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| Content Provider | ACM Digital Library |
|---|---|
| Author | Jung, Stefan Ziegler, Cai-Nicolas |
| Abstract | One of the central tasks of R&D strategy and portfolio management at large technology companies and research institutions refers to the identification of technological synergies throughout the organization. These efforts are geared towards saving resources by consolidating scattered expertise, sharing best practices, and reusing available technologies across multiple product lines. In the past, this task has been done in a manual evaluation process by technical domain experts. While feasible, the major drawback of this approach is the enormous effort in terms of availability and time: For a structured and complete analysis every combination of any two technologies has to be rated explicitly. We present a novel approach that recommends technological synergies in an automated fashion, making use of abundant collective wisdom from the Web, both in pure textual form as well as classification ontologies. Our method has been deployed for practical support of the synergy evaluation process within our company. We have also conducted empirical evaluations based on randomly selected technology pairs so as to benchmark the accuracy of our approach, as compared to a group of general computer science technologists as well as a control group of domain experts. |
| Starting Page | 548 |
| Ending Page | 555 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781605584836 |
| DOI | 10.1145/1571941.1572035 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-07-19 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Text mining Collective wisdom Web 2.0 Semantic similarity |
| Content Type | Text |
| Resource Type | Article |
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