1. Combining human and
computational intelligence for
collaborative knowledge creation
Elena Simperl
Talk at the IEEE International Conference on Intelligent Computer
Communication and Processing, Cluj-Napoca, Romania
8/27/2011 www.insemtives.eu 1
2. Insemtives in a nutshell
• Many aspects of semantic content authoring naturally rely on human
contribution.
• Motivating users to contribute is essential for semantic technologies to
reach critical mass and ensure sustainable growth.
• Insemtives works on
– Best practices and guidelines for incentives-compatible technology design.
– Enabling technology to realize incentivized semantic applications.
– Showcased in three case studies: enterprise knowledge management;
services marketplace; multimedia management within virtual worlds.
www.insemtives.eu 2
3. Incentives and motivators
• Motivation is the driving • Incentives can be related
force that makes humans to both extrinsic and
achieve their goals. intrinsic motivations.
• Incentives are ‘rewards’ • Extrinsic motivation if
assigned by an external task is considered boring,
‘judge’ to a performer for dangerous, useless,
undertaking a specific socially undesirable,
task. dislikable by the
– Common belief (among performer.
economists): incentives • Intrinsic motivation is
can be translated into a
sum of money for all
driven by an interest or
practical purposes. enjoyment in the task
itself.
7. What is different about semantic
systems?
• Semantic Web tools
vs applications.
– Intelligent (specialized)
Web sites (portals) with
improved (local) search
based on vocabularies
and ontologies.
– X2X integration (often
combined with Web
services).
– Knowledge
representation,
communication and
exchange.
8. What do you want your
users to do?
• Semantic applications
– Context of the actual application.
– Need to involve users in knowledge acquisition and
engineering tasks?
• Incentives are related to organizational and social factors.
• Seamless integration of new features.
• Semantic tools
– Game mechanics.
– Paid crowdsourcing (integrated).
• Using results of casual games.
http://gapingvoid.com/2011/06/07/pixie-dust-the-mountain-of-mediocrity/
9. Case studies
• Methods applied
– Mechanism design
– Participatory design
– Games with a purpose
– Crowdsourcing via MTurk
• Semantic content
authoring scenarios
– Extending and populating
an ontology
– Aligning two ontologies
– Annotation of text, media
and Web APIs
10. Mechanism design in practice
• Identify a set of games that represents your situation.
• See recommendations in the literature.
• Translate what economists do into concrete scenarios.
• Assure that the economists’ proposals fit to the concrete situation.
• Run user and field experiments. Results influence HCI,
social and data management aspects.
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11. Factors affecting mechanism
design
Social Nature of good
Goal Tasks
Structure being produced
Communication High High
level (about the Medium Variety of Medium Private good
goal of the tasks) Low Low
Hierarchy
High High neutral
Participation level
Medium Medium
(in the definition Specificity of Public good
of the goal) Low Low
Identification High
High Common resource
with Low
Clarity level Hierarchical
Highly specific
Low Required skills Club good
Trivial/Common
More at http://www.insemtives.eu/deliverables/INSEMTIVES_D1.3.1.pdf and
http://www.insemtives.eu/deliverables/INSEMTIVES_D1.3.1.pdf
8/27/2011 www.insemtives.eu 16
13. Mechanism design for Telefonica
• Interplay of two alternative games
– Principal agent game
• The management wants employees to do a certain action but does
not have tools to check whether employees perform their best effort.
• Various mechanisms can be used to align employees’ and employers’
interests
– Piece rate wages (labour intensive tasks)
– Performance measurement (all levels of tasks)
– Tournaments (internal labour market)
– Public goods
• Semantic content creation is non-rival and non-excludable
• The problem of free riding
• Additional problem: what is the optimal time and effort for
employees to dedicate to annotation
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14. Mechanism design for Telefonica (ii)
• Principal agent game • Public goods game
– Pay-per-performance – To let users know that their
• Points assigned for each contribution was valuable
contribution – The portal should be useful
– Quality of performance • Possibility to search experts,
measurement documents, etc.
• Rate user contributions • Possibility to form groups of
• Assign quality reviewers users and share contributions
– Tournament – The portal should be easy to
• Visibility of contributions by use
single users
• Search for an expert based on
contributions • Experiments
• Relative standing compared to – Pay-per-tag vs winner-takes-
other users it-all for annotation.
4/14/11 www.insemtives.eu 19
15. Knowledge engineering tasks
• Granularity of ontology
engineering activities is too
broad; further splitting is
needed
• Crowdsource very specific
tasks that are (highly) divisible
– Labeling (in different
languages)
– Finding relationships
– Populating the ontology
– Aligning and interlinking
– Ontology-based annotation
– Validating the results of
automatic methods
– …
www.insemtives.eu 20
16. OntoGame API
• API that provides several methods that are
shared by the OntoGame games, such as:
– Different agreement types (e.g. selection
agreement).
– Input matching (e.g. , majority).
– Game modes (multi-player, single player).
– Player reliability evaluation.
– Player matching (e.g., finding the optimal
partner to play).
– Resource (i.e., data needed for games)
management.
– Creating semantic content.
• http://insemtives.svn.sourceforge.net/vie
wvc/insemtives/generic-gaming-toolkit
8/27/2011 www.insemtives.eu 21
18. Lessons learned
• Tasks which can be subject to games
– Definition of vocabulary
– Conceptualization
• Based on competency questions
• Identifying instances, classes, attributes, relationships
– Documentation
• Labeling and definitions
• Localization
– Evaluation and quality assurance
• Matching conceptualization to documentation
– Alignment
– Validating the results of automatic methods
• But, the approach is per design less applicable because
– Knowledge-intensive tasks that are not easily nestable
– Repetitive tasks players‘ retention?
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19. Lessons learned (ii)
• Approach is feasible for mainstream domains, where a
knowledge corpus is available
• Knowledge corpus has to be large-enough to allow for
a rich game experience
– But you need a critical mass of players to validate the
results
• Advertisement is essential
• Game design vs useful content
– Reusing well-kwown game paradigms
– Reusing game outcomes and integration in existing
workflows and tools
• Cost-benefit analysis
