Novelty-focused R&D landscaping using transformer and local outlier factor

TL;DR

This paper introduces a novel method combining transformer models and local outlier factor to evaluate the novelty of research proposals within R&D landscapes, aiding organizations in strategic planning.

Contribution

It presents a systematic approach that uses transformer-based language models and LOF to construct and analyze R&D landscapes for novelty detection in research proposals.

Findings

Effective semantic capture of proposals using transformers

Quantitative novelty scoring with LOF

Case study demonstrates practical utility

Abstract

While numerous studies have explored the field of research and development (R&D) landscaping, the preponderance of these investigations has emphasized predictive analysis based on R&D outcomes, specifically patents, and academic literature. However, the value of research proposals and novelty analysis has seldom been addressed. This study proposes a systematic approach to constructing and navigating the R&D landscape that can be utilized to guide organizations to respond in a reproducible and timely manner to the challenges presented by increasing number of research proposals. At the heart of the proposed approach is the composite use of the transformer-based language model and the local outlier factor (LOF). The semantic meaning of the research proposals is captured with our further-trained transformers, thereby constructing a comprehensive R&D landscape. Subsequently, the novelty of the newly selected research proposals within the annual landscape is quantified on a numerical scale utilizing the LOF by assessing the dissimilarity of each proposal to others preceding and within the same year. A case study examining research proposals in the energy and resource sector in South Korea is presented. The systematic process and quantitative outcomes are expected to be useful decision-support tools, providing future insights regarding R&D planning and roadmapping.