1. Filip Zavoral, Jiří Dokulil SemWex - KSI MFF UK http://www.ksi.mff.cuni.cz/semwex/ Semantic Web infrastructure Trisolda current state and perspectives 10. Mixer 26.11.2008

2. Semantic web vs. semantization Semantic web vision  Tim Berners-Lee  “The Semantic Web,” Scientific Am. 2001  semantic research generously funded  'hardly one has ever seen...'  New buzzwords  Web 2.0, Web 3.0, Social web, Web of data, Meshups, …  Semantic web died?  no, not yet born  Web Semantization

3. Semantic technologies TCP/IP HTTP HTML Browser Security

4. Technical details

5. Semantic web services

6. Trisolda Motto  'hardly one has ever seen...' the semantic web  data from real life incomplete, duplicated, inaccurate, >20 millions triples  Jena very slow load, over >1 million of triples → crash  Sesame unable to load more then 200 000 triples exponential complexity for loading  where is a working platform for semantic web research? Technology background  Repository – data integration  DataPile

7. Trisolda Trisolda Architecture  Import interfaces  Repository  Querying & Executors

8. Repository Trisolda Repository  Stores incoming data  Retrieves results for queries  Stores used ontology  DataPile structure  holds data in any format Applications server  Not all data and knowledge available when imported  the knowledge is not accurate  Background worker  inferencing  data unifications  reasoner  Framework for plug-ins

9. Import Direct import  data in data sources  converters to the used ontology Crawling wild Web  Egothor  web crawler  AgentMat  parsed pages stored  deductors deduce data and ontology  real life data  incomplete, duplicated, inaccurate Import modes batch insert immediate insert 

10. Querying Query API  Based on simple graph matching  query: set of RDF triples with var.  result: multiset of possible variable mapping – a relation  Not another SQL-like language  set of C++ classes and operators  Query evaluation  levels of support by q engines Query environments  present outputs  examples: rep. browser, RDF visualizer, semantic executors  service composition - conductors

11. AgentMat - data semantization framework

12. AgentMat - data extraction

13. Future work Conclusions  working infrastructure  currently not working - re-deployment, AgentMat & TriQ integration  gathering, storing and querying of semantic data  platform for research and experiments Future work & long-term goals  specialized semantic data storage  semantic acquisition, data semantization  interface-based loosely coupled network of Semantic Web repositories  semantic computing, services, composition, executors...

14. Selected Publications  Beňo, Míšek, Zavoral: AgentMat: Framework for Data Scraping and Semantization, 3rd International Conference on Research Challenges in Information Science, IEEE, 2009  Dokulil, Yaghob, Zavoral: Trisolda: The Environment for Semantic Data Processing, International Journal On Advances in Software, IARIA, 2009  Podzimek, Dokulil, Yaghob, Zavoral: Mám hlad: pomůže mi Sémantický web?, Informačné technológie - Aplikácia a Teória, ITAT 2008  Dokulil, Tykal, Yaghob, Zavoral: Semantic Web Repository And Interfaces, International Conference on Advances in Semantic Processing, SEMAPRO 2007, IEEE Computer Society Press - Best Paper Award  Dokulil, Tykal, Yaghob, Zavoral: Semantic Web Infrastructure, IEEE International Conference on Semantic Computing ICSC, IEEE Computer Society Press 2007  Yaghob, Zavoral: Semantic Web Infrastructure using DataPile, Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Itelligent Agent Technology, Hong Kong, IEEE Computer Society Press 2006

15. http://ksi.mff.cuni.cz/semwex

16. PART II Tables in RDF querying - do we really need them?

17. SPARQL syntax  SQL-like – at first look  “simple language” but complex grammar  {?x ?y ?z. OPTIONAL { ?a ?b ?c. }. ?k ?l ?m. }  {?x ?y ?z OPTIONAL { ?a ?b ?c } ?k ?l ?m }

18. SPARQL semantics  lot of changes – now stable  based on algebra  works with sets of variable mappings – i.e. tables  very different from SQL  “closed”  no compositionality

19. SPARQL RDF is a graph SPARQL provides pattern (subgraph) matching – no other graph handling SPARQL handles only fixed-size graphs RDFS supports arbitrary hierarchy of classes SPARQL has no aggregate functions, no “group by”  no constructors

20. Seasoned SQL developer

22. Idea… ? make the language SQL-like inside not just outside  joins, selection, projection, grouping, aggregation  relational algebra works with relation, i.e. sets of triples, the database is made of relations  RDF data is made of… RDF graphs  maybe we should work with RDF graphs

23. Tables – Graphs JohnSmith JohnDoe JaneDoe BillJackson John Smith John Doe Jane Doe Bill Jackson

24. Basic pattern variables -> “columns” ?firstname ?lastname ?person ex:firstname ex:lastname

25. Further operations selection, joins, aggregation, projection group by

26. Multiple values john@doe.com johndoe@work.com ex:john ex:mail

27. Local and global aggregations more values in one “column” maximal number of mails total count of mails

28. What’s more? optional parts of the graph regular expressions textual representation (language)

29. Conclusion current state is bad try something different ?

30. PART III Let’s have a look – RDF visualizer

31. RDF subject – the thing we are describing predicate – the property of the thing object – the value of the property a graph (directed, labeled)

32. Visualization triangle layout  layered drawing for trees node merging  more information for a node navigation  the way to handle huge data

33. Let’s have a look A picture is worth a thousand words…