Abstract

Mapping the technology landscape is crucial for market actors to take informed investment decisions. However,
given the large amount of data on the Web and its subsequent information overload, manually retrieving
information is a seemingly ineffective and incomplete approach. In this work, we propose an end-to-end
recommendation based retrieval approach to support automatic retrieval of technologies and their associated
companies from raw Web data. This is a two-task setup involving (i) technology classification of entities
extracted from company corpus, and (ii) technology and company retrieval based on classified technologies.
Our proposed framework approaches the first task by leveraging DistilBERT which is a state-of-the-art language
model. For the retrieval task, we introduce a recommendation-based retrieval technique to simultaneously
support retrieving related companies, technologies related to a specific company and companies relevant to a
technology. To evaluate these tasks, we also construct a data set that includes company documents and entities
extracted from these documents together with company categories and technology labels. Experiments show
that our approach is able to return 4 times more relevant companies while outperforming traditional retrieval
baseline in retrieving technologies.

Research Paper

article

Source: World Patent Information

BibTex

@article{duong2023scattered,
  title={From scattered sources to comprehensive technology landscape: A recommendation-based retrieval approach},
  author={Duong, Chi Thang and David, Dimitri Perica and Dolamic, Ljiljana and Mermoud, Alain and Lenders, Vincent and Aberer, Karl},
  journal={World Patent Information},
  volume={73},
  pages={102198},
  year={2023},
  publisher={Elsevier}
}