# Knowledge Graph in Astronomical Research with Large Language Models:
Quantifying Driving Forces in Interdisciplinary Scientific Discovery

Zechang Sun1111Work done during internship in Microsoft Research Asia Yuan-Sen
Ting2,3 Yaobo Liang4 Nan Duan4 Song Huang1 Zheng Cai1 1Department of
Astronomy, Tsinghua University, Beijing, China
2Research School of Astronomy and Astrophysics, Australian National
University, Canberra, Australia
3School of Computing, Australian National University, Canberra, Australia
4Microsoft Research Asia, Beijing, China
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>{shuang<EMAIL_ADDRESS>
###### Abstract

Identifying and predicting the factors that contribute to the success of
interdisciplinary research is crucial for advancing scientific discovery.
However, there is a lack of methods to quantify the integration of new ideas
and technological advancements in astronomical research and how these new
technologies drive further scientific breakthroughs. Large language models,
with their ability to extract key concepts from vast literature beyond keyword
searches, provide a new tool to quantify such processes. In this study, we
extracted concepts in astronomical research from 297,807 publications between
1993 and 2024 using large language models, resulting in a set of 24,939
concepts. These concepts were then used to form a knowledge graph, where the
link strength between any two concepts was determined by their relevance
through the citation-reference relationships. By calculating this relevance
across different time periods, we quantified the impact of numerical
simulations and machine learning on astronomical research. The knowledge graph
demonstrates two phases of development: a phase where the technology was
integrated and another where the technology was explored in scientific
discovery. The knowledge graph reveals that despite machine learning has made
much inroad in astronomy, there is currently a lack of new concept development
at the intersection of AI and Astronomy, which may be the current bottleneck
preventing machine learning from further transforming the field of astronomy.

##  1 Introduction

Interdisciplinary collaborations can drive innovation in research by
introducing new theoretical, analytical, or computational tools to specific
scientific domains. These new tools can innovate and transform the fields. For
instance, the theoretical understanding of quantum physics and general
relativity has driven much of modern cosmology Weinberg (2008), and each
subsequent engineering breakthrough leads to new windows of observation. A
prime example is the detection of gravitational waves with LIGO Abbott et al.
(2016), which was made possible by the convergence of cutting-edge
technologies in interferometry. Simultaneously, high-performance computing has
paved the way for understanding complex systems in the cosmos, such as the
evolution of galaxies McAlpine et al. (2016); Pillepich et al. (2018) and the
inner workings of stars and stellar atmospheres Gudiksen et al. (2011),
through N-body or hydrodynamical simulations.

The advancement of astronomy also relies heavily on the revolution of
statistical and analytical methods, which allow for proper inferences based on
observations. The introduction of even well-known statistical techniques to
astrophysics often leads to key turning points in the field. For example, a
cornerstone of our understanding of cosmology comes from analyzing the power
spectrum of the cosmic microwave background Hu and Dodelson (2002), while the
detection of planetary systems outside the solar system has benefited from
Gaussian Processes Hara and Ford (2023). More recently, the advent of deep
learning, with numerous successes in sciences such as AlphaFold Jumper et al.
(2021), has propelled much of the field to rethink statistical inference in
astronomy. This includes using generative models as surrogates for the
likelihood or posterior Cranmer et al. (2020); Sun et al. (2023a) and
employing flow-based generative models to capture higher-order moment
information in stochastic fields Diaz Rivero and Dvorkin (2020).

However, the underpinnings of these successful interdisciplinary results often
stem from a rigorous process of debate and adaptation within the community.
New thought processes are initially treated as disruptors, but a subset of
these promising methods subsequently becomes integrated into the field’s
knowledge base. Over time, such integration gains significant traction and
further creates branching of knowledge in the field, fostering its growth.
Consider the example of numerical simulation, which was initially viewed as a
“distraction” from pure mathematical interest in solving N-body problems and
Navier-Stokes equations Bertschinger (1998). However, astrophysics has
gradually acknowledged that some aspects of the field are non-linear and
beyond analytical understanding. The integration of numerical simulations has
subsequently led to the thriving study of galaxy evolution McAlpine et al.
(2016), a widely researched topic, and has also gradually permeated into more
specialized domains like solving the accretion physics of black holes and
protoplanetary disks Jiang et al. (2014); Bai (2016).

