Creating and Exploiting a Web of Semantic Data - PowerPoint PPT Presentation

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/

• Introduction
• Semantic Web 101
• Recent Semantic Web trends
• Examples: DBpedia, Wikitology
• Conclusion

• 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

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

• 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

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

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

“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

• The W3C Semantic Web approach is
• Distributed
• Open
• Non-proprietary
• Standards based

• 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

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

“finin@umbc.edu”

“Tim Finin”

• 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

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

<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>finin@umbc.edu</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

“finin@umbc.edu”

“Tim Finin”

@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 "finin@umbc.edu"

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

“finin@umbc.edu”

“Tim Finin”

• 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 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

• 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

• 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

• 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

• 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.

• 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

http://lookup.dbpedia.org/

DBpedia extracts structured data from Wikipedia, especially from Infoboxes

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/

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

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)

RDF

RDF

graphs

text

Freebase KB

Yago

WordNet

Databases

Human input & editing

• 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

http://ebiquity.umbc.edu/