system that is based on semantic
relations. In their paper, the terms in user profiles
and terms in documents are matched through
semantic relations that are defined using a thesaurus.
Similarly, the approach taken by (Morales-del-
Castillo et al., 2009) introduces selective
dissemination of information for digital libraries
based on matching information items to user
profiles. Obviously, user profiles correspond to our
DATA2012-InternationalConferenceonDataTechnologiesandApplications
212
channels, however, we instead manually model their
relationship with contents. The system introduced in
(Ma et al., 2006) uses RDF, OWL, and RSS in order
to introduce an efficient publish/subscribe
mechanism that includes an event matching
algorithm based on graph matching. Our approach,
in contrast, matches information items to channels
rather than events to users. Also, instead of graph
matching, we use predefined weavers for channel
selection. While (Morales-del-Castillo et al., 2009)
uses fuzzy linguistic modeling and NLP techniques
for semiautomatic thesaurus generation and perform
a matching based on statistical analysis, we use
semantics to manually define the connections
between information items and the channels.
Since we aim for high precision and
professionalism in on-line communication, we see
little use for statistical based semantic methods
(natural language understanding, information
extraction, etc.). We want to allow the user to
abstract from the content-channel level to the
content level. However, as we expand towards a
full-fledged value management approach that
monitors the entire web space for important
statements, such methods will be needed.
Fortunately, a large number of such web analytical
toolkits already exist (Kasper et al., 2010).
4 FUTURE WORK
Introducing a semantic layer on top of
communication channels is required to enable a
framework that allows common value management.
This combination of research fields opens a broad
variety of new challenges yet to be solved, in
particular, as follows:
Modeling and Interweaving Feedback. Feedback is
an important part of all effective communication.
Without feedback, the sender - the one who intends
to convey information - has no means to validate
whether or not the recipient received or understood
the message. It is also often preferable to have a
fully-fledged two-way conversation instead of
simple one-way broadcasting. The Web 2.0
revolution made it ridiculously easy for everybody
to use the Internet as a two-way conversation
platform where they can provide feedback as well as
react to what was said. Therefore, it will be
necessary to model feedback and interweave it with
content items that we previously published.
Modeling Target Groups. Companies that pursue
common value management usually have a very
restricted target group of people they want to
address. So far in our channel model we do not
distinguish between different target groups in
different channels. However, different target groups
reside on different communication platforms, even
though there is some overlap. For example, you will
find more young and hip people on Facebook, and
more professional users on Xing or LinkedIn, but
there are quite a few users that have a profile on both
platforms. Nonetheless, they expect a different way
of being engaged in different platforms.
Adapting Content. Adapting content is a two part
problem (both of which can be commonly solved):
first, converting an information item into different
formats or, second, automatable transformations
such as extracting images, videos or extracting and
shortening web links from piece of content, and
transforming multimedia content into a different
format. However, adapting content in a way that
requires creativity and human intelligence is still a
strenuous problem that reaches the borders of
computability. Examples of such adaptations are
shortening or translating an essay, or rewriting a text
in a way that matches the target group it addresses.
Crowdturfing and Trust. Crowd sourcing solutions
are very capable of dealing with hard to compute
problems. However, this also allows for harming
platforms that only defend themselves against
automated attacks. Malicious activities, such as
shaping opinions of a large number of people via
social media platforms, and the use of crowd
sourcing platforms are becoming more and more
popular (Wang et al., 2011). That is why some
mechanisms of trust have to be introduced, which is
also tightly connected to reputation management. A
non-trustworthy source may communicate anything
they want, the effect will be very little and its value
drastically decreased.
Quality Management. An important part of targeted
communication is assessing and improving the
quality of conveyed content. Whereas trust,
reputation and brand management are influenced by
how information is perceived, quality assurance is
an inbound process. The business processes for
quality management and what this actually mean
have yet to be defined for common value
management. The bigger the campaign is, the more
visible the effect of proper quality management.
Quantification of Brand and Reputation. Similar to
the quantification of social values, brand and
reputation also have to get a countable unit. It will
be very challenging to find fitting metrics, since
measures already in existence such as brand equity
EffectiveandEfficientOnlineCommunication-TheChannelModel
213
are considered meaningful by a small share of
marketing professionals (Farris et al., 2010). The
combination with social media and the possibilities
of sentiment analysis allows more suitable metrics to
be introduced next to the existing ones.
Enrichment of Yield Management. Yield
management is based on statistical analysis of
different parameters, such as pricing, capacity, and
demand (Kimes, 2000). Already established
calculation models can be extended by channel
based reputation, brand value and other, yet to be
introduced criteria. For example, in a
communication channel where a product’s brand (or
the product itself) has little reputation and is badly
represented, the price of the delivered product could
be less than in other channels where it is reputable.
