Semantic Web and Linked Data

ByHarsh Pareek 07005007

Raman Sharma 07005010Sumit Somani 07005012Shiv Shankar 07005026

Outline

• Outline• Motivation• Semantic Web: History and development• Linked Open Data• Linked Open Data Technologies• DBpedia: An example of LOD• Accessing LOD

Motivation

• Limitations of NLP– “In 2003, President of US ordered Iraq invasion.

George believed it to be a great decision.”– How do we know that George referred here is

referring to George Bush, and he was then President of US.

– It is due to world knowledge– Semantic Web helps us overcome this lack of world

knowledge and helps in processing the language. In this case, co-reference was solved.

Motivation

• Query: “List all phones which have a battery life of 12 hours and cost less than Rs. 10000”– This data may not be explicitly present on the web– But, the information on web is enough to answer

this query. Lack of structured data is the bottleneck.

– Need to represent information about phones in a database-like format and perform sql-like queries

Motivation

• Query: “List all phones which have a battery life of 12 hours and cost less than Rs. 10000”– Many valid pages may not contain the word 10000

but we should be able to infer that the price is <10000

– Semantic Web could be used to build Query systems.

Motivation

Original Doctor Appointment: Thu 9:00-10:00amNew Constraint : Thu 9:30 am onwards– If we store data on web in semantic format,

machine could realize conflict, and would search for alternate doctor free times or similar doctors.

– Tim Berners-Lee calls this as “optimization”. Semantic web could make the web smarter, mechanically usable and accurate.

Semantic Web

“The semantic web is not a separate web, but an extension of the current one, in which information is given a well-defined meaning, better enabling computer and people work better in cooperation”. [1]

Courtesy : Berners-Lee T., Hendler, J., Lassila, O. (2001) The Semantic Web. Scientific American 284(5):34-43Pic Courtesy: wikipedia.orgwww. mechanicsnationalbank.com

Semantic Web: History

Courtesy : http://novaspivack.typepad.com/nova_spivacks_weblog/2007/10/web-30----the-a.html

Ontology

• Ontology is a formal representation of knowledge as a set of concepts within a domain and the relationship among those concepts.

• We need ontologies for :-– Sharing common understanding of information.– Reuse of domain knowledge.– Making domain assumptions explicit.

Ontology

• Camera Ontology

Courtesy : Minsoo Kim, Minkoo Kim: Developing Protégé Plug-in: OWL Ontology Visualization using Social Network. JIPS 4(2): 61-66 (2008)

Ontology

• Examples:– Wordnet– FOAF (Friend of a Friend)– Gene Ontology– GeoPolitical Ontology– Thought treasure ontology– Cyc– Jamendo– Customer Complaint Ontology

Courtesy : http://en.wikipedia.org/wiki/Ontology_(information_science)#Examples_of_published_ontologies

From Ontology to Linked Data

• But ontologies are domain specific• But to match semantic search requirements

we have to use all ontologies together• How can we use all the available ontologies• The answer is to create link all of them

together, making a meta-ontology

Courtesy : http://en.wikipedia.org/wiki/Ontology_(information_science)#Examples_of_published_ontologies

Linked Open Data

• A way of linking these ontologies so as to– encourage reuse– reduce redundancy– maximize inter-connectedness– enable network effects to add value to data

Linked Open Data Technology (1/2)

• URI (Unique Resource Identifier) -> The unique name by which something is referred

• HTTP (Hyper Text Transfer Protocol) -> Provides basic access mechanism using WWW for lookup

• RDF (Resource Description Framework) -> Data format to describe relationships among entities

• OWL (Web Ontology Language) -> Provides a common understanding of concepts aiding in reasoning

Linked Open Data Technology (2/2)• Use URI for unique nomenclature for things

– anything, not just web pages– all kinds of information resources

• Use HTTP as URI– provides globally unique names– allows using existing web for lookup

• Encode useful information in RDF– when servicing a URI lookup

• Include RDF links to other URI– enable discovery of related information

• Encode further information using OWL – enable reasoning about information across domains

RDF - OWL

<rdf:Description rdf:about="subject"> <predicate rdf:resource="object" /> <predicate>literal value</predicate>

<rdf:Description>

Courtesy : http://www.linkeddatatools.com/introducing-rdf

RDF - OWL

Courtesy : http://www.linkeddatatools.com/introducing-rdf-part-2

RDF – OWL : An Example (1/3)<rdf:RDF

xmlns:rdf=“http://www.w3.org/1999/02/22-rdf-syntax-ns#” xmlns:feature="http://www.linkeddatatools.com/clothing-

features#"> <rdf:Description rdf:about="http://www.linkeddatatools.com/clothes#t-shirt">

<feature:size>12</feature:size> <feature:color

rdf:resource="http://www.linkeddatatools.com/colors#white"/> </rdf:Description>

</rdf:RDF>

Courtesy : http://www.linkeddatatools.com/introducing-rdf-part-2

RDF – OWL : An Example (2/3)

Courtesy: NeonTool

RDF - OWL: An Example (3/3)<owl:Class rdf:ID="SpaceTimeThing"><rdfs:label xml:lang="en">things in our time and space</rdfs:label>

