Chapter 8
Conclusion and Outlook
Grigoris Antoniou
Frank van Harmelen
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Chapter 8
A Semantic Web Primer
Lecture Outline
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Which Semantic Web?
Four Popular Fallacies
Current Status
Selected Key Research Challenges
Chapter 8
A Semantic Web Primer
Interpretation 1: The Semantic Web as
the Web of Data
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The main aim of the Semantic Web is to enable
the integration of structured and semistructured
data sources over the Web
The main recipe is to expose datasets on the
Web in RDF format and to use RDF Schema to
express the intended semantics of these
datasets
A typical use is the combination of geodata with
a set of consumer ratings for restaurants in order
to provide an enriched information source
Chapter 8
A Semantic Web Primer
Interpretation 2: The Semantic Web as
Enrichment of the Current Web
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The aim of the Semantic Web is to improve
the current World Wide Web
Typical uses are :
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Improved search engines
Dynamic personalization of Web sites
Semantic enrichment of existing Web pages
Chapter 8
A Semantic Web Primer
Interpretation 2: The Semantic Web as
Enrichment of the Current Web (2)
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The sources of the required semantic metadata
are mostly claimed to be automated sources
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Concept extraction
Named-entity recognition
Automatic classification
More recently the insight is gaining ground that
the required semantic markup can also be
produced by social mechanisms in communities
that provide large-scale human-produced
markup
Chapter 8
A Semantic Web Primer
Lecture Outline
1.
2.
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4.
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Which Semantic Web?
Four Popular Fallacies
Current Status
Selected Key Research Challenges
Chapter 8
A Semantic Web Primer
1. The semantic Web tries to enforce
meaning from the top
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This fallacy claims that the Semantic Web
enforces meaning on users through its
standards OWL and RDFS
The only meaning that OWL and RDFS enforce
is the meaning of the connectives in a language
in which users can express their own meanings
Users are free to choose their own vocabularies
and to describe whatever domains the choose
Chapter 8
A Semantic Web Primer
1. The semantic Web tries to enforce
meaning from the top (2)
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The situation is comparable to HTML:
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HTML does not enforce the lay-out of web-pages
“from the top”
All HTML enforces is the language that people
can use to describe their own lay-out
HTML has shown that such an agreement on the
use of a standardized language is a necessary
ingredient for world-wide interoperability
Chapter 8
A Semantic Web Primer
2. Everybody must subscribe to a single
predefined meaning for the terms they use
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The meaning of terms cannot be predefined for
global use in addition meaning is fluid and contextual
The motto of the Semantic Web is not the
enforcement of a single ontology but rather “let a
thousand ontologies blossom”
That is the reason that the construction of mappings
between ontologies is such a core topic in the
Semantic Web community
Such mappings are expected to be partial, imperfect
and context-dependent
Chapter 8
A Semantic Web Primer
3. Users must understand the complicated details of
formalized knowledge representation
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Some of the core technology of the Semantic
Web relies on intricate details of formalized
knowledge representation
The semantics of RDF Schema and OWL
and the layering of the subspecies of OWL
are difficult formal matters
The design of good ontologies is a
specialized area of Knowledge Engineering
Chapter 8
A Semantic Web Primer
3. Users must understand the complicated details of
formalized knowledge representation (2)
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For most users of Semantic Web
applications, such details will remain entirely
behind the scene
Navigation or personalization engines can be
powered by underlying ontologies, expressed
in RDF Schema or OWL, without users ever
being confronted with the ontologies, let
alone their representation languages
Chapter 8
A Semantic Web Primer
4. The semantic Web requires the manual
markup of all existing Web pages
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It is hard enough for most Web site owners to
maintain the human-readable content of their sites
They will certainly not maintain parallel machineaccessible versions of the same information in RDF
or OWL
If that were necessary, it would indeed spell bad
news for the Semantic Web
Instead, Semantic Web application rely on largescale automation for the extraction of such semantic
markup from the sources themselves
Chapter 8
A Semantic Web Primer
Lecture Outline
1.
2.
3.
4.
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Which Semantic Web?
Four Popular Fallacies
Current Status
Selected Key Research Challenges
Chapter 8
A Semantic Web Primer
Four Main Questions
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Where do the metadata come from?
Where do the ontologies come from?
What should be done with the many
ontologies?
Where’s the "Web " in Semantic Web?
Chapter 8
A Semantic Web Primer
Question 1: Where do the metadata
come from?
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Much of the semantic metadata come from
Natural Language Processing and Machine
Learning technology
It is now possible with off-the-shelf
technology to produce semantic markup for
very large corpuses of Web pages by
annotating them with terms from very large
ontologies with sufficient precision and recall
to drive semantic navigation interfaces
Chapter 8
A Semantic Web Primer
Question 1: Where do the metadata
come from? (2)
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More recent is the capability of social
communities to provide large amounts of
human-generated markup
Millions of images with hundreds of million of
manually provided metadata tags are found
on some of the most popular Web 2.0 sites
Chapter 8
A Semantic Web Primer
Question 2: Where do the ontologies
come from?
