SWT Lecture Session 1 - Introduction

+

Semantic Web Technologies 2012-2013Part I

Mariano Rodriguez-Muro, Free University of Bozen-Bolzano

+Disclaimer

License

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

+Intro

Course organization

Intro to Semantic Web

Intro to Semantic Technologies

+

Course organization

+About me

Research interests:

Techniques for query answering optimization

SPARQL, Big RDFS, virtual RDF

Data integration with Semantic Tech and SemTech in the enterprise.

Mariano Rodríguez-MuroAssistant Professor at KRDBFaculty of computer Science (POS Building, 202)Tel. +390471016228rodriguez =at= inf.unibz.it

+About you

Which program?

Which semester?

Why are you here? Topic is mandatory Topic relates to my area Looking for project/thesis? Just Interesting? Need some credits?

Special interests?

+Course organization (Part I)

Website: http://rodriguez-muro.com/courses/index.php?title=SWT12

Moodle …

Schedule 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

+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.

+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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IntroductionSemantic Web

+Web of Documents

Primary objects: documents Links between documents

Degree of structure in data: low

Semantics of content:Implicit

Designed for: human consumption

+Web of documents: The problem

+Example: Elvis

+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!

+

The VisionI 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 ma- chines. The intelligent agents people have touted for ages will finally materialize.

Barners-Lee, 1999

+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

+Web of data

+

Semantic Technologies

+Not only about the web

The semantic web vision has generated technologies that are applied outside the web context including: Enterprise intelligence Government Research (Bio, Geo, Cultural heritage, etc.) Software development …

Semantic technologies provide flexible and powerful tools to accomplish things that were not possible or not practical in the past.

+Introduction to the Semantic Web approach

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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

1. Map the various data onto an abstract data representation

• 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

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ID Author Title Publisher Year

ISBN0-00-651409-X

id_xyz The Glass Palace id_qpr 2000

ID Name Home page

id_xyz Ghosh, Amitav http://www.amitavghosh.com

ID Publisher Name

City

id_qpr Harper Collins London

Books

Authors

Publishers

+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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+

A B D E

1 ID Titre Original

2

ISBN0 2020386682 A13 ISBN-0-00-651409-X

3

6 ID Auteur7 ISBN-0-00-651409-X A12

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12

13

TraducteurLe Palais des miroirs

NomGhosh, AmitavBesse, Christianne

Data set “F”: Another book store’s data

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+2nd: Export your second set of data

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+3rd: start merging your data 29

+3rd: start merging your data (cont’d) 30

+4th: Merge identical resources 31

+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”!

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+5th: Query the merged data set 33

+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

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+3rd revisited: Use the extra knowledge 35

+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

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+6th: Richer queries 37

+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

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+7th: Merge with Wikipedia data 39

owl:sameAs

+7th (cont’d): Merge with Wikipedia data 40

owl:sameAs

+7th (cont’d): Merge with Wikipedia data41

owl:sameAs

+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 ...may be internal or somewhere on the Web ...are of different formats (RDBMS, Excel spreadsheet, (X)HTML,

etc) ...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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+So where is the Semantic Web?

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Semantic Web technologies make such integration possible

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Semantic TechnologiesToday: Applications, Use cases, Technologies, Systems

+Web of data today

+Semantics today

Linked-in

Schema.org

Good-relations

Oracle (Server)

IBM (DB2, Watson)

Apple (Siri)

SAP

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, N-Triples) 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 (LOD)

+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)

+Reading material

PTSW Chapter 1

SWP Part I, Chapter 1

FTW Section 1.4