1. Social Knowledge Mining
A systematic review of tools and technologies
By Shayne WEerakoon

2. Social Knowledge What Is Social Knowledge?
Social Knowledge is the collective body of knowledge produced by your immediate community or social circle

3. How is Social Knowledge Shared?
The era of web 2.0 has bought about many changes in how internet is used namely, social media sites such as Facebook, twitter
Volume of User Generated Content available on the net rises as a result of this
Social knowledge shared rises proportionately to the volume of UGC
Popularity of Question and Answer platforms rising as well
Stackoverflow had a 600% rise in users over the past few years

4. Introduction to Knowledge Extraction
What is Knowledge Extraction?
“The processing of natural language text and to retrieve occurrences of a particular class of objects or events and occurrences of relationships among them” – Russel & Norvig
Primary Types of Information Extraction
Extraction from unstructured sources
Extraction from structured sources

5. Extraction From Unstructured Sources
There have been several attempts to extract knowledge from text leading may approaches
These approaches can be distinctly grouped as follows
Traditional Information Extraction
Automatic Content Extraction(ACE)
Ontology Based Information Extraction(OBIE)

6. Traditional Information Extraction
Borne out of the initial MUC conferences
Compromises of five steps
Named Entity Recognition
Conference Resolution
Template Element Construction
Template Relation Construction
Scenario Template Production

7. Named Entity Recognition
Identifies Proper Nouns
Simplest task
Provides more than 90% accuracy
Domain Dependent
Coference Resolution
Relates noun-phrases in text to real world entities
Identity of reference between markables
Can be definite noun phrases, demonstrative noun phrases, proper names, appositives, sub–noun phrases that act as modifiers, pronouns, and so on

8. Template Element Construction
Extracts Information related to person or organization entities
Draws evidence from anywhere in the text
Basically adds descriptive information to NER results using CO
Domain Dependent
Template Relation Construction
Identifies relations in the templates identified in previous task
for example, an employee relationship between a person and a company, a family relationship between two persons.
Central feature of almost any information extraction task

9. Scenario Template Construction
Uses all the results from previous tasks
Extracts pre-specified event information
Then, relates the event information to a particular entity involved in the event
More difficult IE tasks – Only 60% accuracy, relative to 80% in humans

10. Automatic Content Extraction
Immediate Successor of traditional IE
Similar to traditional IE has 4 steps.
Data Annotation offered a unique approach to cross domain extraction
Compromises of four steps(occurs simultaneously)
Entity Detection and Tracking
Relation Detection and Characterization

11. Entity Detection and Tracking
Focuses on the identification of entities – not just names
All mentions of a entity are found and collected
Relation Detection and Characterization
Detects relations between pairs of previously detected entities
Divides into 5 general relation
Role, the role a person plays in an organization,
Part, i.e., part-whole relationships, subtyped as Subsidiary, Part-Of, or Other
At, location relationships,4. Near, to identify relative locations
Near, to identify relative locations
Social, social relations

12. ACE – Data Annotation Key Features of ACE over Traditional IE
This is the Entity Linking Task
Establishes co-reference between entity mentions
Produces both training and test data for common research and evaluation tasks
Three types of data annotation:
EDT Annotating – Tagging of all mentions of entities in the document.
RDC Annotation – Identifying all relationships between entities.
VDC Annotation – Identifying the events the previously identified entities participate in
Issue is manual process – does not scale to the breadth of web

13. ACE – Conclusion Key Features of ACE over Traditional IE
This is the Entity Linking Task
Establishes co-reference between entity mentions
Produces both training and test data for common research and evaluation tasks
Three types of data annotation:
EDT Annotating – Tagging of all mentions of entities in the document.
RDC Annotation – Identifying all relationships between entities.
VDC Annotation – Identifying the events the previously identified entities participate in

14. Ontology Based Information Extraction (OBIE)
OBIE has emerged as another subfield in IE
Ontologies play a crucial role in the IE process
Ontologies are used in the information extraction process and the output is also generally an ontology.
Ontologies are usually specific to the domain or which it is created.
Features of OBIE’s:
Process unstructured or semi-structured natural language text
Output should be in ontology format
Ontologies should supplement existing IE processes

15. Common methods of IE in OBIE
Linguistic Rules Using Regular Expressions
Provides good results despite simplicity
Have to manually read documents and create rules
Gazeteer Lists
The words to be recognized are provided to the system in the form of a list
Web Based Search
General idea behind this approach is using the web as a big corpus
Partial Parse Trees
Construct a semantically annotated parse tree for the text

16. The Proposed System

17. Conclusion

18. Questions?

19. Thank You!