Knowledge bases like DBpedia, Yago or Google's Knowledge Graph contain huge amounts of ontological knowledge harvested from (semi-)structured, curated data sources, such as relational databases or XML and HTML documents. Yet, the Web is full of knowledge that is not curated and/or structured and, hence, not easily indexed, for ex- ample social data. Most work so far in this context has been dedicated to the extraction of entities, i.e., people, things or concepts. This poster describes our work toward the extraction of relationships among entities. The objective is reconstructing a typed graph of entities and relation- ships to represent the knowledge contained in social data, without the need for a-priori domain knowledge. The experiments with real datasets show promising performance across a variety of domains. The key distinguishing feature of the work is its focus on highly unstructured social data (tweets and Facebook posts) without reliable grammar structures. Traditional relation extraction approaches supervised , semi-supervised or unsupervised, commonly assume the availability of grammatically correct language corpora.

1. Harvesting Knowledge from Social
Networks:
Extracting Typed Relationships among
Entities
Andrea Caielli, Marco Brambilla, Stefano Ceri, Florian Daniel
marco.brambilla@polimi.it
marcobrambi
SoWeMine Workshop @ ICWE 2017, Rome, Italy

2. Agenda
(1)Context
(2)Objectives
(3)Method
(4)Experiments and Validation
(5)Visualization and Exploration
(6)Conclusions

3. (1) Context

4. Ontology is the philosophical study of
the nature of being, becoming,
existence or reality
and the basic categories of being and their
relations.

5. Formalizing new knowledge is hard
Only high frequency emerges
The long tail challenge

6. Sourcing the Long Tail
Famous Emerging
…

7. (2) Objective

8. Objective
Extraction of relationships
among entities
Reconstruct a typed graph of entities & relationships
Represent the knowledge contained in social data
No need for a-priori domain knowledge

9. Knowledge Enrichment Setting
HF Entity1 HF Entity5
HF Entity2 HF Entity4
HF Entity3
LF Entity1
??
LF Entity2 LF Entity4
LF Entity3
??
High Frequency
Entities
Low Frequency
Entities
??
?? ????
??
Type1
Type11
Type2
Type111
Instances
Types
<<instanceof>>
<<instanceof>>
<<instanceof>>
<<instanceof>>
<<instanceof>>
<<instanceof>>
??
??
??
??
??
Seed Entity
Seed Type
Type of
interest
Legend
Expert inputs
Enrichment problems
Property2
Relations HF - LF entities
Relations LF - LF entities
Typing of LF entities
Extraction of new LF entities
Property1
?? ?? ??
Finding attribute values

10. A Practical Example

11. A Practical Example

12. Challenge and Innovation
Highly unstructured social data
(tweets and Facebook posts)
No reliable grammar structures

13. (3) Method

14. Analysis Pipeline
(0) Preprocessing
(1) Entity Extraction
(2) Relationship Extraction
(3) Relationship Aggregation
(4) Relationship Typing
(1) Evolution of work presented in:
M. Brambilla, S. Ceri, E. Della Valle, R. Volonterio, and F. Acero Salazar.
“Extracting Emerging Knowledge from Social Media”, WWW 2017.

15. Pipeline Summary

16. (0) Preprocessing
Text cleaning and enrichment
+ Traditional text preprocessing (stemming, …)

17. (1) Entity Extraction
Entity identification and semantic typing
Exploiting:
Stanford
CoreNLP
NER
Dandelion
API

18. (2) Relationship Extraction
Baseline with Stanford OpenIE for triple extraction:
Several issues:
- Meaningless relations
- Wrong relations
- Multiple relations

19. (3) Relationship Aggregation
Sails fans. Season 2 airs on May 24th on History on D Stv Jag Comms
Too many answers
for the same question!
Empirical rules
{"entity1":"Season 2",
"relationship":"air on",
"entity2":"May 24th"}

20. (4) Relationship Typing (A): Synonyms
Exploiting synsets based on WordNet 3.1

21. (4) Relationship Typing (B): Matching
Types

22. (4) Relationship Typing (C): Linguistics
Based on VerbNet
Groupings of verbs based on syntactic and semantic properties

23. Pipeline Implementation

24. (4) Validation

25. Experiments
TV Series: Black Salis, Teen Wolf, Vikings
Milan Fashion Week
Rugby games

26. Domains and quality of results -
summary

27. Relationships and Verb Classes

28. Example: Teen Wolf
0
100
200
300
400
500
600
700
800
Occurrences
Teen Wolf Synonyms Classes

29. Example: Teen Wolf
0
100
200
300
400
500
600
700
800
Occurrences
Teen Wolf Synonyms Classes
OCCURRENCES
TEEN WOLF VERBNET CLASSES

30. Overall Quality Indexes of
Entity and Relationships Extraction

31. (5) Visualization

32. Motivation
Resulting semantic
models extremely
large and hard to
interpret
Example:
Black Sails collection,
containing 1243 entities
and 2025 relations.

33. Exploration
Visualization
Filtering
Navigation

34. Exploration
Visualization
Filtering
Navigation

35. Exploration
Visualization
RELATIONSHIP Filtering
Navigation

36. Examples
Milano
Fashion
Week
Generate
d graph

37. Examples
Milano
Fashion
Week
Generated
graph

38. Examples
Milano
Fashion
Week
Generated
graph

39. Examples
Milano
Fashion
Week
Generated
graph

40. (6) Conclusions

41. Conclusions
Extraction of relevant emerging relationships
feasible even in case of extremely unstructured
and informal content (social media)
Still a long way to perfect extraction:
•N-ary relations
•Time-dependency
•Poor typing of entities in ontologies

42. THANKS!
QUESTIONS?
Andrea Caielli, Marco Brambilla, Stefano Ceri, Florian Daniel
Harvesting Knowledge from Social Networks: Extracting Typed Relationships among Entities
Marco Brambilla @marcobrambi marco.brambilla@polimi.it
http://datascience.deib.polimi.it http://home.deib.polimi.it/marcobrambi