Idea Relationship Analysis in Open Innovation Crowdsourcing Systems

Abstract

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.

Large Scale Needs-Based Open Innovation via Automated Semantic Textual Similarity Analysis

“…However, existing systems must still rely on administrator oversight or knowledge of users to identify and flag duplicates. Relationships between ideas may be more complex than only duplication; however, annotations of relationship hierarchies also remain largely manual [9]. Idea overflow and redundancy remain a challenge for managing open innovation data [5,9].…”

“…Relationships between ideas may be more complex than only duplication; however, annotations of relationship hierarchies also remain largely manual [9]. Idea overflow and redundancy remain a challenge for managing open innovation data [5,9]. Assessing similarity using pairwise comparisons of submissions analyzed with automated NLP algorithms may identify redundancy and reduce resources needed to manage data.…”