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Creating and Exploiting a Web of Semantic Data. Tim Finin, UMBC Earth and Space Science Informatics Workshop 05 August 2009 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

Creating and Exploiting a Web of Semantic Data

Tim Finin, UMBC

Earth and Space ScienceInformatics Workshop

05 August 2009

  • Introduction
  • Semantic Web 101
  • Recent Semantic Web trends
  • Examples: DBpedia, Wikitology
  • Conclusion
the age of big data
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
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)

ten years ago
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

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

w3c s semantic web goal
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
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
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
Simple RDF Example


“Intelligent Information Systemson the Web and in the Aether”


Note: “blank node”





“Tim Finin”

the rdf data model
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
RDF is the first SW language


XML Encoding


Data Model

<rdf:RDF ……..>




Good for

human viewing

Good for



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
XML encoding for RDF

<rdf:RDF xmlns:rdf=""



<description about="">

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



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


<bib:Aff resource="" />






“Intelligent Information Systemson the Web and in the Aether”






“Tim Finin”

n3 is a friendlier encoding
N3 is a friendlier encoding

@prefix rdf: .

@prefix dc: .

@prefix bib: .


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


[ bib:Name "Tim Finin";

bib:Email ""

bib:Aff: "" ] .


“Intelligent Information Systemson the Web and in the Aether”






“Tim Finin”

rdfs supports simple inferences
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
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
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
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
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
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
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
Dbpedia uses WP structured data

DBpedia extracts structured data from Wikipedia, especially from Infoboxes


PREFIX dbp: <>

PREFIX dbpo: <>

SELECT distinct ?Property ?Place

WHERE {dbp:Barack_Obama ?Property ?Place .

?Place rdf:type dbpo:Place .}

looking at the rdf description
Looking at the RDF description

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


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)
wikitology 2 0 2008
Wikitology 2.0 (2008)





Freebase KB




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