However, while such integration and branching off are intuitively clear,
studying and quantifying them remains a challenge. Questions such as how long
it might take for a field to adopt a new concept and the quantitative impact
it has on the field still evade rigorous study. A key bottleneck is the
difficulty in defining and extracting the various concepts described in a
paper. The classical approach of classification using only keywords or the
field Xu et al. (2018) of research lacks granularity. Other implicit methods
that aim to extract vectorized semantic representations from papers Meijer et
al. (2021) are hard to parse at the human level.

Recent advancements in large language models (LLMs), particularly generalized
pre-trained transformer techniques Brown et al. (2020); OpenAI et al. (2023),
have demonstrated exceptional zero-shot/few-shot capabilities across various
downstream tasks and have shown broad domain knowledge coverage Bubeck et al.
(2023). The synergy between LLMs and knowledge graphs constitutes an active
area of research. On the one hand, LLMs have shown robust capability for
knowledge graph construction, while on the other hand, the constructed
knowledge graph can help LLMs further enhance the accuracy of their responses
through retrieval augmented generation Pan et al. (2023); Zhu et al. (2023).

In this study, we explore the possibility of using LLMs as a bridging tool by
distilling concepts from research papers in astronomy and astrophysics and
constructing knowledge graphs to study their relationships and co-evolution
over time. To the best of our knowledge, this is the first time an LLM-based
knowledge graph has been constructed for astrophysics. The combination of the
LLM-extracted concepts with our proposed citation-reference-based relevance
allows us to quantitatively analyze cross-domain interactions over time and
the co-evolution of subfields in astronomy.

This paper is organized as follows: In Section 2, we outline the dataset used
for this study. Section 3 details the methodologies employed, including
knowledge graph construction with large language model agents and the
citation-reference-based relevance to quantify the interconnection between
different concepts. We present our findings in Section 4, including a case
study focusing on how numerical simulations were gradually adopted by the
astronomical community, and by extension, quantifying the current impact of
machine learning in astronomy. We discuss and conclude in Section 5.

##  2 Literature in Astronomical Research

This study employs a dataset of 297,807 arXiv papers in the fields of
astronomy and astrophysics, collected from 1993 to 2024 and sourced from the
NASA Astrophysics Data System (NASA/ADS) Accomazzi (2024). Astrophysics is
known to be a field where the vast majority of publications are on arXiv and
easily searchable on ADS. Therefore, the number of arXiv papers here comprises
a nearly complete collection of literature published in the field. We
downloaded all PDFs from arXiv and performed OCR with Nougat Blecher et al.
(2023). Through human inspection, we found that Nougat did a great job
transcribing the data with minimal failures. Auxiliary minor mistakes were
identified and cleaned up during those iterations.

A key component of this paper is understanding the relationship between
concepts, as viewed by the research community, through the citation
relationships within the existing literature. The fact that NASA/ADS oversees
a nearly complete literature makes astronomy one of the well-curated fields to
explore in this study. We further extract the citation-reference relationships
for the entire corpus using the NASA/ADS
API222https://ui.adsabs.harvard.edu/help/api/ to quantify the interaction
among various scientific concepts during their co-evolution.

##  3 Constructing a Knowledge Graph for Astronomy

Constructing a knowledge graph between concepts in astrophysics requires two
essential components: extracting the concepts in astronomical literature
through large language model agents, and determining the strength of
interconnectivity between concepts through the underlying relationships
between paper citations. In this section, we explore these components in more
detail.

###  3.1 Concept Extraction with Large Language Models

Figure 1: Schematic plot outlining the knowledge graph construction using
large language model agents. The extraction of concepts comprises three main
phases: (1) Concept Extraction, where agents construct scientific concepts
from documents; (2) Vectorization and Nearest Neighbor Finding, in which
concepts are vectorized and grouped by semantic similarity; (3) Concept
Merging, where similar concepts are combined to form a more coarse-grained
structures. The connections between concepts are then defined by citation-
reference relevance as detailed in Section 3.2, with concepts involved in more
citation-reference pairs assigned a higher relevance.

The key challenges in distilling concepts from publications using large
language models are twofold. Firstly, LLM agents may generate hallucinations,
producing lists of concepts that deviate from the expectations of human
experts. Secondly, even when the concepts are accurately distilled, the models
may yield concepts that are either too detailed, overly broad, or merely
synonymous with each other, thereby diminishing the practical relevance of
understanding their interrelationships. To address these challenges, we employ
a multi-agent system in this study, as shown in Figure 1. This system consists
of three parts: (a) extraction of concepts from astronomical publications; (b)
nearest neighbor search of the concepts; and (c) merging of the concepts. This
iterative approach enables control over the granularity of the knowledge
graph, tailoring it to our purpose.

