Semantic Web - TKK Semantic We… · So is the whole Semantic Web thing a huge failure? Processing...

How to use in network application development

Semantic Web

A sneak peek on Semantic Web: RDF and SPARQL

SPARQL Query PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?name ?mbox WHERE { ?x foaf:name ?name . ?x foaf:mbox ?mbox }

RDF Data @prefix foaf: <http://xmlns.com/foaf/0.1/> . @prefix people: <http://example.com/People/> . people:John foaf:name "Johnny Lee Outlaw" . people:John foaf:mbox <mailto:[email protected]> . people:Peter foaf:name "Peter Goodguy" . people:Peter foaf:mbox <mailto:[email protected]> . people:Carol foaf:mbox <mailto:[email protected]> .

Query result name mbox "Johnny Lee Outlaw" <mailto:[email protected]> "Peter Goodguy" <mailto:[email protected]>

Content

•  Semantic Web – A simple idea largely unrealized •  Processing structured data in the web •  Semantic Web stack •  Linked Data •  W3C Data Activity •  RDF Streams •  Continuous SPARQL queries

Semantic Web – A simple idea largely unrealized

10.3.2014 Laitoksen nimi

4

A simple idea largely unrealized

•  [Tim Berners-Lee] defines the Semantic Web as "a web of data that can be processed directly and indirectly by machines.” (wikipedia)

•  By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web dominated by unstructured and semi-structured documents into a "web of data”.

•  Berners-Lee described an expected evolution of the existing Web to a Semantic Web,[5] but this has yet to happen.

•  In 2006, Berners-Lee and colleagues stated that: "This simple idea ... remains largely unrealized."[6]

So is the whole Semantic Web thing a huge failure?

Processing Structured Data in the Web


6

Structured data in the web – an example

Structured data example – HTML browser

DB

Web server

SQL

Rows

HTML browser

HTTP get

Structured data processing by HTML scraping

DB

Web server

SQL

Rows

HTML scraper

HTTP get

Xpath rules

JSON CSV XML …

Problems: - Tight coupling -  HTML output formats change -  The changes are not documented

Application

Side note: Scrapy

Structured data processing with a REST API

DB

Web server

SQL

Rows

HTTP get

JSON CSV XML …

Rest API

Problems: -  Tight coupling -  The API calls hard coded in our application -  APIs change

Application

Structured data – multiple data sources

The problems that we had with scraping or APIs multiply •  All the HTML formats or the APIs are hard coded in our application •  Every change means editing our program

Combining the data from heterogeneous sources: •  Each source has its own data format and own naming

conventions

DB

Web server

DB

Web server

DB

Web server

Application

Structured data with Semantic Web technologies

•  Refer to objects using URIs (IRIs) •  Represent structured data using RDF triples •  Use OWL ontologies for shared concepts between different data

sources •  Access the RDF data in RDF Graph stores or SPARQL endpoints •  Query the data using SPARQL query language •  Merge structured data from different data sources

Structured data – Graph store HTTP access

RDF store

HTTP GET containing SPARQL construct query

Application

SPARQL query engine RDF triples

Structured data – Multiple graph store HTTP access

RDF store

HTTP GET containing SPARQL construct query

Application

SPARQL query engine

RDF store

RDF store

RDF triples

Structured data – SPARQL endpoint

JSON CVS XML ...

SPARQL endpoint

Application

HTTP GET containing SPARQL queries

Structured data – multiple SPARQL endpoints HTTP GET containing SPARQL queries

JSON CVS XML ...

SPARQL endpoint

Application SPARQL endpoint

SPARQL endpoint

Semantic Web Stack

Semantic Web Stack


19

URI – Universal Resource Identifier

•  To be able to merge data from different sources we need a mechanism for naming objects uniquely

•  In Semantic Web, URI is this naming mechanism •  More generally, we use Internationalized Resource Identifiers

(IRIs)

RDF – Resource Description Framework

RDF: a set of triples •  Triple is a subject-predicate-object statement

”John knows Bob” can be represented as a triple: <http://example.com/People/John> <http://xmlns.com/foaf/0.1/knows> <http://example.com/People/Bob>

•  Often we use prefixes to shorten the URIs: people:John foaf:knows people:Bob

RDF – Resource Description Framework

RDF: a directed labeled graph

From ”What is RDF and what is it good for?”

