This is an overview presentation about my PhD research, not a very technical one. This was presented in the open session of WebST'16 Summer School in Web Science, July 2016, Bilbao - Spain.

1. Entity-Relationship Extraction from
Wikipedia Unstructured Text
Radityo Eko Prasojo(Rido)
PhD Student @ KRDB, Free University of Bozen-Bolzano
Supervised by:
Mouna Kacimi & Werner Nutt
20.07.16, Bilbao, Spain

2. Automatically generated Manually curated
Automated extraction without
(yet) a KB as a result
Knowledge Vault
[1]
Knowledge Graph
NELL [2]
220/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao
Infobox completion [3] [4]

3. 320/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao

4. 420/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao

5. 520/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao
Where was Obama born?
Who are the children
of Obama?

6. 620/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao
When was Obama born?
Who are the children
of Obama?
Yes we can!
Honolulu, Hawaii
Malia and Sasha Obama

7. 720/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao
Which are Obama’s
favourite sports team?
Does Obama have pets?

8. Our goal is to enrich existing Knowledge Bases by
extracting new facts in the form of machine-readable
entity-relationship from Wikipedia unstructured text.
Specific focus: RDF
820/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao

9. Why is it difficult?
•The extraction problem
• Entity extraction & disambiguation
• Relation extraction
•The representation problem
• Lack of predefined schema/ontology
• Topic-independency
• Complex fact representation
20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 9

10. Why is it difficult? Example
• “Obama is a supporter of the Chicago White Sox”
• Straightforward, singleton information
• Pure syntactic extraction possible
• Barack_Obama supporterOf Chicago_White_Sox
20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 10

11. Why is it difficult? Example
• “Obama is a supporter of the Chicago White Sox”
• Straightforward, singleton information
• Pure syntactic extraction possible
• Barack_Obama supporterOf Chicago_White_Sox
• “He is also primarily a Chicago Bears football fan in the NFL, but in his
childhood and adolescence was a fan of the Pittsburgh Steelers”
• Complex, multiple information
• Semantic understanding necessary
• … how do we represent this?
20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 11

12. Example: representing complex fact
• “He is also primarily a Chicago Bears football fan in the NFL, but in his
childhood and adolescence was a fan of the Pittsburgh Steelers”
• Barack_Obama footballFan Chicago_Bears in NFL
• supporterOf vs footballFan
• Is it necessary to include NFL in the whole relations?
• What about the adjective primarily? What information does it imply?
• Barack_Obama fanOf Pittsburgh_Steelers
• fanOf vs supporterOf
• Missing the time information referred in “in his childhood and adolescence
was”
20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 12

13. Approach
• Document preprocessing to annotate all entity occurences.
• Grammatical dependency to extract (candidate) relations.
• Separation between the extraction problem and the representation
problem
• We first extract all candidate relations and then later apply semantic refinement
for better representation.
20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 13

14. Preliminary results
• Ground truth manually curated from 25 Wikipedia articles of famous
people.
• Preprocessing
• 4 handcrafted extraction rules leveraging grammatical dependency
20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 14

15. Ongoing work
• Automated rule mining
• Semantic refinement for knowledge representation
• Ontology building
• Naming and taxonomy of entities, classes, and relations
• Handling complex fact
• Obama appoints x as y in z
• Handling modality, adjectives, and sentiment
• “In the past”, “it is rumoured that”, “it is not true that”
• Future evaluation
• Bigger ground truth (amount + topic coverage)
• Evaluate how well we enrich existing KBs
20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 15

16. Future work
• Metadata extraction
• Data quality, data completeness
• Natural language question answering based on the enriched KB.
20/07/16 RE Prasojo | KRDB @ UNIBZ | WebST'16, Bilbao 16