1. Semantic Web and Linked DataBy
Harsh Pareek
Raman Sharma
Sumit Somani
Shiv Shankar

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

3. 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.

4. Motivation
Query: “List all phones which have a battery life of 12 hours and cost less than Rs ”
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

5. Motivation
Query: “List all phones which have a battery life of 12 hours and cost less than Rs ”
Many valid pages may not contain the word but we should be able to infer that the price is <10000
Semantic Web could be used to build Query systems.

6. Motivation Original Doctor Appointment: Thu 9:00-10:00am
New 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.

7. 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):34-43
Pic Courtesy: wikipedia.org
www. mechanicsnationalbank.com

8. Semantic Web: History
Courtesy :

9. Semantic Web : History
2001 – Berners-Lee, Tim; Hendler, James; Lassila, Ora. "The Semantic Web". Scientific American.
2004- RDF (implementation)
2005- OWL
2007- Linked Open Data

10. Knowledge Representation
So far we have to come to understand Sematic Web is structured data on web. So how do we structure it? We present the idea in the next slides.

11. 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.

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

13. Ontology Examples: Wordnet FOAF (Friend of a Friend) Gene Ontology
GeoPolitical Ontology
Thought treasure ontology
Cyc
Jamendo
Customer Complaint Ontology
Courtesy :

14. 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 :

15. 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

16. 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

17. 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

18. RDF - OWL
<rdf:Description rdf:about="subject"> <predicate rdf:resource="object" /> <predicate>literal value</predicate> <rdf:Description>
Courtesy :

19. RDF - OWL
Courtesy :

20. RDF – OWL : An Example (1/3)
<rdf:RDF xmlns:rdf=“ xmlns:feature=" <rdf:Description rdf:about=" <feature:size>12</feature:size> <feature:color rdf:resource=" white"/> </rdf:Description> </rdf:RDF>
Courtesy :

21. RDF – OWL : An Example (2/3)
Courtesy: NeonTool

22. 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>
Critique
Aka Semantic Modelling
Requires Human Intelligence
Difficult to be done by machines
Courtesy:
Pic Courtesy: pctechs.biz, www. thedoublethink.com

23. Linked Open Data
Courtesy:

24. 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:

25. Infobox
Courtesy:

26. 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

27. How to access Linked Data
Querying DBpedia
Offline: Linked Open Data Crawl
Billion Triple Challenge Dataset
SPARQL
PREFIX dbprop: <
PREFIX db: <
SELECT ?who ?work ?genre WHERE { db:Tokyo_Mew_Mew dbprop:illustrator ?who .
?work dbprop:author ?who .
OPTIONAL { ?work dbprop:genre ?genre } . }

28. SPARQL
Courtesy:

29. Document Web vs Linked Data
Web of Linked Documents
Web of Linked Data
A global filesystem
Human usage
Primary objects documents
Links between documents
Low degree of structure
Implicit Semantics of content and links
A global database
Machine interpretation
Primary objects entities or things
Links between entities
High Degree of structure
Explicit Semantics of content and links

30. 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

31. 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
Further Reading
NLP and the Semantic Web
Proceedings of the NLP4SW conference:

32. Questions?

33. EXTRA

34. Falcon demo

35. 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

36. 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

37. 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

38. 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

39. 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”

40. 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?”

41. Would Web Search + NLP win Jeopardy?
Source: Stephen Wolfram’s Blog (