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Mapping the technology landscape is crucial for market actors to takeinformed investment decisions. However, given the large amount of data on theWeb and its subsequent information overload, manually retrieving information isa seemingly ineffective and incomplete approach. In this work, we propose anend-to-end recommendation based retrieval approach to support automaticretrieval of technologies and their associated companies from raw Web data.This is a two-task setup involving (i) technology classification of entitiesextracted from company corpus, and (ii) technology and company retrieval basedon classified technologies. Our proposed framework approaches the first task byleveraging DistilBERT which is a state-of-the-art language model. For theretrieval task, we introduce a recommendation-based retrieval technique tosimultaneously support retrieving related companies, technologies related to aspecific company and companies relevant to a technology. To evaluate thesetasks, we also construct a data set that includes company documents andentities extracted from these documents together with company categories andtechnology labels. Experiments show that our approach is able to return 4 timesmore relevant companies while outperforming traditional retrieval baseline inretrieving technologies.

From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach