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Creating and Exploiting a Web of Semantic Data. Tim Finin, UMBC Earth and Space Science Informatics Workshop 05 August 2009 http://ebiquity.umbc.edu/resource/html/id/272/. Overview. Introduction Semantic Web 101 Recent Semantic Web trends Examples: DBpedia, Wikitology Conclusion.

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Creating and exploiting a web of semantic data l.jpg

Creating and Exploiting a Web of Semantic Data

Tim Finin, UMBC

Earth and Space ScienceInformatics Workshop

05 August 2009

http://ebiquity.umbc.edu/resource/html/id/272/


Overview l.jpg
Overview

  • Introduction

  • Semantic Web 101

  • Recent Semantic Web trends

  • Examples: DBpedia, Wikitology

  • Conclusion


The age of big data l.jpg
The Age of Big Data

  • Massive amounts of data is available today

  • Advances inmany fields driven by availability of unstructured data, e.g., text, audio, images

  • Increasingly, large amounts of structured and semi-structured data is also online

  • Much of this available in the Semantic Web language RDF, fostering integration and interoperability

  • Such structured data is especially important for the sciences


Twenty years ago l.jpg
Twenty years ago…

Tim Berners-Lee’s 1989 WWW proposal described a web of rela- tionships among named objects unifying many information management tasks

Capsule history

  • Guha’s MCF (~94)

  • XML+MCF=>RDF (~96)

  • RDF+OO=>RDFS (~99)

  • RDFS+KR=>DAML+OIL (00)

  • W3C’s SW activity (01)

  • W3C’s OWL (03)

  • SPARQL, RDFa (08)

  • Rules (09)

http://www.w3.org/History/1989/proposal.html


Ten years ago l.jpg
Ten years ago ….

  • The W3C started developing standards for the Semantic Web

  • The vision, technology and use cases are still evolving

  • Moving from a web of documents to a web of data


Today l.jpg
Today

4.5 billion integrated facts published on the Web as RDF Linked Open Data


Tomorrow l.jpg
Tomorrow

Large collections of integrated facts published on the Web for many disciplines and domains


W3c s semantic web goal l.jpg
W3C’s Semantic Web Goal

“The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.”

-- Berners-Lee, Hendler and Lassila, The Semantic Web, Scientific American, 2001


Contrast with a non web approach l.jpg
Contrast with a non-Web approach

  • The W3C Semantic Web approach is

  • Distributed

  • Open

  • Non-proprietary

  • Standards based


How can we share data on the web l.jpg
How can we share data on the Web?

  • POX, Plain Old XML, is one approach, but it has deficiencies

  • The Semantic Web languages RDF and OWL offer a simpler and more abstract data model (a graph) that is better for integration

  • Its well defined semantics supports knowledge modeling and inference

  • Supported by a stable, funded standards organization, the World Wide Web Consortium


Simple rdf example l.jpg
Simple RDF Example

http://umbc.edu/~finin/talks/idm02/

dc:Title

“Intelligent Information Systemson the Web and in the Aether”

dc:Creator

Note: “blank node”

bib:Aff

bib:email

http://umbc.edu/

bib:name

[email protected]

“Tim Finin”


The rdf data model l.jpg
The RDF Data Model

  • An RDF document is an unordered collection of statements, each with a subject, predicate and object

  • Such triples can be thought of as a labelled arc in a graph

  • Statements describe properties of resources

  • A resource is any object that can be referenced or denoted by a URI

  • Properties themselves are also resources (URIs)

  • Dereferencing a URI produces useful additional information, e.g., a definition or additional facts


Rdf is the first sw language l.jpg
RDF is the first SW language

Graph

XML Encoding

RDF

Data Model

<rdf:RDF ……..>

<….>

<….>

</rdf:RDF>

Good for

human viewing

Good for

Machineprocessing

Triples

stmt(docInst, rdf_type, Document)

stmt(personInst, rdf_type, Person)

stmt(inroomInst, rdf_type, InRoom)

stmt(personInst, holding, docInst)

stmt(inroomInst, person, personInst)

RDF is a simple language for graph based representations

Good for storage and reasoning


Xml encoding for rdf l.jpg
XML encoding for RDF

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:dc="http://purl.org/dc/elements/1.1/"

xmlns:bib="http://daml.umbc.edu/ontologies/bib/">

<description about="http://umbc.edu/~finin/talks/idm02/">

<dc:title>Intelligent Information … and in the Aether</dc:Title>

<dc:creator>

<description>

<bib:Name>Tim Finin</bib:Name>

<bib:Email>[email protected]</bib:Email>

<bib:Aff resource="http://umbc.edu/" />

</description>

</dc:Creator>

</description>

</rdf:RDF>

http://umbc.edu/~finin/talks/idm02/

dc:Title

“Intelligent Information Systemson the Web and in the Aether”

dc:Creator

bib:Aff

bib:email

http://umbc.edu/

bib:name

[email protected]

“Tim Finin”


N3 is a friendlier encoding l.jpg
N3 is a friendlier encoding

@prefix rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns# .

@prefix dc: http://purl.org/dc/elements/1.1/ .

@prefix bib: http://daml.umbc.edu/ontologies/bib/ .