In this study, we focus on extracting key concepts from the titles and
abstracts of astronomical publications to minimize computational cost. In
astronomy, the abstract often encapsulates the essential information,
including scientific motivation, methods, and data sources. The whole text
processing process, which will be detailed in Section 3, involved about 2
billion tokens with additional prompt and RAG source. To efficiently handle
this large-scale data while maintaining cost-effectiveness, we leverage open-
source large language models for concept extraction. Specifically, we employ
Mistral-7B-Instruct-v0.2333https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
Jiang et al. (2023) as our inference model and jina-embeddings-v2-base-
en444https://huggingface.co/jinaai/jina-embeddings-v2-base-en Günther et al.
(2023) for text embedding.

#### Concept Extraction:

The first agent is prompted to extract a preliminary set of scientific
concepts from the abstracts and titles. While most of these concepts appear to
be valid, some of them seem to be hallucinations that are not pertinent to
astronomy, such as “misleading result” and “maternal entity in astronomy”. To
address this issue, a secondary LLM agent is deployed to explain and clarify
each term, ensuring the removal of ambiguities and allowing only
scientifically valid concepts to proceed. In this clarifying step, we utilize
the entire document as an additional source enhanced by retrieval augmented
generation to assist our agent in accurately understanding the meanings of
various scientific terminologies. The validated scientific concepts are
denoted as $\\{c_{1},c_{2},\dots,c_{N}\\}$.

#### Vectorize and Nearest Neighbor Finding:

Once the concepts are extracted and validated, they are transformed into
vector representations using the text-embedding models, enabling the accurate
computation of similarity measures. We group the concepts based on the cosine
similarity of their corresponding vector representations into $M$ clusters,
represented as $\\{\\{c^{i}_{j},j=1,\dots,k_{i}\\},i=1,\dots,M\\}$. The number
of elements in each cluster, $k_{i}$, is adaptively determined based on a
predefined cosine similarity threshold among the elements within the cluster.
In this study, we set the threshold at 0.85, striking a balance between the
granularity of the concepts and the computational feasibility of the
subsequent steps.

#### Concept Merging:

Finally, the final agent merges these grouped concepts by analyzing clusters
of semantically similar concepts and distilling them into more general,
unified entities. For example, the concepts “X-Shooter spectra”, “DEIMOS
spectrograph”, and “Keck LRIS spectrograph” were combined into the broader
concept of “spectrograph”. This merging simplifies the structure of the
knowledge graph, reducing redundancy. Furthermore, a coarser knowledge graph
improves the readability of the visualization.

We iterate the neighbour finding and merging steps three times, gradually
coarsening the collection of concepts from 1,057,280, 164,352, and finally
24,797 concepts, respectively. We found, through domain expert evaluation
that, the granularity of the concepts after three iterations is appropriate,
with sufficient concepts covering the broad range of topics explored and
methods employed in the literature, but with enough fine-grained level to
understand the subtle evolution of the field in astrophysics. Some of the
final concepts include the commonly known concepts such as “dark matter” and
“inflation.” On average, each paper consists of $\sim$ 10 concepts.

###  3.2 Determining Concept Relevance

Upon defining the concepts, perhaps more critical is to determine,
quantitatively, how strongly two concepts are relevant. The relevancy of two
concepts is certainly subjective—concepts that were deemed irrelevant at a
certain point in time by the domain expert community might gradually become
relevant over time. However, such temporal evolution is exactly what we are
after to understand the shift of knowledge over time.

To gauge how two concepts are perceived as relevant by the community at a
fixed point in time, the citation-reference relationships between articles
become a natural annotated link between the concepts. In the following, we
will define based on the probability with which a pair of concepts appears
simultaneously in a certain article and its neighboring documents that have a
citation-reference relationship, the proximity of the two concepts. This
metric between concepts is inspired by the process by which researchers
randomly sample through the network of articles from one concept to another.
If the researcher can find another new concept from the parent concept that
they were originally interested in by searching through the direct citation
relation from the paper which contains the parent concept, and this leads the
researcher to another paper with a new concept, the two concepts are deemed
close. However, if the two concepts can only be found through a small subset
of papers of the parent concepts and their citations or references, then the
two concepts are deemed further apart at that point in time. We emphasize that
while the linkage (and here, the hypothetical “search”) is done through the
domain of the published literature, the knowledge graph is constructed at the
level of the extracted concepts.

