Dynamic Technology trend monitoring from unstructured data using Machine learning

Type: MA thesis

Status: finished

Date: August 1, 2020 - February 1, 2021

Supervisors: Andreas Maier, Thomas Kinkeldei

New technologies are enablers for product and process innovations. However, in the multitude of available technologies on the market, identifying the relevant and new technologies for one’s own company and one’s own problem is associated with a high effort. ROKIN as a technology platform offers a key component for the rapid identification of new technologies and thus for the acceleration of innovation processes in companies. For this purpose, new technologies are identified in the Internet, profiles are created and these are made available to companies via an online platform. Companies are provided with suitable solution proposals for their specific problem.
ROKIN automates the individual steps for this process, from data collection via web crawler, through the matching process, to the visualization of information in technology profiles. A central point in this process is detecting newest technological trends in the market in the collected data. This allows companies to keep up with upcoming technological shifts.
Due to the recent successes with so-called “Transformer Models” (e.g. “Bidirectional Encoder Representations from Transformers” (BERT)), new possibilities in the recognition and understanding of texts are opening up like never before. These models were trained domain-independent using general information from Wikipedia and book corpus. An open question is how these approaches perform in a domain-specific context like engineering. Can the sentiment understanding of such algorithms be used to improve existing classical NLP keyword analysis and topic modelling for trend detection? Especially the early onset of a trend, where little evidence through keywords is given a sentiment understanding using transformer based approaches might help. The goal is therefore to implement and extend existing classical NLP algorithms with Transformer models and use the new model to identify trends in big amount of engineering text documents.
• Literature research and analysis of existing NLP tools for trend detection (transformers as well as classic keyword analysis and topic modelling approaches).
• Setting up an information database (via Web-Crawling and Google Search APIs) for a given problem out of the engineering environment of a company (topic provided by ROKIN).
• Semantic modelling and analysis of the information database for identifying technology trends by different approaches of NLP algorithms.
• Strengths and weaknesses evaluation in respect to the created algorithms and based on the individual results.
• Development of a strategy or approach for an ideal trend detection strategy. Specific to early stage trend detection.
• Evaluation and optimization of the algorithms and documentation of the results.