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Semantic Web
Technologies 2012-2013
Part I
Mariano Rodriguez-Muro,
Free University of Bozen-Bolzano

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Disclaimer
License
This work is licensed under the
Creative Commons Attribution-Share Alike 3.0 License
http://creativecommons.org/licenses/by-sa/3.0/

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Intro
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Course organization
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Intro to Semantic Web
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Intro to Semantic Technologies

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Course organization

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About me
Mariano Rodríguez-Muro
Assistant Professor at KRDB
Faculty of computer Science (POS Building, 202)
Tel. +390471016228
rodriguez =at= inf.unibz.it
Research interests:
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Techniques for query answering optimization
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SPARQL, Big RDFS, virtual RDF
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Data integration with Semantic Tech and SemTech in the
enterprise.

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About you
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Which program?
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Which semester?
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Why are you here?
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Topic relates to my area
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Looking for project/thesis?
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Just Interesting?
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Topic is mandatory
Need some credits?
Special interests?

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Course organization (Part I)
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Website:
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Moodle
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…
Schedule
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http://rodriguez-muro.com/courses/index.php?title=SWT12
Lecture: Tuesday:10:30 am to 12:30 pm
Lecture: Thursday 8:30 am to 10:30 am
Lab: Tuesday 2:00 to 4:00 pm
Office Hours
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With appointment
Please use forums as main means of comunication

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Reference Material
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Slides, Papers
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Foundations of Semantic Web. Pascal HItzler, Markus
Krotzsch and Sebastian Rudolph. Chapman & Hall/CRC, 2010.
(Code FSW)
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Semantic Web Programming. John Hebeler et. al. Wiley.
2009. (Code SWP)
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Programming the Semantic Web. Toby Segaran, Colin Evans
and Jamie Taylor. O‟Reilly. 2009. (Code PTSW)
Available at the library. SWP and PTSW available as ebooks.

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Grading
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Part I 50%, Part II 50%
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Grading Part I
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Lab exercises: 15%
Mid-term: 35%
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Exercises: Each week a new assignment. All assignments are
graded. All assignments are mandatory. Delivery must be
done by the next week. Java and SQL/JDBC is required.
Projects must be packaged with Maven.
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Midterm. Covers all material seen during the lectures. From
slides, presentation and selected book chapters/readings
(marked at the end of each slide)

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Introduction
Semantic Web

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Web of Documents
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Primary objects: documents
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Degree of structure in data: low
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Semantics of content:Implicit
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Designed for: human consumption
Links between documents

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Web of documents: The problem

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Example: Elvis

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Web of data: The problem
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How about this query:
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How many romantic comedy Hollywood movies are directed by a
person who is born in a city that has average temperature above 15
degrees!?
You need to:
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Find reliable sources containing facts about movies (genre &
director), birthplaces of famous artists/directors, average
temperature of cities across the world, etc.
 The result: several lists of thousands of facts
Integrate all the data, join the facts that come from heterogeneous
sources
Even if possible, it may take days to answer just a single query!

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The Vision
I have a dream for the Web in which computers
become capable of analyzing all the data on the
Web - the content, links, and transactions
between people and computers. A Semantic
Web, which should make this possible, has yet
to emerge, but when it does, the day-to-day
mechanisms of trade, bureaucracy and our daily
lives will be handled by machines talking to machines. The intelligent agents people have
touted for ages will finally materialize.
Barners-Lee, 1999

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The semantic web
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Primary objects: things
Links between: things
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Degree of Structure: high
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Explicit semantics of contents and links
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Designed for both machines and humans

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Web of data

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Semantic Technologies

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Not only about the web
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The semantic web vision has generated technologies that are
applied outside the web context including:
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Government
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Research (Bio, Geo, Cultural heritage, etc.)
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Software development
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Enterprise intelligence
…
Semantic technologies provide flexible and powerful tools to
accomplish things that were not possible or not practical in the
past.

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Introduction to the Semantic Web
approach
How does a Semantic Web approach help us
merge data sets, infer new relations, and
integrate outside data sources?