More formally, let the final set of concepts be denoted as
$\mathrm{C}:\\{c_{1},c_{2},\dots,c_{n}\\}$, identified using large-language
model-based agents as outlined in Section 3.1. Let these concepts be
associated with a corpus of academic papers,
$\mathrm{N}:\\{n_{1},n_{2},\dots,n_{k}\\}$, and a set of citation-reference
relationships $\mathrm{L}:\\{(n_{a},n_{b})|n_{a},n_{b}\in\mathrm{N},\exists
n_{a}\rightarrow n_{b}\\}$, where $n_{a}\rightarrow n_{b}$ signifies that
paper $n_{a}$ cites paper $n_{b}$. To explore the propagation of a concept
$c_{\alpha}$ within this network, we define the probability of encountering
another concept $c_{\beta}$ starting from a specific paper $n_{k}$ that
discusses $c_{\alpha}$. This probability, denoted as
$p_{\alpha\rightarrow\beta|n_{k}}$, is formulated as:

$p_{\alpha\rightarrow\beta|n_{k}}=\frac{\mathrm{N}_{\beta}}{|S(n_{k},\mathrm{L},\beta)|}.$
(1)

The set $S(n_{k},\mathrm{L},\beta)$ is defined through an iterative process
starting with the initial paper set $n_{k}$ (denoted as ${S_{0}}$). In each
iteration, we expand the set by including papers that are directly cited by
any paper in the current set and have not been included in previous sets.
Formally, if $S_{n-1}$ is the set of papers at iteration $n-1$, then
$S_{n}=S_{n-1}\cup\\{n_{e}|(n_{s},n_{e})\in\mathrm{L},n_{s}\in
S_{n-1},n_{e}\notin S_{n-1}\\}$. The iteration continues until at least one
paper in the current set contains concept $c_{\beta}$, at which point we
denote the final set as $S_{T}$ and set $S_{T}=S(n_{k},\mathrm{L},\beta)$. The
number of papers containing $c_{\beta}$ within $S(n_{k},\mathrm{L},\beta)$ is
set to be $\mathrm{N}_{\beta}$.

Typically, the growth of the sets follows a pattern where $|S_{0}|=1$,
$|S_{1}|\sim 10^{2}$, and $|S_{2}|\sim 10^{4}$ in our experiments. This means
that if the concepts cannot be found directly from a direct citation from the
original paper that contains the parent concept, the number of papers “needed
to be read”, i.e., $|S|$, will drastically reduce the relevance of the two
concepts. Nonetheless, if the concepts are very prevalent, after a certain
level of search, the numerator $\mathrm{N}_{\beta}$ would then offset the
volume of search.

As this probability pertains to just a specific paper containing concept
$c_{\alpha}$, the probability of transitioning from concept $c_{\alpha}$ to
$c_{\beta}$, for all the papers $S_{\alpha}$ that contain $c_{\alpha}$, would
then be the expectation averaging over all papers in $S_{\alpha}$, or,

$p_{\alpha\rightarrow\beta}=\frac{1}{|S_{\alpha}|}\sum_{n_{k}\in
S_{\alpha}}p_{\alpha\rightarrow\beta|n_{k}}$ (2)

The above equation computes the average probability of moving from
$c_{\alpha}$ to $c_{\beta}$ across all papers that contain $c_{\alpha}$. To
assess the bidirectional relevance of concepts $c_{\alpha}$ and $c_{\beta}$,
and we will assume that the order of transition between two concepts is not
relevant, we define the citation-reference relevance between them as the
geometric average of the probabilities of transitioning in both directions:

$p_{\alpha,\beta}=\left(p_{\alpha\rightarrow\beta}\cdot
p_{\beta\rightarrow\alpha}\right)^{1/2}$ (3)

Finally, the transition probability attains the following trivial properties:
(1) $p_{\alpha,\beta}\leq 1,\forall c_{\alpha},c_{\beta}\in\mathrm{C}$; (2)
$p_{\alpha,\alpha}\equiv 1,\forall c_{\alpha}\in\mathrm{C}$; and (3)
$p_{\alpha,\beta}=p_{\beta,\alpha},\forall c_{\alpha},c_{\beta}\in\mathrm{C}$.
These properties ensure that the relevance metric is well-defined and
consistent, providing a foundation for analyzing the relationships between
concepts in the knowledge graph.

###  3.3 From Concept Relevance to Knowledge Graph

From the relevance defined as $p_{\alpha,\beta}$ above, which serves as a
robust metric for the link strength between two nodes, we can visualize the
knowledge as a force-directed graph. A force-directed graph Kobourov (2012);
Bannister et al. (2012), also known as a spring-embedder or force-based
layout, is a visual tool designed to illustrate relational data within network
graphs. This method leverages simulation techniques inspired by physical
systems, arranging nodes—which symbolize entities or concepts—and links—which
depict the relationships or connections between these nodes—in a coherent and
insightful layout. These graphs utilize the concept of attraction and
repulsion forces to strategically distribute nodes.