RDF – Resource Description Framework Part of the previous graph as triples (N3 notation) @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix ex: <http://www.example.org/> . ex:vincent_donofrio ex:starred_in ex:law_and_order_ci . ex:law_and_order_ci rdf:type ex:tv_show . ex:the_thirteenth_floor ex:similar_plot_as ex:the_matrix .

Part of the previous graph as RDF/XML

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:ex="http://www.example.org/"> <rdf:Description rdf:about="http://www.example.org/vincent_donofrio"> <ex:starred_in> <ex:tv_show rdf:about="http://www.example.org/law_and_order_ci" /> </ex:starred_in> </rdf:Description> <rdf:Description rdf:about="http://www.example.org/the_thirteenth_floor"> <ex:similar_plot_as rdf:resource="http://www.example.org/the_matrix" /> </rdf:Description> </rdf:RDF>

SPARQL – an RDF query language •  Resembles (unfortunately?) SQL a lot

QUERY PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?name ?mbox WHERE { ?x foaf:name ?name . ?x foaf:mbox ?mbox }

DATA @prefix foaf: <http://xmlns.com/foaf/0.1/> . _:a foaf:name "Johnny Lee Outlaw" . _:a foaf:mbox <mailto:[email protected]> . _:b foaf:name "Peter Goodguy" . _:b foaf:mbox <mailto:[email protected]> . _:c foaf:mbox <mailto:[email protected]> .

QUERY RESULT name mbox "Johnny Lee Outlaw" <mailto:[email protected]> "Peter Goodguy" <mailto:[email protected]>

Ontologies Definition:

In computer science and information science, an ontology formally represents knowledge as a set of concepts within a domain, and the relationships between pairs of concepts. It can be used to model a domain and support reasoning about entities.

RDF schema •  Provides basic elements (classes and properties) for the description of ontologies

Web Ontology Language (OWL) •  A family of languages for representing ontologies •  Related to description logics •  IMO, the darker side of the semantic web; don’t go too deep into this, if you

want to get things done.

Ontologies Example ontologies •  FOAF (Friend of a friend) an ontology describing persons,

their activities and their relations to other people and objects.

•  Dublin Core, a simple ontology for documents and publishing

•  Cyc, a large Foundation Ontology for formal representation of the universe of discourse (don’t go in there)

•  DBpedia Ontology, a shallow, cross-domain ontology, manually created based on the most commonly used infoboxes within Wikipedia.

Example SPARQL Endpoint – data.aalto.fi


27

RDF stores – the only standardized NoSQL solutions available at the moment


28

•  A simple and uniform standard data model. •  A powerful standard query language. •  Standardized data interchange formats.

- Data portability - Toolchain interoperability - No vendor or product lock-in - Future proof.

From: “How RDF Databases Differ from Other NoSQL Solutions”

Linked Data


29

Linked Data


30

•  A method of publishing structured data so that it can be interlinked and become more useful - Builds on URIs, RDF, and SPARQL - Term coined by Tim Berners-Lee in a design note year 2006

•  For more detailed information, see the wikipedia page, and the articles referred there •  Four principles:

1.  Use URIs to denote things. 2.  Use HTTP URIs so that these things can be referred to and looked up

by people and user agents. 3.  Provide useful information about the thing when its URI is dereferenced,

leveraging standards such as RDF, SPARQL. 4.  Include links to other related things (using their URIs) when

publishing data on the Web.

Linked Data (cont.)


31

•  Four principles (repeated): 1.  Use URIs to denote things. 2.  Use HTTP URIs so that these things can be referred to and looked up

by people and user agents. 3.  Provide useful information about the thing when its URI is dereferenced,

leveraging standards such as RDF, SPARQL. 4.  Include links to other related things (using their URIs) when

publishing data on the Web. •  Restated by Berners-Lee 2009:

1.  All kinds of conceptual things, they have names now that start with HTTP. 2.  I get important information back… 3.  I get back that information it's not just got somebody's height and weight and when

they were born, it's got relationships. And when it has relationships, whenever it expresses a relationship then the other thing that it's related to is given one of those names that starts with HTTP.

