Strephit is a Wikimedia Foundation project aimed at improving the reliability of Wikidata content through an NLP pipeline that extracts structured data from raw texts. The project addresses challenges in content validation and aims to generate trust by using references to authoritative sources for data. Initial focuses include harvesting data from biographies, companies, and biomedical sources to ensure robust and quality content creation.

1. STREPHIT
A WIKIMEDIA FOUNDATION
IEG PROJECT
MARCO FOSSATI - HJFOCS - FOSSATI@FBK.EU
TRENTO, 15TH JANUARY 2016

2. HAPPY BIRTHDAY,
WIKIPEDIA!
Preamble
PREAMBLE 2

3. INDIVIDUAL
ENGAGEMENT
GRANTS
Preamble
PREAMBLE 3

4. THE FREE
KNOWLEDGE BASE
THAT ANYONE CAN EDIT
Preamble
PREAMBLE 4

5. 5
MARCO FOSSATI
EMILIO DORIGATTI
WHO?

6. WHO?
‣ ADVISOR: CLAUDIO GIULIANO
‣ VOLUNTEERS:
‣ AUVA87, BOLIOLIANDREA, DANROK,
NISPRATEEK, PROJEKT ANA,
VLADIMIR ALEXIEV
6

7. WHAT?
‣ IS A NLP PIPELINE
‣ HARVESTS STRUCTURED DATA FROM
RAW TEXT
‣ PRODUCES WIKIDATA CONTENT WITH
REFERENCES
7

8. WHY?
1. THE CRITICAL ISSUE
2. THE VISION
3. THE TECHNICAL PROBLEM
8

9. ▸ Reliability of content across Wikimedia
projects
▸ Trust needed on the content addition
process
▸ Mature in Wikipedia, but what about
Wikidata?
WHY
THE CRITICAL ISSUE
9

10. WHY
THE CRITICAL ISSUE
▸ StrepHit = novel, automatic process
▸ Generates trust and reliability over
Wikidata content
▸ Alleviates the burden of manual
curation
10

11. WHY
THE VISION
▸ Wikidata as a
central Open
Data hub
11

12. WHY
THE TECHNICAL PROBLEM
▸ Content should be validated against
third-party resources
▸ References to external authoritative
sources
▸ Ensure at least one reference for each
piece of data
12

13. HOW?
‣ INPUT = PRIMARY SOURCES CORPUS
‣ OUTPUT = DATASET FOR WIKIDATA
‣ AUTHENTICATE EXISTING CONTENT
‣ PROPOSE NOVEL CONTENT
‣ VIA REFERENCES TO SUCH SOURCES
13

14. HOW?
‣ LEXICOGRAPHICAL ANALYSIS
‣ RELATION EXTRACTION
‣ FRAME SEMANTICS
‣ MACHINE LEARNING
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15. HOW
MAIN TASKS
1. Sources selection
2. Corpus harvesting
3. Corpus analysis
4. Frame repository selection
5. Training set construction
6. Frame extraction
7. Dataset production
15

16. WHERE?
PRIMARY SOURCES TOOL
16

17. A. BIOGRAPHIES
B. COMPANIES
C. BIOMEDICAL
which domain?
FIRST STEP 17
THANKS NEMO FOR OUR PRECIOUS CONVERSATION

18. FIRST STEP
BIOGRAPHIES
▸ plenty of existing data
▸ broad coverage
▸ potentially easy to find valuable primary sources
18
LIBRARIANS,
WHAT DO YOU THINK?

19. FIRST STEP
COMPANIES
▸ relatively biased domain
▸ ad-prone content
▸ the company edits the page on the company itself
▸ low-quality data
19

20. FIRST STEP
BIOMEDICAL
▸ great primary source
▸ PubMed: scientific papers
▸ proof of usage for an Open Access corpus
20

21. OPEN DISCUSSION
DOMAIN + SOURCES SELECTION
MARCO FOSSATI - HJFOCS - FOSSATI@FBK.EU
TRENTO, 15TH JANUARY 2016
THIS WORK IS LICENSED UNDER A CC BY SA 4.0 LICENSE
https://pad.okfn.org/p/strephit