Abstract—Idea Management Systems are an implementation of open innovation notion in the Web environment with the use of crowdsourcing techniques. In this area, one of the popular methods for coping with large amounts of data is duplicate detection. With our research, we answer a question if there is room to introduce more relationship types and in what degree would this change affect the amount of idea metadata and its diversity. Furthermore, based on hierarchical dependencies between idea relationships and relationship transitivity we propose a number of methods for dataset summarization. To evaluate our hypotheses we annotate idea datasets with new relationships using the contemporary methods of Idea Management Systems to detect idea similarity. Having datasets with relationship annotations at our disposal, we determine if idea features not related to idea topic (e.g. innovation size) have any relation to how annotators perceive types of idea similarity or dissimilarity.

1. Idea Relationship Analysis in
Open Innovation CrowdSourcing
systems
Adam Westerski, Carlos A. Iglesias and Javier Espinosa
Grupo de Sistemas Inteligentes
Autor:
Universidad Politécnica de Madrid
Grupo de Sistemas Inteligentes
http://www.gsi.dit.upm.es
Universidad Politécnica de Madrid

2. Outline
 Idea Management Systems
 The problem
 too many similar ideas
 Our solution
 idea relationship hierarchy
 Evaluation
 Ubuntu brainstorm
 Conclusions

3. Idea Management Systems
 Evolution of suggestion boxes...
 for open innovation purposes
 Collect ideas, suggestions, from customers,
employees, ...

4. Innovation through
Idea Management Systems
Idea
Improvement
Idea
Selection
Idea
Generation
Idea Idea
Deployment Implementation

8. Goal: improve how ideas are
automatically filtered

9. Our research so far
 Define Gi2MO ontology for formalising and
interlinking idea management systems (IMS)
 Connect ideas with Enterprise systems
following the Linked Data model
 Integrating Opinion Mining techniques with
IMS
 Idea Classification, filtering and analysis of
idea similarities independent of idea topic (this
talk!)

10. Our research so far...
 Software released as open source, everything
available at http://www.gi2mo.org

11. The problem
 Too many ideas to be analysed
 Many ideas are similar

12. Proposal: similarity relationships

13. Proposal Similarity Relationships

14. Evaluation methodology
Semantic scrapper, available at
http://www.gsi.dit.upm.es/index.php/en/software/details/1/2/software-scrappy.html

15. Manual annotation

16. Experiment I
 Goal: determine if all the relationships are
useful, apart from duplicate relationship
 We compare our manual annotation with the
annotation of duplicate available in Ubuntu
Brainstorm

17. Results Experiment I
 76.7% new relationships compared with the
original IMS

18. Experiment II
 Goal: new relationships help to identify
duplicates and reduce the data set
 We compare
 Detected duplicates in Ubuntu
 Detected duplicates in our dataset based on
the new relationships
 200 ideas with 5 manual annotations suggested
by Lucene
 Same as before but taking into account the
hierarchy

19. Results II
 Ubuntu: 1.13% of duplicate ideas of entire
dataset
 Data set with relationships: 0.5%
 Data set with relationships + hierarchy: 1.95%
→ increase in 95%!

20. Experiment III
 558 out of 1000 ideas are not related each
other (so, they are not similar)
 Analyse similarity based on other annotations:
 Trigger type
 Innovation type
 Proposal type
 Object type

21. Gi2MO Types

22. Results Experiment III
 Calculated similarity S between two related
ideas (iA, iB) for 50 ideas for Metric Mx

23. Analysis
 Same correlation in similar and disjoint idea
subsets with the analysed metrics

24. Conclusions
 Semantic relationship can
improve the detection of
similar ideas
 Relationship hierarchy
and transitivity improve
similarity detection
 Other annotations (Gi2MO
types) and associated
metrics do not help for
detecting similar ideas

25. Questions?