Figure 2: Visualization of a knowledge graph of 24,939 concepts, constructed
from 297,807 astronomical research papers. Only concepts appearing in more
than 20 papers and links with a link strength greater than 0.001 are
displayed. Each concept is categorized into one of the following domains: (A)
Galaxy Physics, (B) Cosmology & Nongalactic Physics, (C) Earth & Planetary
Science, (D) High Energy Astrophysics, (E) Solar & Stellar Physics, (F)
Statistics & AI, (G) Numerical Simulation, or (H) Instrumental Design. In the
upper panels, we show connections between galaxy physics and other scientific
domains. In the lower panel, we highlight the concepts from simulation,
statistics, and observational instruments and their respective locations with
respect to galaxy physics. Unsurprisingly, the technological concepts are
generally more globally spread, as the same techniques can have wide
implications for a broad range of topics in astronomy. Machine learning
techniques are still at the periphery of the knowledge graph, suggesting that
their integration in astronomy is still in its early stages. The interactive
version of the knowledge graph is made publicly available at
https://astrokg.github.io/.

By iteratively updating the positions of nodes based on these attraction and
repulsion forces, the force-directed graph algorithm converges to a layout
that minimizes the overall energy of the system. This results in an
informative 3D representation of the knowledge graph, where closely related
concepts are automatically positioned near each other, enhancing the
visibility of the density and connectivity within the graph. The capacity of
force-directed graphs to dynamically represent complex relational data makes
them particularly suitable for visualizing knowledge graphs.

In our context, the link strength between two nodes (concepts) is set to their
citation-reference relevance, $p_{\alpha,\beta}$. Concepts with higher
relevance will attract each other more strongly Cheong et al. (2021), causing
them to be positioned closer together in the visualized graph. Conversely, the
repulsion force is applied between all pairs of nodes, ensuring that they
remain adequately spaced to prevent overlap and maintain clear visual
separation. By leveraging the citation-reference relevance as the link
strength between concepts, we can create a graph that intuitively conveys the
relationships and clustering of ideas within the astronomical literature.

##  4 Intersection between Technological Advancement and Scientific Discovery

Our knowledge graph consists of 24,939 concepts, extracted from 297,807
astronomical research papers, with 339,983,272 interconnections. The
visualization of the knowledge graph as a force-directed graph is shown in
Figure 2. The filamentous structure shown in the knowledge graph demonstrates
the close interconnections across various subdomains within astronomical
research. For clarity, we only display concepts that appear in at least 20
papers and consider only those links with a citation-reference relevance
$p_{\alpha,\beta}>0.001$. This leads to 9,367 nodes and 32,494 links for the
visualization. We set the size of the nodes to be proportional to the
logarithm of their frequency of occurrence in the papers.

In the visualization, we further categorize all the concepts into scientific
concepts, following the categorization of astrophysics on
arXiv555https://arxiv.org/archive/astro-ph, namely Astrophysics of
Galaxies,666Astrophysics of Galaxies focuses on phenomena related to galaxies
and the Milky Way, including star clusters, interstellar medium, galactic
structure, formation, dynamics, and active galactic nuclei. Cosmology and
Nongalactic Astrophysics,777Cosmology and Nongalactic Astrophysics covers the
early universe’s phenomenology, cosmic microwave background, dark matter,
cosmic strings, and the large-scale structure of the universe. Earth and
Planetary Astrophysics,888Earth and Planetary Astrophysics studies deal with
the interplanetary medium, planetary physics, extrasolar planets, and the
formation of the solar system. High Energy Astrophysics,999High Energy
Astrophysics explores cosmic ray production, gamma ray astronomy, supernovae,
neutron stars, and black holes. and Solar and Stellar Astrophysics,101010Solar
and Stellar Astrophysics pertains to the investigation of white dwarfs, star
formation, stellar evolution, and helioseismology.. As we aim to understand
how concepts in technological advancement propel scientific discoveries, we
further define another three classes of “technological” domains, which we
identify as Statistics and Machine Learning, Numerical Simulation, and
Instrumental Design. The classifications below are conducted using
GPT-4111111https://openai.com/index/gpt-4/.

