=Paper=
{{Paper
|id=Vol-2032/oaei17_paper4
|storemode=property
|title=I-Match and OntoIdea results for OAEI 2017
|pdfUrl=https://ceur-ws.org/Vol-2032/oaei17_paper4.pdf
|volume=Vol-2032
|authors=Abderrahmane Khiat,Maximilian Mackeprang
|dblpUrl=https://dblp.org/rec/conf/semweb/KhiatM17
}}
==I-Match and OntoIdea results for OAEI 2017==
https://ceur-ws.org/Vol-2032/oaei17_paper4.pdf
I-Match and OntoIdea results for OAEI 2017
Abderrahmane Khiat and Maximilian Mackeprang
Human-Centered Computing Lab, Freie Universität Berlin, Germany
abderrahmane.khiat@fu-berlin.de, maximilian.mackeprang@fu-berlin.de
Abstract. Presenting a set of similar or diverse ideas during the idea generation
process leads ideators to come-up with more creative and diverse ideas. How-
ever, to better assess the similarity between the ideas, we designed two matching
systems, namely I-Match and OntoIdea. In the context of the idea generation pro-
cess, each idea is represented by a set of instances from DBpedia describing the
main concepts of the idea. Then, the developed matching systems are applied to
compute the similarity between a set of instances that represent the ideas. The
purpose of our participation at OAEI is to evaluate our designed instance match-
ing algorithm in order to apply it to assess the similarity between ideas. The re-
sults obtained for the first participation of I-Match and OntoIdea systems at OAEI
2017, on different instance matching tracks are so far quite promising.
Keywords: Collaborative Ideation, Semantic Annotation, Ontology, Instance Match-
ing, OAEI.
1 Introduction
The idea generation process is the key part of innovation. This process aims to gener-
ate ideas to solve problems and challenges. A promising approach for supporting such
process is the ”brainstorming method” [3]. This method seeks to increase the number
of ideas based on ideas of collaborating individuals while restricting criticism.
In addition to leveraging the crowd [10], prior work has shown that generating ideas
that are both creative and diverse can be greatly enhanced through presenting inspira-
tional examples [6]. However, a major issue is ”how to find inspiring ideas from hun-
dreds” [9]. To overcome this challenge, research has shown three ways of selecting a set
of inspiring examples systematically [4, 5]: (1) presenting diverse ideas, (2) presenting
similar ideas and (3) visualizing all ideas.
Our work is in line with approaches that assess the diversity (i.e. low similarity
rating) of inspiring examples automatically [8]. However, assessing similarity between
ideas is challenging due to the form of the ideas, i.e. the ideas are described in a short
unstructured text.
To solve this problem, we propose another strategy from our prior work proposed
in [2]. This strategy consists of two main parts: (1) concepts annotation and (2) an in-
stance matching mechanism. Firstly, we annotated the main concepts of an idea with
instances from DBpedia, a validation through user-based selection of images are car-
ried out in order to obtain the right meaning of the identified concepts. Secondly, these
annotated concepts with a set of instances are used as a support to calculate the sim-
ilarity between ideas using an instance matching system. Using our approach, we can
assess the similarity of two ideas, which can then be used further to select (1) a set of
diverse ideas (low similarity rating), (2) a set of similar ideas (high similarity rating)
that inspire the user to generate more creative ideas. Furthermore, we use the similar-
ity ratings obtained to provide a visualisation of the solution space to give ideators an
overview of the collaborative effort.
In this paper, we focus on the matching part of the proposed solution by describ-
ing our two instance matching systems I-Match and OntoIdea. The designed systems
implement an enhancing algorithm that we proposed in our previous work [1]. The pro-
posed algorithm extracts first all information about the two instances to be matched
and normalizes them using NLP. Then, it applies edit distance as a matcher to calcu-
late the similarities between the normalized information. Finally, the approach selects
the equivalent instances based on the maximum of shared information between the two
instances.
2 Instance Matching Algorithm
We summarize the algorithm of our developed systems to provide a general idea of the
proposed solution. It consists of the following successive phases:
2.1 Extraction and Normalization
The system extracts from each individual Ii P1 m1 ; P2 m2 ,... a set of information m1 ,
m2 , ... using different properties P1 , P2 , .... Then, NLP techniques are applied to nor-
malize these information. In particular, three pre-processing steps are performed: (1)
case conversion (conversion of all words in same upper or lower case) (2) lemmatiza-
tion stemming and (3) stop word elimination. Since String based algorithm is used to
calculate the similarities between information, these steps are necessary.
2.2 Similarity Calculation
In this step, the system calculates the similarities between the normalized informations
using edit distance as string matcher. Our system selects the maximum similarity values
calculated between different informations by edit distance. If two informations are the
same (based on maximum similarity values) the counter is incremented to 1, etc.
2.3 Identification
Finally, we apply a filter on maximum counter values in order to select the correspon-
dences which mean that the selected correspondences (equivalent individuals) are those
who share maximum informations.
3 Experimentation
The I-Match and Ontoidea systems participated only for instance matching tracks of
OAEI 2017 evaluation campaign. For the results Please refer to the following website:
http://oaei.ontologymatching.org/2017/results/index.html.
4 Conclusion
In this paper, we have introduced I-Match and OntoIdea, two systems specially designed
to compute similarity between instances. The proposed algorithm is useful, especially
when the instances contain terminological information. The developed systems provide
a quite promising results, thus, we will be applied in the context of the idea generation
process to asses similarity between ideas.
As future perspective, we attempt to apply enhance our instance matching algorithm
especially for DORUMUS track.
Acknowledgements The authors acknowledge the financial support of the Federal
Ministry of Education and Research of Germany in the framework of Ideas to Mar-
ket (project number 03IO1617).
References
1. A. Khiat, M. Benaissa and M. A. Belfdhal, ”STRIM results for OAEI 2015 instance matching
evaluation”. In Proceedings of the 10th International Workshop on Ontology Matching co-
located with the 14th International Semantic Web Conference (ISWC 2015), USA, 2015.
2. A. Khiat, M. Mackeprang, C. Mller-Birn, ”Semantic Annotation for Enhancing Collaborative
Ideation”, Semantics 2017, Netherlands.
3. Alex F. Osborn. 1963. Applied imagination; principles and procedures of creative problem-
solving. Scribner, NY, 1963.
4. Pao Siangliulue, Kenneth Arnold, Krzysztof Gajos and Steven Dow. 2015. Toward collab-
orative ideation at scale-leveraging ideas from others to generate more creative and diverse
ideas”. In Proceedings of CSCW, 2015.
5. Pao Siangliulue, Joel Chan, Steven Dow and Krzysztof Gajos. 2016. IdeaHound: improv-
ing large-scale collaborative ideation with crowd-powered real-time semantic modeling. In
Proceedings of UIST.
6. Richard Marsh, Joshua Landau and Jason Hicks. 1996. How examples may (and may not)
constrain creativity. Memory and Cognition, 24 (5), pp. 669–680.
7. Nicholas Kohn and Steven Smith, 2011. Collaborative fixation: Effects of others’ ideas on
brainstorming. Applied Cognitive Psychology 25 (3), pp. 359–371.
8. Joel Chan, Pao Siangliulue, Denisa McDonald, Ruixue Liu, Reza Moradinezhad, Safa Aman,
Erin Solovey, Krzysztof Gajos and Steven Dow. 2017. Semantically Far Inspirations Consid-
ered Harmful? Accounting for Cognitive States in Collaborative Ideation. In Proceedings of
C& C’17.
9. Elahe Javadi, Judith Gebauer and Joseph Mahoney. 2013. The impact of user interface design
on idea integration in electronic brainstorming: an attention based view. Journal of AIS 14,
pp. 1–21.
10. Victor Girotto, Erin Walker and Winslow Burleson. 2017. The effect of peripheral micro-
tasks on crowd ideation. In Proceedings of CHI ’17, pp. 1843–1854.
11. Osvald Bjelland and Robert Wood. 2008. An inside view of IBM’s’ innovation jam’”. MIT
Sloan Management Review 50 (1), pp. 32–40.
12. Joel Chan, Steven Dang and Steven Dow. 2016. IdeaGens: enabling expert facilitation of
crowd brainstorming. In Proceedings of CSCW, pp. 13–16.