<rdfs:comment xml:lang="en">A specialisation of #$SpatialThing and #$TemporalThing. A collection of things that physically exist in our universe.</rdfs:comment>

<rdfs:subClassOf rdf:resource="#SpatialThing"/>

<rdfs:subClassOf rdf:resource="#TemporalThing”/>

</owl:Class>

Courtesy:http://www.qrst.de/ontology/owl.xmlPic Courtesy: http://www. pctechs.biz, www. thedoublethink.com

CritiqueAka Semantic Modelling• Requires

Human Intelligence

• Difficult to be done by machines

Linked Open Data

Courtesy: http://linkeddata.org/

DBpedia

• Wikipedia contains structural information such as – "infobox" tables– categorisation information– Images– geo-coordinates – links to external Web pages

• Dbpedia lets us treat Wikipedia as a database which can be queried

Courtesy: http://en.wikipedia.org/wiki/DBpedia

Infobox

Courtesy: http://en.wikipedia.org/wiki/Sachin_Tendulkar

DBpedia

• Contains:– 3.4 million things– Abstracts in upto 92 different languages– 1,460,000 links to images – 5,543,000 links to external web pages– 4,887,000 external links into other RDF datasets– 565,000 Wikipedia categories

How to access Linked Data

Querying DBpedia• Offline: Linked Open Data Crawl– Billion Triple Challenge Dataset

• SPARQLPREFIX dbprop: <http://dbpedia.org/property/> PREFIX db: <http://dbpedia.org/resource/> SELECT ?who ?work ?genre WHERE { db:Tokyo_Mew_Mew

dbprop:illustrator ?who . ?work dbprop:author ?who . OPTIONAL { ?work dbprop:genre ?genre } . }

SPARQL

Courtesy: http://dbpedia.org/sparql

Document Web vs Linked Data

Web of Linked Documents• A global filesystem• Human usage• Primary objects

documents• Links between documents• Low degree of structure • Implicit Semantics of

content and links

Web of Linked Data• A global database• Machine interpretation • Primary objects

entities or things• Links between entities• High Degree of structure• Explicit Semantics of

content and links

Conclusion• Imposing structure and standards on available information

increasing its usability and value

• As semantic web spreads it would become priceless, allowing machines to analyze all the data on the Web – the content, links, and even transactions between people and computer

• Searching over all of linked data is possible but at current stage not effective.As the structure becomes larger and more accepted it would become easier

• Ontology creation still requires human intelligence.But by "bolstering human intelligence" definition of AI, we could win the battle

References

• Berners-Lee T., Hendler, J., Lassila, O. (2001) The Semantic Web. Scientific American 284(5):34-43

• Christian Bizer, Tom Heath, Tim Berners-Lee. Linked Data – The Story So Far. IJSWIS

• http://linkeddata.org/Further Reading• NLP and the Semantic Web

http://www.csc.villanova.edu/~nlp/pres1/presentation.pdf

• Proceedings of the NLP4SW conference: http://www.dcs.shef.ac.uk/~diana/courses/lrec-nlp-semweb-tutorial.html

Questions?

EXTRA

Falcon demo

Ontology Learning

• Semantic annotation – annotate in the texts all mentions of instances relating to concepts in the ontology

• Ontology learning – automatically derive an ontology from Texts

• Ontology population – given an ontology, populate the concepts with instances derived automatically from a text

Ontology Learning: Hearst Patterns[1992]

• Such NP as {NP}* {or|and} NP– “such games as baseball and cricket”

• NP {,NP}* {,} {and|or} other NP– “rabbits and other animals”– But, “rabbits and other pets”

• NP {,} including {NP,}* {or|and} NP– “fruits including apples and pears”

• NP{,} especially {NP,}*{or|and}NP– “Europeans, especially Italians”– But, “US Presidents, especially democrats”

• Extended by newer systems such as KnowItAll

NLP for Semantic Web So how does Natural language processing fit in?• Semantic Web requires machine-interpretable semantics in

order to process textual information on the internet• Natural language processing is vital to the success of the

semantic web because it is the method of communication between humans and software agents

• Parsing, knowledge representation, information extraction, disambiguation, term recognition and semantic analysis are used in many semantic web technologies

NLP for Semantic Web

• Linked Open Data is mostly academic and volunteer work

• Converting the current snapshot of the web to Semantic Web requires effort and time

• This is disregarding the fact that the Web is growing at very high rates

• Semi-automated mechanisms using NLP techniques are required to keep up with the increasing content

Semantic Web for NLP

• Entity Disambiguation– Word Sense Disambiguation using ontologies • Adds context to information• Allows using richer lexicon• Use world knowledge

– Eg. “Senator Green gave the green light for the green bill in parliament”

– Eg. “Moses led the Jews to the banks of Jordan”

Semantic Web for NLP

• Question Answering– “Sir Edward Heath died from pneumonia”– Sir Edward Heath -> UK Prime Minister->politician– Died from -> killed by– Pneumonia->disease

– “Has a politician died of a lung disease?”

Would Web Search + NLP win Jeopardy?

Source: Stephen Wolfram’s Blog(http://blog.stephenwolfram.com/2011/01/jeopardy-ibm-and-wolframalpha/)