W3C Data Activity


32

W3C Data activity


33

W3C RDF Stream Processing


34

W3C RSP Community Group


35

Continuous SPARQL queries


36

Traditional approach: Executing SPARQL queries on RDF data

•  Just like executing SQL queries on a relational database

DB

SQL query

Rows of data

RDF store

SPARQL query

JSON/CVS/XML… data

Continous query approach: Executing RDF data on a set of SPARQL queries

•  The queries remain the same, but the data changes •  The results of the queries are updated immediately, when the data changes •  This should be realized without executing the queries against the complete

RDF store every time the data changes •  Continous queries also exist in SQL domain •  Also known as standing queries


38

Continuous query example – RDF data @prefix people:<http://example.com/People/> @prefix foaf:<http://xmlns.com/foaf/0.1/> @prefix event:<http://purl.org/NET/c4dm/event.owl#> @prefix geo:<http://www.w3.org/2003/01/geo/wgs84_pos#> @prefix tl:<http://purl.org/NET/c4dm/timeline.owl#> @prefix xsd:<http://www.w3.org/2001/XMLSchema#> people:John foaf:knows people:Bob . event:e123 event:agent people:John ;

event:place _:p789 ; event:time _:t790 .

_:p789 geo:lat 60.15922234475837 ; geo:long 24.87610729902513 . _:t790 tl:at "2012-03-05T13:32:28"^^xsd:dateTime . event:e124 event:agent people:Bob ;

event:place _:p791 ; event:time _:t792 .

_:p791 geo:lat 60.168418510496544 ; geo:long 24.857417412169575 . _:t792 tl:at "2012-03-05T13:32:36"^^xsd:dateTime .


39

Continuous query example – Event RDF graph


40

:e1$

rdf:

type

event:$Event$

event: agent :p3$

event: place

geo:

la

t

60.158776$

geo:

lo

ng

24.881490$

even

t: tim

e rdf: type

tl:$Instant$

tl:

at

2011810803T08:17:11$

geo: alt

Continuous query example – Event RDF graph


41

:e1$

rdf:

type

event: agent :p3$

even

t: tim

e

:e27$

rdf:

type

event: agent :p4$

even

t: tim

e

foaf: knows

:nearby

Continuous query example – Remove old events DELETE { ?event event:agent ?person . ?event event:place ?place . ?place geo:lat ?lat . ?place geo:long ?long . ?event event:time ?time . ?time tl:at ?dttm

} WHERE {

?event event:agent ?person . ?event event:place ?place . ?place geo:lat ?lat . ?place geo:long ?long . ?event event:time ?time . ?time tl:at ?dttm FILTER EXISTS { ?event2 event:agent ?person . ?event2 event:time ?time2 . ?time2 tl:at ?dttm2 FILTER (?dttm < ?dttm2) }

} .


42

Continuous query example – Detect nearby INSERT { ?person1 :nearby ?person2 } WHERE { ?person1 foaf:knows ?person2 . ?event1 event:agent ?person1 . ?event1 event:place ?place1 . ?place1 geo:lat ?lat1 . ?place1 geo:long ?long1 . ?event1 event:time ?time1 . ?time1 tl:at ?dttm1 . ?event2 event:agent ?person2 . ?event2 event:place ?place2 . ?place2 geo:lat ?lat2 . ?place2 geo:long ?long2 . ?event2 event:time ?time2 . ?time2 tl:at ?dttm2 . FILTER ((abs(hours(?dttm2)* 60 + minutes(?dttm2)-hours(?dttm1)*60 - minutes(?dttm1)) < 5)) FILTER ((abs(?lat2 - ?lat1) < 0.01) && (abs(?long2 - ?long1) < 0.01)) FILTER NOT EXISTS { ?person1 :nearby ?person2 } } .


43

Continuous query example – Detect not nearby DELETE { ?person1 :nearby ?person2 } WHERE { ?person1 foaf:knows ?person2 . ?event1 event:agent ?person1 . ?event1 event:place ?place1 . ?place1 geo:lat ?lat1 . ?place1 geo:long ?long1 . ?event1 event:time ?time1 . ?time1 tl:at ?dttm1 . ?event2 event:agent ?person2 . ?event2 event:place ?place2 . ?place2 geo:lat ?lat2 . ?place2 geo:long ?long2 . ?event2 event:time ?time2 . ?time2 tl:at ?dttm2 . FILTER ( (abs(?lat2-?lat1)>0.02) || (abs(?long2-?long1)>0.02)) FILTER EXISTS {?person1 :nearby ?person2} }


44

Continuous query example – Report nearby SELECT ?person1 ?person2 WHERE { ?person1 :nearby ?person2 } .


45

INSTANS – Incremental Engine for Standing SPARQL


46

Semantic Web - TKK Semantic We… · So is the whole Semantic Web thing a huge failure? Processing...

Documents

Transcript of Semantic Web - TKK Semantic We… · So is the whole Semantic Web thing a huge failure? Processing...