Proceedings of the 4th Global TechMining Conference (Leiden, Netherlands)
The goal of the conference is to help build cross-disciplinary networks of analysts, software specialists, and researchers to advance the use of textual information in multiple science, technology, and business development fields. Within this context, conference themes will include, but are not limited to:
- Sourcing, preparing, and interpreting data sources including patents, publications, webscraping, and other novel data sources
Text-mining tools and methods
- Best practices in software-based topic modeling, clumping, association rules, term manipulation, text manipulation, etc.
- Future-Oriented Technology Analysis (FTA)
- Intelligence gathering to support decision-making in the private sector (e.g., Management of Technology)
Automated identification of new technology trends (trend monitoring, trend hunting, trend watch) is among the hot topics in technology management. Despite many beneficial results in this field, almost no solutions allow users to escape from getting too general or garbage results which make it impossible to identify trends at the stage of weak signals. Lack of attention is paid to automated labeling and merging (for the ‘same’ trends).
Our approach aimed at overcoming such drawbacks is based on the ‘BlackBox’ principle. The concept of a technology trend (TT) is characterized by a complex nature, low formalization level, blurred boundaries, and high degree of domain dependency leading to the need for expert knowledge. For all that, ‘Big Data’ in IT and ‘Genome Editing’ in Healthcare should have some similar features which actually allow us to name both phenomena ‘a TT’. This leads us to an idea of hunting for domain independent ‘external signs’ (trend indicators) while letting a TT itself stay a black box for an observer.
We employ Gartner’s Hype Cycle in our methodology. We build an elaborate ontology of a TT and a system of indicators of TTs ‘presence’ in documents of various genres. The indicators are interrelated with the ontology through linguistic and extra linguistic markers. Both markers and text genres are mapped onto the phases of a technology life cycle. The ontology-driven information extraction (IE) is carried out.