Figure 2 illustrates how relevant concepts cluster within the same domain and
how different domains interconnect. The upper panels demonstrate how the
different scientific clusters interact with each other. For instance, galaxy
physics, as anticipated, connects with both the largest scales in astronomical
research, such as cosmology and general relativity, and the smaller scales,
including stellar physics and planetary physics. The lower panel shows how the
technological concepts are embedded within the scientific concepts, including
numerical simulations, statistics, machine learning, and instrumental design.
The technological concepts are generally distributed more globally in the
knowledge graph, demonstrating their omnipresence in different subfields.

Interestingly, as shown in the figure, despite the booming interest and
popularity, machine learning techniques, particularly deep learning, are
situated only at the peripheral region of the knowledge graph. This suggests
that machine learning techniques are not yet fully integrated into the
astronomical research community, at least from the citation-reference point of
view. We will provide a more quantitative comparison of this observation in
the following section.

Figure 3: The average linkage for five distinct time periods is used to
investigate the temporal integration of technological techniques into
scientific research. The middle and lower panels illustrate a consistent
increase in the count of concepts, both in terms of scientific concepts
(bottom panel) and technical concepts (middle panel). The upper panel shows
the total cross-linkage between individual technical domains and scientific
concepts, with higher values indicating stronger adoption. The upper panel
reveals a two-phase evolution, with an observed latency of approximately five
years. The two phases signify the period of development and introduction of
new techniques in astronomy and their subsequent adoption by the community
(see text for details). Machine learning has begun to reach integration levels
comparable to those of numerical simulations seen two decades earlier.
However, the number of concepts in machine learning within astronomical
research has only increased rather marginally, rising from 152 between 1993
and 2000, to 215 from 2005 to 2010, and reaching 230 between 2015 and 2020.

###  4.1 Numerical Simulations in Astronomy

To demonstrate how technological advancement drives scientific discovery, we
will study the impact of numerical simulations on astronomy in more depth. In
modern-day astronomical research, numerical simulation has become an
indispensable tool. However, this was not always the case. The scientific
community experienced a gradual transition from focusing primarily on
theoretical deduction and analytical formulas to modeling complex phenomena
through numerical simulations. To understand this transition, we assess the
average relevance between numerical simulations and scientific concepts across
various time periods. We divided the dataset into five time periods from 1993
to 2020. In each time period, we recalculate the citation-reference relevance
using the papers published within that specific timeframe.

As shown in the bottom panel of Figure 3, unsurprisingly, the number of
“scientific concepts” has surged over time. Complementary to these scientific
concepts, we also see that the number of technical concepts has surged
alongside, especially in terms of numerical simulations and statistical
methods, which are shown as red and blue lines in the middle panel. On the
other hand, despite the interest in the field, the number of concepts in
machine learning in the astronomical literature, as shown by the green line,
is still lagging behind these other well-developed technological concepts by
an order of magnitude.

Perhaps more interesting is showing the weighted “intersection” between the
scientific concepts and the technical concepts, which is shown in the top
panels. The top panel shows the weighted “linkage” among all the scientific
concepts with the specific technical domain. We define the average linkage as
follows:

$\mathrm{Average\,\,Linkage}=\frac{1}{|\mathrm{A}|\cdot|\mathrm{B}|}\sum_{\alpha\in\mathrm{A},\beta\in\mathrm{B}}p_{\alpha,\beta}$
(4)

where $\mathrm{A}$ and $\mathrm{B}$ represent the sets of concepts related to
specific subfields, such as machine learning and numerical simulation in our
study. Here, $p_{\alpha,\beta}$ denotes the citation-reference relevance as
defined in Equation 3. If the new methods are well-adopted in the astronomical
community and advance scientific discovery, we should see an improvement in
the average citation-reference linkage (large values in the top panel). Viewed
this way, there is a clear two-phase evolution with the gradient of the
integration oscillating positively (blue arrow) and negatively (red arrow).

This is perhaps not surprising. For any technological advancement, it might
once be proposed with many technically focused papers written; however, the
citation-reference relation is mostly limited to the “technologists,” leading
to a dilution of the cross-correlation, which is shown by the red arrow. For
example, during the period of 1993-2000, there have been many works focusing
on the development of N-body simulation techniques Bode et al. (2000); Romeo
et al. (2004); Springel (2005). Yet, the integration remains marginal.
However, from 2000 onward, the astronomical community began to embrace N-body
simulations to resolve scientific questions Paz et al. (2006); Peñarrubia et
al. (2006); Zhou and Lin (2007), resulting in an increase in citation-
reference relevance during this time. A similar two-phase pattern is observed
from [2010, 2015) to [2015, 2020), during which time hydrodynamical
simulations developed Genel et al. (2014); Carlesi et al. (2014b, a) and
gradually gained acceptance McAlpine et al. (2016); Pillepich et al. (2018)
within the community. The delay between the development of new technologies
and their impact on scientific discovery spans approximately five years.

