Altuntas, S., Dereli, T., & Kusiak, A. (2015). Forecasting technology success based on patent data. Technological Forecasting and Social Change,
Amanatidou, E., Butter, M., Carabias, V., Könnölä, T., Leis, M., & Saritas, O., et al. (2012). On concepts and methods in horizon scanning: Lessons from initiating policy dialogues on emerging issues. Science and Public Policy,
Angeli, G., Premkumar, M. J., & Manning, C. D. (2015). Leveraging linguistic structure for open domain information extraction. In Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing of the Asian Federation of Natural Language Processing, ACL (pp. 26–31).
Anick, P. G., Verhagen, M., & Pustejovsky, J. (2014). Identification of technology terms in patents. In LREC (pp. 2008–2014).
Assfalg, J., Bernecker, T., Kriegel, H. P., Kröger, P., & Renz, M. (2009, April). Periodic pattern analysis in time series databases. In Database systems for advanced applications (pp. 354–368). Berlin: Springer.
Blei, D. (2012). Probabilistic topic models. Communications of the ACM,
Burmaoglu, S., & Saritas, O. (2016). Changing characteristics of warfare and the future of Military R&D. Technological Forecasting and Social Change, Article in Press. doi:10.1016/j.techfore.2016.10.062.
Carvalho, K. M., Winter, E., & de Souza Antunes, A. M. (2015). Analysis of technological Developments in the treatment of Alzheimer’s disease through patent documents. Intelligent Information Management,
Cassiman, B., Veugelers, R., & Zuniga, M. P. (2007). Science linkages and innovation performance: An analysis on CIS-3 firms in Belgium.
Chen, D., & Manning, C. (2014). A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 740–750). Doha, Qatar: Association for Computational Linguistics.
Chidamber, S. R., & Kon, H. B. (1994). A research retrospective of innovation inception and success: The technology–push, demand–pull question. International Journal of Technology Management,
Daim, T. U., Chiavetta, D., Porter, A. L., & Saritas, O. (Eds.). (2016). Anticipating future innovation pathways through large data analytics. Berlin: Springer.
de Miranda Santo, M., Coelho, G. M., dos Santos, D. M., & Fellows Filho, L. (2006). Text mining as a valuable tool in foresight exercises: A study on nanotechnology. Technological Forecasting and Social Change,
Ena, O., Mikova, N., Saritas, O., & Sokolova, A. (2016). A technology trend monitoring methodology: The case of semantic technologies. Scientometrics,
Guan, J., & He, Y. (2007). Patent-bibliometric analysis on the Chinese science—technology linkages. Scientometrics,
Huang, Y., Schuehle, J., Porter, A. L., & Youtie, J. (2015). A systematic method to create search strategies for emerging technologies based on the Web of Science: Illustrated for ‘Big Data’. Scientometrics,
Jones, K. S. (1965). Experiments in semantic classification. Mech Translation,
Judea, A., Schütze, H., & Brügmann, S. (2014). Unsupervised training set generation for automatic acquisition of technical terminology in patents. In COLING (pp. 290–300).
Kaufman, L., & Rousseeuw, P. J. (1987). Clustering by means of medoids. In Y. Dodge (Ed.), Statistical data analysis based on L1-norm and related methods (pp. 405–416). Amsterdam: North-Holland.
Kerr, C. I., Mortara, L., Phaal, R., & Probert, D. R. (2006). A conceptual model for technology intelligence. International Journal of Technology Intelligence and Planning,
Kim, J., Hwang, M., Jeong, D. H., & Jung, H. (2012). Technology trends analysis and forecasting application based on decision tree and statistical feature analysis. Expert Systems with Applications,
Lahoti, G., Porter, A., Zhang, C., Youtie, J., Wang, B., & Hicks, D. (2015). Tech mining to validate and refine a technology roadmap. In Proceedings of the 5th global TechMining conference. Atlanta, USA.
Li, H., Xu, F., & Uszkoreit, H. (2011). TechWatchTool: innovation and trend monitoring. In RANLP (pp. 660–665).
