SWT Lecture Session 1 - Introduction
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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.
SWT Lecture Session 1 - Introduction
- 1. + Semantic Web Technologies 2012-2013 Part I Mariano Rodriguez-Muro, Free University of Bozen-Bolzano
- 2. + Disclaimer License This work is licensed under the Creative Commons Attribution-Share Alike 3.0 License http://creativecommons.org/licenses/by-sa/3.0/
- 3. + Intro Course organization Intro to Semantic Web Intro to Semantic Technologies
- 5. + 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.
- 6. + 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
- 8. + 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.
- 9. + 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)
- 11. + Web of Documents Primary objects: documents Degree of structure in data: low Semantics of content:Implicit Designed for: human consumption Links between documents
- 12. + Web of documents: The problem
- 13. + Example: Elvis
- 16. + 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!
- 17. + 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
- 18. + 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
- 19. + Web of data
- 21. + 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.
- 22. + 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?
- 23. + 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
- 24. + 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
- 25. + 25 1st: Export your data as a set of relations
- 26. + 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
- 27. + 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 + 28
- 29. 3rd: start merging your data + 29
- 30. 3rd: start merging your data (cont‟d) + 30
- 31. 4th: Merge identical resources + 31
- 32. + 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 + 33
- 34. + 34 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 + 35
- 36. + 36 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
- 38. + 38 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
- 42. + 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
- 43. + 43 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
- 44. + 44 What did we do? (cont‟d)
- 45. + 45 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
- 46. + 46 So where is the Semantic Web? Semantic Web technologies make such integration possible
- 47. + Semantic Technologies Today: Applications, Use cases, Technologies, Systems
- 48. + Web of data today
- 49. + Semantics today Linked-in Schema.org Good-relations Oracle (Server) IBM (DB2, Watson) Apple (Siri) SAP Evri, Linked-in, many startups Many deployed systems
- 50. + 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 (LOD)
- 51. + 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
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- #20: We need A data modelA query languageStandards and tools to publish the dataStandards and tools to consume the data
- #49: Big players are betting on this