5 CONCLUSIONS
As a main contribution of this paper, we have
developed a channel model for an approach to
efficient and effective communication, applicable to
the multi-channel dissemination conditions of Web
2.0 and Web 3.0 a.k.a. Semantic Web. The approach
as a whole facilitates and automates communication
for the communication managers, saving them time
and money, as well as reducing the information
overload for the information consumers.
The following core features characterize our
channel modeling principles:
• We use ontologies to model content in order to
have a representation layer independent from the
communication channel. We want to achieve reuse
of content over channels, allowing small
organizations to deal with an increasing number of
communication channels and exploit their potential.
These ontologies model certain vertical domains
such as research projects, associations,
accommodations, restaurants, bars, touristic events
and services, etc. Thus, the ontologies and their
channel alignments can be reused at large scale,
providing a quick return of the investment necessary
to build and maintain them.
• Our approach is bi-directional, i.e., in the same
way that we disseminate through concepts we use
these concept to aggregate feedback and impact
found in various channels.
• We support in an integrated fashion the
dissemination via traditional web channels, web 2.0
means, and semantic based channels, using various
formats and vocabularies.
In order to broaden the proof of our claims on
scalability and reusability of content and ontologies,
we are currently performing case studies. First, we
use our approach in the dissemination of other
research projects and associations. Second, we are
entering more commercial areas such as eTourism,
where the demand for a scalable dissemination
strategy is supported by millions of hotels, given the
fact that soon the majority of room bookings will be
done on-line.
ACKNOWLEDGEMENTS
This work has been partially supported by EU
projects RENDER and PlanetData.
REFERENCES
D. C. Barlund: A transactional mode of communication. In
C. D. Mortensen (eds.), Communication Theory (2
nd
ed.), New Jersey Transaction, 2008.
B. Bishop, A. Kiryakov, D. Ognyanoff, I. Peikov, Z.
Tashev, and R. Velkov: OWLIM: A family of scalable
semantic repositories, Technical report, 2010.
S. Corlosquet, R. Delbru, T. Clark, A. Polleres, and S.
Decker: Produce and consume linked data with
Drupal, Springer Constraints Journal, 1380:763-778,
2009.
P. W. Farris, N. T. Bendle, P. E. Pfeifer, and D. J.
Reibstein: Marketing Metrics: The Definitive Guide to
Measuring Marketing Performance (2nd Ed.), Pearson
Education, 2010.
R. Garcia, J. M. Gimeno, F. Perdrix, R. Gil, and M. Oliva:
The Rhizomer Semantic Content Management
System. In Proceedings of the 1st World Summit on
The Knowledge Society: Emerging Technologies and
Information Systems for the Knowledge Society,
WSKS’'08, pages 385-394, Springer, 2008.
Y. Jin, S. Decker, and G. Wiederhold. Ontowebber:
Modeldriven ontology-based web site management. In
Proceedings of the Semantic Web Working Symposium
(SWWS), Stanford University, 2001.
H. Kasper, M. Dausinger, H. Kett, and T. Renner:
Marktstudie Social Media Monitoring Tools,
Frauenhofer IAO Studie, 2010.
I.-E. Katzagiannaki and D. Plexousakis: Information
dissemination based on semantic relations. In CAiSE
Short Paper Proceedings'03, 2003.
S. E: Kimes: A strategic approach to yield management. In
A. Ingold, U. McMahon-Battie, and I. Yeoman (eds.),
Yield Management: Strategies for the Service Industry,
International Thomson Business Press, 2000.
G. Kobilarov, T. Scott, Y. Raimond, S. Oliver, C.
Sizemore, M. Smethurst, C. Bizer, and R. Lee: Media
meets semantic web: how the BBC uses dbpedia and
linked data to make connections. In Proceedings of
DATA2012-InternationalConferenceonDataTechnologiesandApplications
214
European Semantic Web Conference (ESWC 2009),
LNCS 5554, pp. 723-737, 2009.
J. Ma, G. Xu, J. L. Wang, and T. Huang: A semantic
publish/subscribe system for selective dissemination
of the RSS documents. In Proceedings of the Fifth
International Conference on Grid and Cooperative
Computing (GCC'06), pp. 432-439, 2006.
J. M. Morales-del-Castillo, R. Pedraza-Jimenez, A. A.
Ruiz, E. Peis, and E. Herrera-Viedma: A semantic
Model of Selective Dissemination of Information for
Digital Libraries, Information Technology and
Libraries, 28(1):21-31, 2009.
S. Mulpuru, H. H. Harteveldt, and D. Roberge: Five Retail
eCommerce Trends to Watch In 2011, Forrester
Research Report, January 31, 2011.
A. Sheth, C. Bertram, D. Avant, B. Hammond, K. Kochut,
and Y. Warke: Managing semantic content for the
web, IEEE Internet Computing, 6:80-87, July 2002.
J. Tennison: HTML Data Guide, W3C Editor’s Draft 02
March 2012.
EffectiveandEfficientOnlineCommunication-TheChannelModel
215