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The rough structure of data
integration with SWT
Map the various data onto an abstract data representation
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Make the data independent of its internal representation…
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Merge the resulting representations
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Start making queries on the whole
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Queries not possible on the individual data sets

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Data set “A”: A simplified book store
Books
ID
Author
ISBN0-00-651409-X id_xyz
Authors
ID
Title
The Glass Palace
Name
id_xyz
Ghosh, Amitav
Publishers
ID
Harper Collins
id_qpr
Home page
Publisher Name
id_qpr
Publisher
http://www.amitavghosh.com
City
London
Year
2000
24

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1st: Export your data as a set of
relations

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Some notes on the data export
Data export does not necessarily mean physical conversion of
the data
Relations can be virtual, generated on-the-fly at query time
via SQL “bridges”
scraping HTML pages
extracting data from Excel sheets
etc.
One can export part of the data

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Data set “F”: Another book store‟s
data
A
1
7
11
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13
D
E
ID
ISBN0 2020386682
Traducteur
Titre
Original
Le Palais A13
ISBN-0-00-651409-X
des
miroirs
ID
ISBN-0-00-651409-X
Auteur
A12
2
3
6
B
Nom
Ghosh, Amitav
Besse, Christianne

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2nd: Export your second set of data
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3rd: start merging your data
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3rd: start merging your data (cont‟d)
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4th: Merge identical resources
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Start making queries…
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User of data set “F” can now ask queries like:
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“What is the title of the original version of Le Palais des miroirs?”
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This information is not in the data set “F”...
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…but can be retrieved after merging with data set “A”!

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5th: Query the merged data set
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However, more can be achieved…
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We “know” that a:author and f:auteur are really the same
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But our automatic merge does not know that!
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Let us add some extra information to the merged data:
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a:author is equivalent to f:auteur
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Both identify a Person, a category (type) for certain resources
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a:name and f:nom are equivalent to foaf:name

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3rd revisited: Use the extra knowledge
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Start making richer queries!
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User of data set “F” can now query:
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“What is the home page of Le Palais des miroirs’s „auteur‟?”
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The information is not in data set “F” or “A”…
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…but was made available by:
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Merging data sets “A” and “F”
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Adding three simple “glue” statements

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6th: Richer queries
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Bring in other data sources
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We can integrate new information into our merged data set
from other sources
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e.g. additional information about author Amitav Ghosh
Perhaps the largest public source of general knowledge is
Wikipedia
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Structured data can be extracted from Wikipedia using dedicated
tools
May 12, 2009

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7th: Merge with Wikipedia data
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owl:sameAs
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7th (cont‟d): Merge with Wikipedia data
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owl:sameAs
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7th (cont‟d): Merge with Wikipedia data
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owl:sameAs
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Is that surprising?
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It may look like it but, in fact, it should not be…
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What happened via automatic means is done every day by
Web users!
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The difference: a bit of extra rigour so that machines could do
this, too

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What did we do?
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We combined different data sets that
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...are of different formats (RDBMS, Excel spreadsheet, (X)HTML, etc)
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...may be internal or somewhere on the Web
...have different names for the same relations
We could combine the data because some URIs were identical
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i.e. the ISBNs in this case
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We could add some simple additional information (the “glue”) to
help further merge data sets
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The result? Answer queries that could not previously be asked

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What did we do? (cont‟d)

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The abstraction pays off because…
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…the graph representation is independent of the details of the
native structures
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…a change in local database schemas, HTML structures, etc.
do not affect the whole
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“schema independence”
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…new data, new connections can be added seamlessly &
incrementally
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… it doesn‟t matter if you are at the enterprise level or at the
web level

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So where is the Semantic Web?
Semantic Web technologies make such integration possible

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Semantic Technologies
Today: Applications, Use cases, Technologies, Systems

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Web of data today

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Semantics today
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Linked-in
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Schema.org
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Good-relations
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Oracle (Server)
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IBM (DB2, Watson)
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Apple (Siri)
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SAP
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Evri, Linked-in, many startups
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Many deployed systems

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Semantic Web Technologies
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A set of technologies and frameworks that enable semantic
data management, data integration and the web of data
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Resource Description Framework (RDF)
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A variety of data interchange formats (e.g., RDF/XML, N3, Turtle, NTriples)
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Semantic languages such as RDF Schema (RDFS) and the Web
Ontology Language (OWL) and Rules (SWRL)
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Query language (SPARQL)
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Software infrastructure (RDF/SPARQL frameworks, Triple stores,
Data integrators, Query engines, Reasoners)
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Publicly available connected dataset and open data initiatives
(LOD)

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SWT Part I
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The Data Model (RDF)
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The query language (SPARQL)
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Software Development (Architecture, Frameworks and Tools)
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A little more semantics (RDFS, inference techniques, tools and
data integration)
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Interacting with the enterprise (Legacy sources, XML, DBMS,
mappings)
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More complex semantics (Rules, data integration and
reasoning with rules)

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Reading material
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PTSW Chapter 1
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SWP Part I, Chapter 1
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FTW Section 1.4