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The term ontology as used by the Semantic Web
community now covers a wide array of semantic
structures, from lightweight hierarchies such as
MeSH to heavily axiomatized ontologies such as
GALEN
The world is full of such “ontologies”:
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Companies have product catalogs
Organizations have internal glossaries
Scientific communities have their public metadata
schemata
Chapter 8
A Semantic Web Primer
Question 2: Where do the ontologies
come from? (2)
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These have been constructed for other
purposes, most often predating Semantic Web
There are also significant advances in the area
of ontology learning, although results there
remain mixed
Obtaining the concepts of an ontology is feasible
given the appropriate circumstances, but placing
them in the appropriate hierarchy with the right
mutual relationships remains a topic of active
research
Chapter 8
A Semantic Web Primer
Question 3: What should be done with
the many ontologies?
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The Semantic Web crucially relies on the
possibility of integrating multiple ontologies
This is known as the problem of ontology
alignment, ontology mapping, or ontology
integration
Chapter 8
A Semantic Web Primer
Question 3: What should be done with
the many ontologies? (2)
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A wide array of techniques is deployed for
solving this problem
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Ontology-mapping techniques based on natural
language technology
Machine learning
Theorem proving
Graph theory
Statistics
Chapter 8
A Semantic Web Primer
Question 3: What should be done with
the many ontologies? (3)
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Although there are encouraging results, this
problem is by no means solved
Automatically obtained results are not yet
good enough in terms of recall and precision
to drive many of the intended Semantic Web
use cases
Ontology mapping is seen by many as the
Achilles’ heel of the Semantic Web
Chapter 8
A Semantic Web Primer
Question 4: Where’s the "Web " in
Semantic Web?
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Semantic Web has sometimes been criticized as
being too much about “semantic” and not
enough about “Web”
This was perhaps true in the early days of
Semantic Web development, when there was a
focus on applications in rather circumscribed
domains like intranets
The main advantage of company intranets is that
the ontology-mapping problem can be avoided
Chapter 8
A Semantic Web Primer
Question 4: Where’s the "Web " in
Semantic Web? (2)
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Recent years have seen a resurgence in the
Web aspects of Semantic Web applications
A prime example is the deployment of FOAF
technology, and of semantically organized
P2P systems
The Web is more than just a collection of
textual documents
Chapter 8
A Semantic Web Primer
Question 4: Where’s the "Web " in
Semantic Web? (3)
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Nontextual media such as images and
videos are an integral part of the Web
For the application of Semantic Web
technology to such nontextual media we
currently rely on human-generated semantic
markup
Deriving annotations through intelligent
content analysis in images and videos is
under way
Chapter 8
A Semantic Web Primer
Main Application Areas
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Looking at industrial events, either dedicated
events or co-organized with the major
international scientific Semantic Web
conferences, we observe that a healthy uptake
of Semantic Web technologies is beginning to
take shape in the following areas :
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Knowledge management, mostly in intranets of large
corporations
Data integration (Boeing, Verizon, and other)
E-science, in particular the life sciences
Convergence with Semantic Grid
Chapter 8
A Semantic Web Primer
Main Application Areas (2)
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If we look at the profiles of companies active in this area, we
see a transition from small start-up companies such as
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To large vendors such as
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Aduna
Ontoprise
Network Inference
Top Quadrant
IBM (Snobase ontology Management System)
HP (Jena RDF platform)
Adobe (RDF-based XMP metadata framework)
Oracle (support for RDF storage and querying in their datavase
product)
Chapter 8
A Semantic Web Primer
Main Application Areas (3)
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However, there is a noticeable lack of uptake
in some other areas. In particular, the
promise of the Semantic Web for
Pesonalization
– Large-scale semantic search (on the scale of the
World Wide Web)
– Mobility and context-awareness
is largely unfulfilled
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Chapter 8
A Semantic Web Primer
Main Application Areas (4)
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A difference that seems to emerge between
the successful and unsuccessful application
areas is that the successful are all aimed at
closed communities, whereas the
applications aimed at the general public are
still in the laboratory phase at best
The underlying reason for this could well be
the difficulty of the ontology mapping
Chapter 8
A Semantic Web Primer
Lecture Outline
1.
2.
3.
4.
29
Which Semantic Web?
Four Popular Fallacies
Current Status
Selected Key Research Challenges
Chapter 8
A Semantic Web Primer
Selected Key Research Challenges (1)
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Several challenges that were outlined in
2002 article by van Harmelen have become
active areas of research:
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Scale inference and storage technology, now
scaling to the order of billions of RDF triples
Ontology evolution and change
Ontology mapping
Chapter 8
A Semantic Web Primer
Selected Key Research Challenges (2)
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A number of items on the research agenda, though
hardly developed, have had a crucial impact on the
feasibility of the Semantic Web vision:
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Interaction between machine-processable representations
and the dynamics of social networks of human users
Mechanisms to deal with trust, reputation, integrity and
provenance in a semiautomated way
Inference and query facilities that are sufficiently robust to
work in the face of limited resources (computational time,
network latency, memory, or storage space) and that can
make intelligent trade-off decisions between resource use
and output quality
Chapter 8
A Semantic Web Primer