<http://umbc.edu/~finin/talks/idm02/>

dc:title "Intelligent ... and in the Aether" ;

dc:creator

[ bib:Name "Tim Finin";

bib:Email "[email protected]"

bib:Aff: "http://umbc.edu/" ] .

http://umbc.edu/~finin/talks/idm02/

dc:Title

“Intelligent Information Systemson the Web and in the Aether”

dc:Creator

bib:Aff

bib:email

http://umbc.edu/

bib:name

[email protected]

“Tim Finin”


Rdfs supports simple inferences l.jpg
RDFS supports simple inferences

  • RDF Schema adds vocabulary for classes, properties & constraints

  • An RDF ontology plus some RDF statements may imply additional RDF statements (not possible in XML)

  • Note that this is part of the data model and not of the accessing or processing code.

  • @prefix rdfs: <http://www.....>.

  • @prefix : <genesis.n3>.

  • parent a rdf: property;

  • rdfs:domain person;

    • rdfs:range person.

    • mother rdfs:subProperty parent;

    • rdfs:domain woman;

    • rdfs:range person.

    • eve mother cain.

person a class.

woman subClass person.

mother a property.

eve a person;

a woman;

parent cain.

cain a person.


Owl adds further richness l.jpg
OWL adds further richness

OWL adds richer representational vocabulary, e.g.

  • parentOf is the inverse of childOf

  • Every person has exactly one mother

  • Every person is a man or a woman but not both

  • A man is the equivalent of a person with a sex property with value “male”

    OWL is based on ‘description logic’ – a logic subset with efficient reasoners that are complete

  • Good algorithms for reasoning about descriptions


That was then this is now l.jpg
That was then, this is now

  • 1996-2000: focus on RDF and data

  • 2000-2007: focus on OWL, developing ontologies, sophisticated reasoning

  • 2008-…: Integrating and exploiting large RDF data collections backed by lightweight ontologies


A linked data story l.jpg
A Linked Data story

  • Wikipedia as a source of knowledge

    • Wikis are a great ways to collaborateon building up knowledge resources

  • Wikipedia as an ontology

    • Every Wikipedia page is a concept or object

  • Wikipedia as RDF data

    • Map this ontology into RDF

  • DBpedia as the lynchpin for Linked Data

    • Exploit its breadth of coverage to integrate things





Wikipedia as an ontology l.jpg
Wikipedia as an ontology

  • Using Wikipedia as an ontology

    • each article (~3M) is an ontology concept or instance

    • terms linked via category system (~200k), infobox template use, inter-article links, infobox links

    • Article history contains metadata for trust, provenance, etc.

  • It’s a consensus ontology with broad coverage

  • Created and maintained by a diverse community for free!

  • Multilingual

  • Very current

  • Overall content quality is high


Wikipedia as an ontology24 l.jpg
Wikipedia as an ontology

  • Uncategorized and miscategorized articles

  • Many ‘administrative’ categories: articles needing revision; useless ones: 1949 births

  • Multiple infobox templates for the same class

  • Multiple infobox attribute names for same property

  • No datatypes or domains for infobox attribute values

  • etc.


Dbpedia wikipedia in rdf l.jpg
Dbpedia : Wikipedia in RDF

  • A community effort to extractstructured information fromWikipedia and publish as RDFon the Web

  • Effort started in 2006 with EU funding

  • Data and software open sourced

  • DBpedia doesn’t extract information from Wikipedia’s text, but from the its structured information, e.g., links, categories, infoboxes




Dbpedia uses wp structured data l.jpg
Dbpedia uses WP structured data

DBpedia extracts structured data from Wikipedia, especially from Infoboxes


Slide32 l.jpg

PREFIX dbp: <http://dbpedia.org/resource/>

PREFIX dbpo: <http://dbpedia.org/ontology/>

SELECT distinct ?Property ?Place

WHERE {dbp:Barack_Obama ?Property ?Place .

?Place rdf:type dbpo:Place .}

http://dbpedia.org/sparql/




Looking at the rdf description l.jpg
Looking at the RDF description

We find assertions equating DBpedia's object for Baltimore with those in other LOD datasets:

dbpedia:Baltimore%2C_Maryland

owl:sameAs census:us/md/counties/baltimore/baltimore;

owl:sameAs cyc:concept/Mx4rvVin-5wpEbGdrcN5Y29ycA;

owl:sameAs freebase:guid.9202a8c04000641f800000000004921a;

owl:sameAs geonames:4347778/ .

Since owl:sameAs is defined as an equivalence relation, the mapping works both ways



Wikitology l.jpg
Wikitology

We’ve been exploring a different approach to derive an ontology from Wikipedia through a series of use cases:

  • Identifying user context in a collaboration system from documents viewed (2006)

  • Improve IR accuracy by adding Wikitology tags to documents (2007)

  • ACE: cross document co-reference resolution for named entities in text (2008)

  • TAC KBP: Knowledge Base population from text (2009)

  • Improve Web search engine by tagging documents and queries (2009)


Wikitology 2 0 2008 l.jpg
Wikitology 2.0 (2008)

RDF

RDF

graphs

text

Freebase KB

Yago

WordNet

Databases

Human input & editing


Conclusion l.jpg
Conclusion

  • The Semantic Web approach is a powerful approach for data interoperability and integration

  • The research focus is shifting to a “Web of Data” perspective

  • Many research issue remain: uncertainty, provenance, trust, parallel graph algorithms, reasoning over billions of triples, user-friendly tools, etc.

  • Just as the Web enhances human intelligence, the Semantic Web will enhance machine intelligence

  • The ideas and technology are still evolving



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