An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
Automatic computation of semantic proximity using taxonomic knowledge
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
Leveraging sources of collective wisdom on the web for discovering technology synergies
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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Spotting and quantifying technological synergies across organizational levels is of utter importance for corporate strategy departments. These efforts aim at saving resources by consolidating scattered expertise and by reusing technologies across multiple product lines. In the past, this task has been done in a manual process by domain experts. While feasible, the major drawback lies in the enormous cost of time: For a structured and complete analysis every combination of any two technologies has to be assessed. We present an approach that discovers those synergies in an automated fashion, using collective wisdom from the Web. Our method has been deployed for the synergy evaluation process within Siemens. We have also conducted 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.