The improvement of the efficacy of enterprise crowdsourcing activities is heavily dependent on finding, sharing, and integrating the right information for certain use cases. These efforts may include activities such as recommending a crowdsourcing task to a competent worker or evaluating an ongoing or completed crowdsourcing project. However, to pave the way for intelligent enterprise crowdsourcing platforms, the semantic richness of the data must be improved. Therefore, an ontology including a wide set of classes and properties is proposed in this paper. The ontology development is based on the ontology engineering methodology. A first general assessment of the ontology is given at the end of the paper, which describes how it addresses major crowdsourcing requirements.

1. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Developing an Ontology for Enterprise
Crowdsourcing
MKWI 2014, Paderborn, February the 26th, 2014
Lars Hetmank

2. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Agenda
1
Enterprise Crowdsourcing
2
Current Situation & Problem Relevance
3
Anticipated Benefits & Requirements
4
Research Objective & Methodology
5
CSM Ontology
6
Conclusion & Future Work
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 2 | 18

3. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Enterprise Crowdsourcing
“ An online, distributed
problem-solving and production
model
that leverages the collective
intelligence of online communities
to serve specific organizational goals.”
(Brabham, 2013)
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 3 | 18

4. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Current Situation & Problem Relevance
(source: Amazon Mechanical Turk)
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 4 | 18

5. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Benefits & Requirements
CROWDSOURCING SYSTEM (CSS)
2
1
1
1
Task specification
Task allocation
3
1
Team building
identify
propose
form
define
select
Requester
4
1
Monitoring
5
1
Crowd
Crowdsourcing task
evaluate & adjust process
Interoperability
EXTERNAL BUSINESS
APPLICATIONS
Search
engines
CSS
Knowledge
repository
Enterprise
SNS
HR
database
...
Key requirements in enterprise crowdsourcing environments (source: own illustration)
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 5 | 18

6. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Research Objective & Methodology
 Research Objective:
 Development of a lightweight and extensible
ontology for capturing, storing, utilizing, and
sharing crowdsourcing data that improves the
automation and interoperability in enterprise
crowdsourcing environments
 Semantic Web vocabulary
 Methodology:
 Design Science
 Ontology Engineering
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 6 | 18

7. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Ontology Engineering
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 7 | 18

8. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Literature Review (Source 1)
Article
Crowdsourcing
systems on the
World-Wide
Web
(Doan,
Ramakrishnan,
& Halevy, 2011)
Dimension
Semantic Entity
Type of target problem
Type of action
Design of incentive mechanism
Reward and incentive mechanism
Task complexity
Complexity Level
Impact Level
Approach to combine solutions
Type of aggregation
Method to evaluate users
Evaluation mechanism
Degree and distribution of manual effort
Type of aggregation, evaluation mech.
Role of human users
Human requirement
Type of architecture
Technical requirement
…
February 26th,
2014
Interaction mode
Impact of contribution
…
Nature of collaboration
…
Developing an Ontology for Enterprise
Crowdsourcing
Slide 8 | 18

9. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Literature Review (Preliminary Result 1)
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 9 | 18

10. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
System Analysis (Source 2)
microtask
open innovation and co-creation
design
job marketplace
crowdfunding
software testing & translation
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 10 | 18

11. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
System Analysis II
Task properties
Platform
Task specification
Task description
Amazon mTurk
Atizio
crowdSPRING
…
project name, task
title, task
description,
keywords, task
type (categorize,
collect data,
moderate, get
sentiment, survey,
tag, transcribe,
create content),
instructions
title, description,
image, additional
information (text,
document),
important
information,
acceptance
criteria, thank-you
text, visibility
project title,
project
description,
external resources
...
February 26th,
2014
Task allocation
Time and
priority
duration,
expiration,
approval time
after completion,
Reward
Evaluation
Requester-oriented
reward per
assignment
-
qualification type,
approval rate,
number of approved
tasks
duration (start
and end
date/time)
amount of
(alternative)
reward,
-
-
end date
amount of
payment
-
…
…
…
ParticipantOriented
creation date, task
available, reward
amount, expiration
date, duration
Workflow and
quality control
User properties
(requester and
participant)
number of
assignments per
task, status (in
progress, for
review, reviewed)
name, login name,
contact address
information, prepaid
balance
reward, accepted
languages (de, fr,
en), duration
user activity (ideas,
projects,
comments,
comment
evaluation, idea
evaluation, time of
membership)
specialization,
country, language
product category,
activity score, award,
time, contributions,
status
user activity
(reputation score,
projects, awarded
projects)
…
…
…
first name, last name,
address (street, zip
code, city, country),
age, about me,
website, interests,
profession, job status,
educational level,
languages, references,
career/CV, contact list
first name, last name,
about me, address
(city, state, postal
code, country),
language, time zone,
specialization, profile
image, email, portfolio
items
…
Developing an Ontology for Enterprise
Crowdsourcing
Slide 11 | 18

12. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Standards and Semantic Web Vocabularies (Source 3)
people, organizations, and
information objects
events and contextual information
social networks and online
communities
business processes and workflows
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 12 | 18

13. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
CSM Ontology I
 A shared crowdsourcing model (CSM) to
describe the key conceptual entities: user,
project, task, requirement, reward
mechanism, evaluation mechanism, and
contribution
 Includes 24 classes, 22 object properties
and 30 datatype properties + several named
individuals
 Implemented in OWL using Protégé
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 13 | 18

14. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
CSM Ontology II
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 14 | 18

15. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
CSM Ontology Specification
http://www.purl.org/csm/
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 15 | 18

16. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Example: Translate Technical Specification
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 16 | 18

17. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Example SPARQL Query
Which type, nature and amount of reward is appropriate for a
translation task which lasts approximately 30 minutes?
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 17 | 18

18. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management
Conclusion & Future Work
 Balancing between simplicity and semantics of the
crowdsourcing ontology remains a key challenge
 Reuse of existing standards and vocabularies
 Further evaluation steps to achieve successive
adjustment and improvement
 Dissemination in research in practice
(standardization process)
February 26th,
2014
Developing an Ontology for Enterprise
Crowdsourcing
Slide 18 | 18