###  4.2 Machine Learning in Astrophysics

The revelation of the two-phase adoption in numerical simulations leads to the
possibility of better quantifying the integration of machine learning in
astronomy. In recent years, we have seen a booming interest in AI and its
applications in science. As modern-day astronomy is driven by big data, with
billions of sources routinely being surveyed, it is not surprising that
astronomy has also seen a drastic integration of AI to advance data processing
and analysis Baron (2019).

Figure 4 shows the average cross-domain linkage, as defined in Equation 3, but
between the concepts in machine learning and the five scientific domains. In
terms of the application of machine learning in astronomy, Cosmology &
Nongalactic Astrophysics takes the lead, as it benefits from machine
learning’s capacity to manage complex, large data sets from simulations and
surveys Villaescusa-Navarro et al. (2021b, a); Sun et al. (2023b). This is
followed by Galaxy Physics, which leverages ML for tasks like photometric
redshift prediction Sun et al. (2023a) and galactic morphology classification
Robertson et al. (2023). Solar and Stellar Physics have also shown promise in
emulating and analyzing stellar spectra Ting et al. (2019). High Energy
Astrophysics and Earth & Planetary Astrophysics have been slower to adopt ML.

But is machine learning now well-adopted in astronomical research? Figures 2
and 3 paint an interesting picture. On the one hand, the top panel of Figure 3
shows that there has been a rapid increase in the cross-science-and-AI
citation-reference linkage, demonstrating a huge interest among the community.
For instance, the scientific-technology score remains flat and low before
2015, signifying that despite a history of AI in astronomy such as the use of
neural networks for galaxy morphology classification can trace back to as
early as 1990s Storrie-Lombardi et al. (1992)—its impact remained minimal
until the surge in popularity of deep learning post-2015.

Yet, at the same time, even currently, Figure 2 shows that most of these
concepts still occupy a peripheral position in the knowledge graph. This
suggests that, from a citation-reference relevance perspective, such concepts
are still considered niche within the broader scientific community. This is
perhaps not too surprising because, compared to the deep integration of
numerical simulations, quantitatively, the cross-linkage score of machine
learning with astronomy remains only at the level that numerical simulations
and classical statistics were twenty years ago.

Perhaps what is strikingly lacking is that the number of machine learning
concepts $(\sim 300)$ in the astronomical literature remains an order of
magnitude smaller than that of numerical simulations ($\sim 2,000$), as shown
in the middle panel of Figure 3. This might imply that the machine learning
techniques widely adopted in astronomy, even at present, remain some of the
more classical techniques, such as linear regression and random forests. The
rapid adoption of “existing” techniques, while encouraging, might also signify
a bigger underlying problem of lack of innovation in applying AI to astronomy.
However, if the two-phase evolution applies, we should expect that in the
coming years, there will be more novel deep learning techniques introduced
before they are gradually adopted by the community.

Figure 4: Integration of machine learning in different subfields of astronomy.
The integration is defined as the average cross-domain linkage similar to the
top panel of Figure 3. Cosmology and Nongalactic Astrophysics currently lead
the application of machine learning in astronomy, followed by Galaxy Physics
and Solar & Stellar Physics. The adoption of machine learning concepts in
Earth & Planetary Physics and High Energy Astrophysics still lags behind.

##  5 Discussions and Conclusions

A quantitative study of the evolution of concepts and their interconnections
would not be possible without modern-day LLMs, partly due to the large amount
of arduous work required to manually label, extract concepts, and classify
topics, which can be easily done with minimal computing resources in our case.
Even when manual extraction is possible, the taxonomy of a scientific field is
often limited—tailored to provide vague contours of the domain, e.g., for
publication purposes, rather than a deep and more fine-grained differentiation
of the knowledge embedded in the field.

In this study, we construct, to the best of our knowledge, the first large-
language-model-based knowledge graph in the domain of astronomy and
astrophysics. The knowledge graph comprises 24,939 concepts extracted through
a careful iterative process with LLMs from 297,807 papers. We design a
relevance metric defined through the citation-reference relations in the
astronomical literature to understand the relations as well as the temporal
evolution between different concepts. The relevance metric follows the
intuition of how humans search for new concepts by quantifying the degree of
separation in the citation network as well as the prevalence of the concepts
in the field. The relevance is then applied as the linkage strength of the
force-directed graph to construct the knowledge graph, allowing us to
visualize the knowledge in the field in detail.

