=Paper=
{{Paper
|id=None
|storemode=property
|title=Crowd sourcing through social gaming for community driven
ontology engineering, results and observations
|pdfUrl=https://ceur-ws.org/Vol-689/om2010_poster13.pdf
|volume=Vol-689
|dblpUrl=https://dblp.org/rec/conf/semweb/ChuaCDAELC10
}}
==Crowd sourcing through social gaming for community driven
ontology engineering, results and observations==
https://ceur-ws.org/Vol-689/om2010_poster13.pdf
Crowd sourcing through social gaming for community
driven ontology engineering, results and observations
Alloy Martin Chua, Roland Christian Chua, Arthur Vincent Dychiching, Tinmon
Ang, Jose Lloyd Espiritu, Nathalie Rose Lim, Danny C. Cheng
College of Computer Studies, De La Salle University Manila Philippines
{alchua01, arvolution7000, avd_arthur, angtinmon}@yahoo.com,
espiritu.lloyd@gmail.com, nats.lim@delasalle.ph, danny.cheng@dlsu.edu.ph
Abstract. In developing ontology, expert driven approaches lack the scalability
to accommodate the vast amount of data on the web. As such, the community
is being tapped to build ontologies to cope with highly dynamic data sources.
Common problems (like difficulty of the task, quality of output, and incentives
needed to motivate the community), as discussed by other authors, are
considered. In this paper, we discuss observations on our approach to improve
the quality and sustain community ontology refinement though the use of social
gaming and interaction. Current observations show that profile and knowledge
of the concept in question, understanding and expressivity of the relationships
play a key role in the quality of the result.
Keywords: Matcher selection, self-organization, community driven ontology
engineering, social gaming, incentives.
1 Introduction
Given the disadvantages of expert driven ontology engineering [1], communities of
stakeholders would have to be involved in the engineering process to allow the
capture of emergent data and concepts and keep pace with ontology evolution. Social
Networks are suitable for this process as the members all share common background
knowledge, goals, and interests. Researches by [2][3][4][6] have explored
community-driven approaches or games-with-a-purpose to develop the ontology.
However, these works do not consider the familiarity and perception of the user and
their ability to provide quality feedback (with regards to the concept in question).
Also, the motivation or incentive for the user to continuously provide input to the
system and its sustainability in terms of application propagation and social influence
is not fully considered. Our methodology is to present the engineering task as a
Facebook game to verify a lightweight ontology extracted from delicious and consider
common background knowledge or familiarity to concept. Different aspects of social
influence (both direct and indirect) [5] are used to allow for sustainability and
scalability of the system. In terms of sustainability, we refer to direct influence such
as friend requests to participate, while for indirect influence, we look at general
awareness of peer activities via public postings. For scalability, the community is
allowed to engineer the ontology and version the ontology on a community basis
(small world graphs) and it uses direct and indirect peer influence to allow self
monitoring and propagation of the application. For both cases, activities that promote
social influence are tightly integrated to incentive schemes to motivate the community
to perform those actions.
2 Results and Observations
During our two-month testing with 110 users subdivided into 4 community groupings,
we encountered several issues and discovered some interesting results. It was seen
that groups who are familiar with the area (topic) were able to participate more.
These were also the users who, generally, return more than once; as opposed to the
community that is from a different background. This result serves as a promising
response to the assertion that selection of participants is important and knowledge of
the tag in question by the community is necessary for them to provide feedback. Also,
users are more capable of identifying erroneous relationships as opposed to validating
them. The results show that the majority of the players “disagreed” with the initial
discovered ontology. Perception and interpretation affect how concepts will be
organized. Limiting our current discussion to simply identifying hierarchical
relationships such as “kind-of”, it can be seen that connections validated and
invalidated differ between groups. For the non-computer science community, some
users perceived video as a “kind-of” learning mechanism (when they meant to express
“videos can be used for learning”). This was due to the lack of expressivity of the
relationships in the game. It should be noted that no “kind-of” relationship between
“video” and “learning” was produced from inputs in the computer science user
community. It was observed perspectives and interpretation play key roles and user
profile and social affinity can serve as some of the bases to limit and select users for
participation. Finally, the ontology is able to stabilize and converge; however, it is
still not known whether it is the stability of the domain that allowed the ontology to
converge or the process itself.
References
1. Dieter Fensel. Ontology Management Semantic Web, Semantic Web Services, and
Business Applications. [ed.] Pieter De Leenheer, Aldo de Moor, York Sure Martin Hepp.
New York : Springer Science+Business Media, LLC, 233 Spring Street, New York, NY,
2008.
2. Siorpaes, Katharina, Prantner, Kathrin and Bachlechner, Daniel. Class Hierarchy for the e-
tourism Ontology Version 8. e-Tourism. [Online] November 2004. [Cited: January 6,
2009.] http://e-tourism.deri.at/ont/.
3. Cardoso, Jorge and Sheth, Amit P. Semantic Web Services, Processes and Applications.
United States of America : Springer Science+Business Media, LLC, 2006.
4. Steve LEUNG, Fuhua LIN, Dunwei WEN. Towards a Data-driven Ontology Engineering.
Taipei : The 16th International Conference on Computers in Education, 2008.
5. Eytan Bakshy, Brian Karrer,Lada A. Adamic. Social Influence and the Diffusion of User-
Created Content. California : ACM, 2009. pp. 325-334. ISBN:978-1-60558-458-4.
6. Hepp, Katharina Siorpaes and Martin. OntoGame: Weaving the Semantic Web by Online
Gaming. Tenerife : Springer, 2008.