WordNet: a lexical database for English
Communications of the ACM
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modern Information Retrieval
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
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
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Algorithmic detection of semantic similarity
WWW '05 Proceedings of the 14th international conference on World Wide Web
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
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
Discovery of Technology Synergies through Collective Wisdom
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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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.