The evolution of scientific literature as metastable knowledge states

TL;DR

This paper models scientific knowledge as metastable states and uses natural language clustering plus citation analysis to predict how ideas evolve, linking articles to past and future concepts beyond traditional citations.

Contribution

It introduces a novel approach combining language clustering and citation analysis to predict the evolution of scientific ideas over time.

Findings

Successful prediction of idea evolution over time

Linking articles to past and future concepts beyond citations

Modeling knowledge as metastable states

Abstract

The problem of identifying common concepts in the sciences and deciding when new ideas have emerged is an open one. Metascience researchers have sought to formalize principles underlying stages in the life-cycle of scientific research, determine how knowledge is transferred between scientists and stakeholders, and understand how new ideas are generated and take hold. Here, we model the state of scientific knowledge immediately preceding new directions of research as a metastable state and the creation of new concepts as combinatorial innovation. We find that, through the combined use of natural language clustering and citation graph analysis, we can predict the evolution of ideas over time and thus connect a single scientific article to past and future concepts in a way that goes beyond traditional citation and reference connections.