This document provides an introduction and overview of a course on Semantic Web Technologies taught by Professor Mariano Rodriguez-Muro. It outlines the course organization, topics to be covered, reading materials, and grading structure. It also introduces key concepts in the Semantic Web and semantic technologies, including using semantic data integration to merge datasets and answer complex queries.

1. +
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

Course organization

Intro to Semantic Web

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:

Techniques for query answering optimization

SPARQL, Big RDFS, virtual RDF

Data integration with Semantic Tech and SemTech in the
enterprise.

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

7. +
Course organization (Part I)

Website:


Moodle


…
Schedule




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


With appointment
Please use forums as main means of comunication

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Reference Material

Slides, Papers

Foundations of Semantic Web. Pascal HItzler, Markus
Krotzsch and Sebastian Rudolph. Chapman & Hall/CRC, 2010.
(Code FSW)

Semantic Web Programming. John Hebeler et. al. Wiley.
2009. (Code SWP)

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

Part I 50%, Part II 50%

Grading Part I


Lab exercises: 15%
Mid-term: 35%

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.

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

Primary objects: documents

Degree of structure in data: low

Semantics of content:Implicit

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

How about this query:


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:


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

Primary objects: things
Links between: things

Degree of Structure: high

Explicit semantics of contents and links

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

The semantic web vision has generated technologies that are
applied outside the web context including:


Government

Research (Bio, Geo, Cultural heritage, etc.)

Software development


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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22
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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23
The rough structure of data
integration with SWT
Map the various data onto an abstract data representation
1.
•
Make the data independent of its internal representation…
2.
Merge the resulting representations
3.
Start making queries on the whole
•
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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25
1st: Export your data as a set of
relations

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26
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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27
Data set “F”: Another book store‟s
data
A
1
7
11
12
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

28. 2nd: Export your second set of data
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28

29. 3rd: start merging your data
+
29

30. 3rd: start merging your data (cont‟d)
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30

31. 4th: Merge identical resources
+
31

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32
Start making queries…

User of data set “F” can now ask queries like:

“What is the title of the original version of Le Palais des miroirs?”

This information is not in the data set “F”...

…but can be retrieved after merging with data set “A”!

33. 5th: Query the merged data set
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33

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

35. 3rd revisited: Use the extra knowledge
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35

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

37. 6th: Richer queries
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37

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

39. 7th: Merge with Wikipedia data
+
owl:sameAs
39

40. 7th (cont‟d): Merge with Wikipedia data
+
owl:sameAs
40

41. 7th (cont‟d): Merge with Wikipedia data
+
owl:sameAs
41

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42
Is that surprising?

It may look like it but, in fact, it should not be…

What happened via automatic means is done every day by
Web users!

The difference: a bit of extra rigour so that machines could do
this, too

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What did we do?

We combined different data sets that


...are of different formats (RDBMS, Excel spreadsheet, (X)HTML, etc)


...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

i.e. the ISBNs in this case

We could add some simple additional information (the “glue”) to
help further merge data sets

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…

…the graph representation is independent of the details of the
native structures

…a change in local database schemas, HTML structures, etc.
do not affect the whole

“schema independence”

…new data, new connections can be added seamlessly &
incrementally

… it doesn‟t matter if you are at the enterprise level or at the
web level

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46
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

Linked-in

Schema.org

Good-relations

Oracle (Server)
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IBM (DB2, Watson)
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Apple (Siri)
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SAP

Evri, Linked-in, many startups

Many deployed systems

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

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

52. +
Reading material

PTSW Chapter 1

SWP Part I, Chapter 1

FTW Section 1.4