Building a social graph for the history of Europe: the CUbRIK histoGraph

CUbRIK Summer School 2014


histoGraph

Building a social graph for the history of Europe

Lars Wieneke, CVCE – Luxembourg


2-4/07/2014 CUbRIK Summer School 1

Agenda

The CVCE

What are the Digital Humanities?

The DHLab at the CVCE

Vision: From image collection to Social Graph

The CUbRIK approach

Demo

Challenges, Lessons learned & Outlook


2-4/07/2014 CUbRIK Summer School 2

The CVCE


3


4


5


6


WHAT ARE THE DIGITAL HUMANITIES?

7


What are the digital humanities?

8

„[…] the issue would be not how much

computing we need for getting the answers, but

how much computer science needs us to ask

the right questions.“http://whatisdigitalhumanities.com, Domenico Fiormonte, Université Roma Tre

Digital Humanities is the application of

computational methods and tools for the

humanities

but



9

F.Kapplan, EPF LausanneVenice Fall Digital Humanities School

2013



10

F.Kapplan, EPF LausanneVenice Fall Digital Humanities School

2013


The DHLab at the CVCE

11

European Integration Studies

Humanities

Development&

Operations

DHLab


OUR VISION: BUILDING A SOCIAL GRAPH FROM IMAGE COLLECTIONS

12


Building a social graph from image collections

13

13


Building a social graph from image collections

14

histograph.eu

histograph.eu


THE CUBRIK APPROACH

15


Four pillars

16

Researcher Requirements

Entity Repository

Efficient Indexation Process

Toolchain for visualization and analysis


Sourcing researcher requirements

17

Requirements

User pull

Technology push



18

Selection of targetuser group

First draft of the appscenario

Feedback on technical scope

Exploratory interviews

(daily work practices)

Second draft of the app scenario

Focus group(user needs and app

scenarios)Feedback on

technical feasability

Lessons learned:issues and features

Specification

Implementation 1. demonstrator

Workshop: Review of app and features

Revised specification

Implementation 2.demonstrator

Evaluation and test

Stage 1

Stage 2

Stage 3

Stage 4

Stage 5

Users Requirements Technology




19

Selection of target user group

First draft of the app scenario

Feedback ontechnical scope

Exploratory interviews

(daily work practices)

Second draft of the app scenario

Focus group(user needs and app

scenarios)Feedback on

technical feasability

Lessons learned:issues and features

Specification Implementation 1. demonstrator

Workshop: Review of app and features

Revised specification Implementation 2. demonstrator

Evaluation and test

Stage 1

Stage 2

Stage 3

Stage 4

Stage 5




Building an entity repository

20


Efficient indexation

21

Conflict

(e.g., “Image contains

‘Romano Prodi’ ”

Confidence = low)?

Conflict store Conflict

manager

Conflict resolution

task store

Conflict resolution

task: conflict,

required skill, priority, ..

CUbRIK app

for Conflict

resolution

Game Q&ACrowdtask


Efficient indexation

22

Face

detection

Face

identification

Clickworkers

Crowd Face

posit ion

validation

Expert

Crowd

Expert

validation

Collect ion

ingestion

Social Graph

creation

SMILA


Demo

23

http://histograph.eu/

http://histograph.eu/


Challenges

Detection and identification ofidentities/places/events in time

Verification of identities/places/events in time

Analysis of relationships (e.g. co-occurrences)

Rights aware crawling and storage

Verification of provenance and licenseinformation

Truth and provenance

24


Approach

Crowd-sourced verification of detected faces(false positives/negatives)

Verification of identities through/places/events intime social networks of experts

Visual knowledge discovery/exploration

Integrated rights aware crawling and storage

Integrated license and provenance management

25


Discursive interface

CUbRIK Presentation 26

Voting (ref. Stackoverflow) is supported

through source referencing and

explanations. Solutionspace

Shared visualization

Multipleperspectives

Problem space


Motivation, gamification and reputation

Different levels of motivation for different tasks

Face position validation -> Monetary incentives

Face validation -> reputation gain

Actions in the application can be rewardedthrough the system and through other users

Goal to motivate AND to capture reputation

27


Lessons learned

No one truth in history but interpretation,context and discussion

Therefore need to represent ambivalence,contradictions and discussion

Close ties between data representation (Socialgraph) and their original context (primarysources)

28


Next step: evaluation

4 week evaluation phase in July

kick-off with a physical workshop in Luxembourg

Closure with a virtual workshop

29


THANK YOU FOR YOURATTENTION

30