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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.