Text Mining Methods for Real Time Intelligence on Graphene Enterprise
Introduction

The project aims to develop new data sources and methods for real-time intelligence to understand and map enterprise development and commercialisation in a rapidly emerging and growing new technology. More specifically, the project focusses on new venture and small and mid-size (SME) enterprise development and commercialisation of graphene. This is a nanoscale two-dimensional material with exceptional properties holding great promise for path-breaking applications across a range of domains including electronics, medicine, batteries, and sensors. The field is expanding rapidly, with thousands of new patents and hundreds of companies already entering the graphene domain.
Project goals
The project will develop novel and scalable methods to mine and combine information from three sources:- unstructured enterprise webpages;
- unstructured data from Twitter; and
- data from established structured databases, including data on patenting.
Web pages are used to extract information on enterprise business strategies, trials, tests and new products, funding, managerial and ownership developments, and relationships with other businesses and research organisation.
Twitter feeds are accessed and sourced to provide data on fast-breaking developments related to graphene, including developments associated with start-ups and SMEs.
Databases on publications and patent applications (such as the Web of Knowledge and Derwent Innovations) are accessed to validate company names and corroborate the presence (or absence) of intellectual property applications and grants by graphene-related topic areas.
Outputs from the information extraction suite are stored in a repository at processing time, so that the information is available on the fly at demonstration time. For instance, users can retrieve graphene based products grown on specific substrates (e.g., epitaxial graphene grows on SiC), properties of graphene (e.g., conductivity, flexibility), which companies produce which products, information about companies, e.g., location, partnerships, funders, social media environments used.
Project information
The project is funded by NESTA.
Project team
Prof. Sophia Ananiadou, Mr. William Black, Mr. Jacob Carter, Dr. Ioannis Korkontzelos, Mr. Claudiu Mihăilă, Mr. Paul Thompson
Featured News
- Invited talk at BioASQ 2023
- Prof. Ananiadou appointed as Senior Area Chair for ACL 2023 and IJCNLP-AACL 2023
- New Knowledge Transfer Partnership with 10BE5
- Panellist at Digital Trust and Society Forum 2023
- Chinese Government AwardAward for PhD student Tianlin Zhang
- Advances in Data Science and AI Conference 2023
- Keynote talk at EMBL-EBI industry club Machine Learning for Text Mining
- Talk at Open Data Science Conference (ODSC)
- BioLaySumm 2023 - Shared Task @ BioNLP 2023
- Prof. Ananiadou gives talk as distinguished speaker in the Women in AI speaker series
- Junichi Tsujii awarded Order of the Sacred Treasure, Gold Rays with Neck Ribbon
Other News & Events
- Keynote Talk at the Festival of AI
- Recent funding successes for Prof. Sophia Ananiadou
- New article on using neural architectures to aggregate sequence labels from multiple annnotators
- New article on improving biomedical extractive summarisation using domain knowledge
- New article on automated detection and analysis of depression and stress in social media data