Filip Zavoral, Jiří DokulilSemWex - KSI MFF UK

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

Semantic Web infrastructure Trisolda current state and perspectives

10. Mixer 26.11.2008

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

Semantic technologies

TCP/IP

HTTP

HTML

Browser

Technical details

Semantic web services

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

Trisolda

Trisolda Architecture

Import interfaces

Repository

Querying & Executors

Repository

Trisolda Repository Stores incoming data Retrieves results for queries Stores used ontology DataPile structure

holds data in any formatApplications server

Not all data and knowledge available when imported the knowledge is not

accurate Background worker

inferencing data unifications reasoner

Framework for plug-ins

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,

inaccurateImport modes

batch insert immediate insert

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

AgentMat - data semantization framework

AgentMat - data extraction

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 ...

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

PART II

Tables in RDF querying -do we really need them?

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 }

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

SPARQL

RDF is a graphSPARQL provides pattern (subgraph) matching –

no other graph handling

SPARQL handles only fixed-size graphsRDFS supports arbitrary hierarchy of classes

SPARQL has no aggregate functions, no “group by” no constructors

Seasoned SQL developer

Seasoned SQL developer

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

Tables – Graphs

John Smith

John Doe

Jane Doe

Bill Jackson

John

Smith

John

Doe

Jane

Doe

Bill

Jackson

Basic pattern

variables -> “columns”

?firstname

?lastname

?personex:firstname

ex:lastname

Further operations

selection, joins, aggregation, projectiongroup by

Multiple values

john@doe.com

johndoe@work.com

ex:johnex:mail

ex:mail

Local and global aggregations

more values in one “column”

maximal number of mailstotal count of mails

What’s more?

optional parts of the graphregular expressionstextual representation (language)

Conclusion

current state is badtry something different ?

PART III

Let’s have a look – RDF visualizer

RDF

subject – the thing we are describingpredicate – the property of the thingobject – the value of the property

a graph (directed, labeled)

Visualization

triangle layout layered drawing for trees

node merging more information for a node

navigation the way to handle huge data

Let’s have a look

A picture is worth a thousand words…