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We need A data modelA query languageStandards and tools to publish the dataStandards and tools to consume the data
Big players are betting on this
SWT Lecture Session 1 - Introduction
Free University of Bozen-Bolzano
This work is licensed under the
Creative Commons Attribution-Share Alike 3.0 License
Intro to Semantic Web
Intro to Semantic Technologies
Assistant Professor at KRDB
Faculty of computer Science (POS Building, 202)
rodriguez =at= inf.unibz.it
Techniques for query answering optimization
SPARQL, Big RDFS, virtual RDF
Data integration with Semantic Tech and SemTech in the
Why are you here?
Topic relates to my area
Looking for project/thesis?
Topic is mandatory
Need some credits?
Course organization (Part I)
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
Please use forums as main means of comunication
Foundations of Semantic Web. Pascal HItzler, Markus
Krotzsch and Sebastian Rudolph. Chapman & Hall/CRC, 2010.
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.
Part I 50%, Part II 50%
Grading Part I
Lab exercises: 15%
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)
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
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
Even if possible, it may take days to answer just a single query!
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.
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
Not only about the web
The semantic web vision has generated technologies that are
applied outside the web context including:
Research (Bio, Geo, Cultural heritage, etc.)
Semantic technologies provide flexible and powerful tools to
accomplish things that were not possible or not practical in the
Introduction to the Semantic Web
How does a Semantic Web approach help us
merge data sets, infer new relations, and
integrate outside data sources?
The rough structure of data
integration with SWT
Map the various data onto an abstract data representation
Make the data independent of its internal representation…
Merge the resulting representations
Start making queries on the whole
Queries not possible on the individual data sets
Data set “A”: A simplified book store
The Glass Palace
1st: Export your data as a set of
Some notes on the data export
Data export does not necessarily mean physical conversion of
Relations can be virtual, generated on-the-fly at query time
via SQL “bridges”
scraping HTML pages
extracting data from Excel sheets
One can export part of the data
Data set “F”: Another book store‟s
Le Palais A13
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”!
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
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
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
Structured data can be extracted from Wikipedia using dedicated
May 12, 2009
7th: Merge with Wikipedia data
7th (cont‟d): Merge with Wikipedia data
7th (cont‟d): Merge with Wikipedia data
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
The difference: a bit of extra rigour so that machines could do
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
The abstraction pays off because…
…the graph representation is independent of the details of the
…a change in local database schemas, HTML structures, etc.
do not affect the whole
…new data, new connections can be added seamlessly &
… it doesn‟t matter if you are at the enterprise level or at the
So where is the Semantic Web?
Semantic Web technologies make such integration possible
Today: Applications, Use cases, Technologies, Systems
IBM (DB2, Watson)
Evri, Linked-in, many startups
Many deployed systems
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)
Query language (SPARQL)
Software infrastructure (RDF/SPARQL frameworks, Triple stores,
Data integrators, Query engines, Reasoners)
Publicly available connected dataset and open data initiatives
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
Interacting with the enterprise (Legacy sources, XML, DBMS,
More complex semantics (Rules, data integration and
reasoning with rules)
PTSW Chapter 1
SWP Part I, Chapter 1
FTW Section 1.4