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014, June). The Stanford CoreNLP natural language processing toolkit. In ACL (System Demonstrations) (pp. 55–60).
Martin, B. R. (1995). Foresight in science and technology. Technology Analysis and Strategic Management,
Mikova, N., & Sokolova, A. (2014). Global technology trends monitoring: Theoretical frameworks and best practices. Foresight-Russia,
Miles, I., Saritas, O., & Sokolov, A. (2016). Foresight for Science, Technology and Innovation. Berlin: Springer.
Park, H., Ree, J. J., & Kim, K. (2013). Identification of promising patents for technology transfers using TRIZ evolution trends. Expert Systems with Applications,
Porter, A. L. (2009). Tech mining for future-oriented technology analyses. In J. C. Glenn & T. J. Gordon (Eds.), Futures research methodology.
Porter, A., & Cunningham, S. (2004). Tech mining: Exploiting new technologies for competitive advantage. Hoboken: Wiley.
Saritas, O. (2013). Systemic foresight methodology. In D. Meissner, L. Gokhberg, & A. Sokolov (Eds.), Science, technology and innovation policy for the future: Potentials and limits of foresight studies (pp. 83–117). Berlin: Springer.
Saritas, O., & Burmaoglu, S. (2015). Future of sustainable military operations under emerging energy and security considerations. Technological Forecasting and Social Change,
Saritas, O., & Smith, J. E. (2011). The big picture–trends, drivers, wild cards, discontinuities and weak signals. Futures,
Scherer, F. M. (1982). Demand-pull and technological invention: Schmookler revisted. The Journal of Industrial Economics, 30, 225–237.
Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle (Vol. 55). Transaction Publishers.
Smith, J., & Saritas, O. (2011). Science and technology foresight baker’s dozen: a pocket primer of comparative and combined foresight methods. Foresight,
Sokolov, A., & Chulok, A. (2016). Priorities for future innovation: Russian S&T foresight 2030. Futures, 80, 17–32. doi:10.1016/j.futures.2015.12.005.
Sun, G., Guo, Y., & Yang, F. (2015). Technology early warning model: A new approach based on patent data. In Proceedings of the Second International Workshop on Patent Mining and its Applications (IPAMIN). May 27–28, 2015, Beijing, China. Accessed 14 Mar 2017. http://ceur-ws.org/Vol-1437/ipamin2015_paper4.pdf.
Szu-chia, S. L. (2010). Scientific linkage of science research and technology development: A case of genetic engineering research. Scientometrics,
Trumbach, C. C., Payne, D., & Kongthon, A. (2006). Technology mining for small firms: Knowledge prospecting for competitive advantage. Technological Forecasting and Social Change,
Verhaegen, P. A., D’hondt, J., Vertommen, J., Dewulf, S., & Duflou, J. R. (2009). Relating properties and functions from patents to TRIZ trends. CIRP Journal of Manufacturing Science and Technology,
Wang, X., Qiu, P., Zhu, D., Mitkova, L., Lei, M., & Porter, A. L. (2015). Identification of technology development trends based on subject–action–object analysis: The case of dye-sensitized solar cells. Technological Forecasting and Social Change,
Yoon, B. (2008). On the development of a technology intelligence tool for identifying technology opportunity. Expert Systems with Applications,
Yoon, J., & Kim, K. (2011). An automated method for identifying TRIZ evolution trends from patents. Expert Systems with Applications,
Yoon, J., & Kim, K. (2012a). An analysis of property–function based patent networks for strategic R&D planning in fast-moving industries: The case of silicon-based thin film solar cells. Expert Systems with Applications,
Yoon, J., & Kim, K. (2012b). TrendPerceptor: A property–function based technology intelligence system for identifying technology trends from patents. Expert Systems with Applications,
Zhang, Y., Zhang, G., Chen, H., Porter, A. L., Zhu, D., & Lu, J. (2016). Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research. Technological Forecasting and Social Change,