Based on this knowledge graph, we evaluate the temporal evolution of the
relevance of numerical simulations and machine learning in astronomical
research. We showed that while numerical simulations are routinely adopted in
modern-day astronomy, the concepts related to them have gone through a long
process of gradually being integrated into and accepted by the community. We
also found that the integration of numerical simulation into scientific
discovery shows a two-phase process, in which a five-year latency can be
observed between the development of techniques, where the relevance of the
techniques and the science might temporarily diminish, followed by the
flourishing period, where the methods mature and are widely applied to
astronomical research. We also found that the same trend can be found in
classical statistical analysis.

By the same metric, we found that, despite much of the interest and the
booming field of machine learning, the impact of machine learning in astronomy
remains marginal. While there is a drastic increase in the technique-science
cross-referencing, quantitatively, the referencing remains at a level that we
observed for numerical simulations about two decades ago. Furthermore, the
number of machine learning concepts introduced in astronomy remains an order
of magnitude smaller than that of numerical simulations and classical
statistical methods, which might imply that the current rapid increase in
relevance is driven mainly by the adoption of established machine learning
techniques from decades ago. Nonetheless, if the two-phase transition applies,
we expect more innovative techniques will be gradually introduced. In fact, in
recent years, we have seen many more modern-day techniques, both in terms of
flow-based and score-based generative models De Santi et al. (2024); Zhao et
al. (2023), being introduced, as well as, like this study, the application of
LLMs in astronomical research Dung Nguyen et al. (2023); Perkowski et al.
(2024). The metric introduced here will be able to continue monitoring this
process.

This study primarily aims to show a proof of concept, using LLM-based
Knowledge Graph to quantifiably understand the evolution of astronomical
research. As such our study certainly has much room for improvement. For
instance, proper robust extraction of scientific concepts from literature
heavily relies on the alignment between the agents and the researchers’
perception. In our study, the concepts are autonomously extracted through the
LLM agent, with the granularity of the concepts optimized through merging and
pruning. Such an LLM agent can certainly benefit from a subset of high-quality
annotated data and comparison with existing hierarchical taxonomies. The
process of concept pruning and merging is also somewhat crude, involving
vectorizing the concepts and performing a cosine similarity search. A better
method would involve further comparing these concepts, utilizing the
capabilities of large language models for more detailed concept
differentiation and pruning.

In a nutshell, our study demonstrates the potential of LLM-based knowledge
graphs in uncovering the intricate relationships and evolution of astronomical
research. By providing a quantitative framework for analyzing the integration
of new technologies and methodologies, this approach opens up new avenues for
understanding the dynamics of interdisciplinary research and the factors that
drive scientific progress, in astronomy and beyond.

## Ethical Statement

In this study, we construct a knowledge graph by extracting concepts from the
astronomical literature available on the arXiv preprint server. Our work aims
to advance the understanding of the evolution and interconnections of
scientific concepts within the field of astronomy. We emphasize that our study
does not involve the direct reproduction or distribution of the original
literature itself. Instead, we focus on distilling and analyzing the key
concepts present in the existing body of work.

To ensure ethical compliance and respect for intellectual property rights, we
will only release the extracted concepts and their relationships, without
sharing or reproducing the original text or any substantial portions of the
literature. This approach minimizes the risk of copyright infringement and
maintains the integrity of the original authors’ works.

Furthermore, the field of astronomical research generally operates under an
open-sky policy, which promotes collaboration, transparency, and the free
exchange of scientific knowledge. This policy aligns with our research
objectives and mitigates potential ethical or monetary disputes arising from
our work. Our goal is to provide insights that benefit the astronomical
community and contribute to the advancement of scientific understanding.

## Acknowledgments

The authors acknowledge the initial discussions with Kangning Diao and Jing
Tao from the Department of Astronomy in Tsinghua University. The authors are
grateful to Dr. Peng Cheng of the School of Social Science at Tsinghua
University for his expert advice on the philosophy of science. This research
has made use of NASA’s Astrophysics Data System Bibliographic Services. Y.S.T.
acknowledges financial support received from the Australian Research Council
via the DECRA Fellowship, grant number DE220101520 and support received from
Microsoft’s Accelerating Foundation Models Research (AFMR). S.H. is supported
by the National Science Foundation of China (grant no. 12273015).

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