Leveraging sources of collective wisdom on the web for discovering technology synergies

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.

REFERENCES

• 1: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 2: Measuring semantic similarity between words using web search engines, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242675]
• 3: Borgida, A., Walsh, T., and Hirsh, H. Towards measuring similarity in description logics. In Proceedings of the International Workshop on Description Logics (Ediburgh, UK, July 2005), vol. 147 of CEUR Workshop Proceedings.
• 4: Budanitsky, A., and Hirst, G. Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In Proceedings of the Workshop on WordNet and Other Lexical Resources (Pittsburgh, PA, USA, June 2000).
• 5: Freund, Y., and Schapire, R. Experiments with a new boosting algorithm. In International Conference on Machine Learning (Bary, Italy, July 1996), Morgan Kaufmann, pp. 148--156.
• 6: Prasanna Ganesan , Hector Garcia-Molina , Jennifer Widom, Exploiting hierarchical domain structure to compute similarity, ACM Transactions on Information Systems (TOIS), v.21 n.1, p.64-93, January 2003 [doi>10.1145/635484.635487]
• 7: Yuhua Li , Zuhair A. Bandar , David McLean, An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources, IEEE Transactions on Knowledge and Data Engineering, v.15 n.4, p.871-882, July 2003 [doi>10.1109/TKDE.2003.1209005]
• 8: Dekang Lin, An Information-Theoretic Definition of Similarity, Proceedings of the Fifteenth International Conference on Machine Learning, p.296-304, July 24-27, 1998
• 9: Lutz, C., Wolter, F., and Zakharyaschev, M. Resasoning about concepts and similarity. In Description Logics (Rome, Italy, September 2003), D. Calvanese, G. D. Giacomo, and E. Franconi, Eds., vol. 81 of CEUR Workshop Proceedings.
• 10: Ana G. Maguitman , Filippo Menczer , Heather Roinestad , Alessandro Vespignani, Algorithmic detection of semantic similarity, Proceedings of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1060745.1060765]
• 11: George A. Miller, WordNet: a lexical database for English, Communications of the ACM, v.38 n.11, p.39-41, Nov. 1995 [doi>10.1145/219717.219748]
• 12: Resnik, P. Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11 (1999), 95--130.
• 13: Mehran Sahami , Timothy D. Heilman, A web-based kernel function for measuring the similarity of short text snippets, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135834]
• 14: Upendra Shardanand , Pattie Maes, Social information filtering: algorithms for automating “word of mouth”, Proceedings of the SIGCHI conference on Human factors in computing systems, p.210-217, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223931]
• 15: Peter D. Turney, Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL, Proceedings of the 12th European Conference on Machine Learning, p.491-502, September 05-07, 2001
• 16: Cai-Nicolas Ziegler , Sean M. McNee , Joseph A. Konstan , Georg Lausen, Improving recommendation lists through topic diversification, Proceedings of the 14th international conference on World Wide Web, May 10-14, 2005, Chiba, Japan [doi>10.1145/1060745.1060754]
• 17: Cai-Nicolas Ziegler , Kai Simon , Georg Lausen, Automatic computation of semantic proximity using taxonomic knowledge, Proceedings of the 15th ACM international conference on Information and knowledge management, November 06-11, 2006, Arlington, Virginia, USA [doi>10.1145/1183614.1183682]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

General Terms:
Algorithms, Experimentation, Human Factors, Measurement

Keywords:
collective wisdom, semantic similarity, text mining, web 2.0