  How does scientific knowledge grow? This question has occupied a central
place in the philosophy of science, stimulating heated debates, but yielding no
clear consensus. Many explanations can be understood in terms of whether and
how they view the expansion of knowledge as proceeding through the accretion of
scientific concepts into larger conceptual structures. Here, we examine these
views empirically, performing a large-scale analysis of the physical and social
sciences, spanning five decades. Using natural language processing techniques,
we create semantic networks of concepts, wherein noun phrases become linked
when used in the same paper abstract. For both the physical and social
sciences, we observe increasingly rigid conceptual cores (i.e., densely
connected sets of highly central nodes) accompanied by the proliferation of
periphery concepts (i.e., sparsely connected nodes that are highly connected to
the core). Subsequently, we examine the relationship between conceptual
structure and the growth of scientific knowledge, finding that scientific works
are more innovative in fields with cores that have higher conceptual churn and
with larger cores. Furthermore, scientific consensus is associated with reduced
conceptual churn and fewer conceptual cores. Overall, our findings suggest that
while the organization of scientific concepts is important for the growth of
knowledge, the mechanisms vary across time.
