Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
Year: 2023

Abstract:
Researchers usually come up with new ideas only after thoroughly comprehending vast quantities of literature. The difficulty of this procedure is exacerbated by the fact that the number of academic publications is growing exponentially. In this study, we devise a framework based on concept co-occurrence for academic idea inspiration, which has been integrated into a research assistant system. From our perspective, the fusion of two concepts that co-occur in an academic paper can be regarded as an important way of the emergence of a new idea. We construct evolving concept graphs according to the co-occurrence relationship of concepts from 20 disciplines or topics. Then we design a temporal link prediction method based on masked language model to explore potential connections between different concepts. To verbalize the newly discovered connections, we also utilize the pretrained language model to generate a description of an idea based on a new data structure called co-occurrence citation quintuple. We evaluate our proposed system using both automatic metrics and human assessment. The results demonstrate that our system has broad prospects and can assist researchers in expediting the process of discovering new ideas.1

heading: 1 Introduction
text: Academic publications have witnessed the evolution and advancement of human civilization. In modern society, out-of-box and interdisciplinary scientific work can get more attention from science funders, industry, and the public (Thurner et al., 2020), where a good idea is the cornerstone of academic research. However, for most researchers, it takes a lot of time to put forward new ideas. For one thing, the number of academic publications is increasing exponentially, and it is difficult for an
∗ Luoyi Fu is the corresponding author. 1The project is publicly available for research purpose
https://github.com/xyjigsaw/Kiscovery.
independent researcher to understand these papers thoroughly. Besides, researchers often focus on their specialized but narrow fields, which makes it a challenge to discover underlying connections beyond their familiar areas (Lahav et al., 2022; Krenn and Zeilinger, 2020). In this work, our purpose is to unveil the profound connections between different academic concepts and ignite researchers’ exploration of potential academic ideas while expediting the research process. The two primary goals are idea exploration and verbalization.
For the first goal, we need to understand how new ideas originate. Generally speaking, the emergence of a simple idea is often formed by the interaction between two different concepts rather than from scratch. For example, the combination of convolution and graph neural network contributes to graph convolutional network (Kipf and Welling, 2017). This understanding of idea as connection and combination inspires us to model the process of idea exploration as a link prediction task based on the evolving co-occurrence graph of concepts. Such graphs are constructed according to the cooccurrence relationship of concepts in the papers published in different years. It should be highlighted that there exist numerous factors leading to new ideas in the real world. We provide a possible way as a preliminary exploration.
The second goal, idea verbalization, is carried out after idea exploration to generate fluent and reasonable texts describing an idea, which usually comprises new contents derived from the combination of two different concepts. We retrieve sentences pertaining to concepts from existing publications and then verbalize ideas using the technique of natural language generation. Specifically, We propose a new data structure called co-occurrence citation quintuple (Figure 1), which stores two concepts, their corresponding sentences of papers, and idea texts. The definition is given in section 3.1. The quintuple is an extension of edges
13001
in the evolving concept co-occurrence graph and indicates where an idea comes from. We use such quintuples to train a sequence-to-sequence text generation model.
In our application scenario, there are various types of disciplines. Each of them has distinct characteristics and concepts. Existing methods of link prediction and text generation (Yao et al., 2019; Wang et al., 2019; Krenn and Zeilinger, 2020; Pareja et al., 2020; Da Li et al., 2022) are mostly trained on one dataset by optimizing a set of parameters. Owing to the fact that different datasets require specific training configurations and hyperparameters, such models cannot be transferred to other datasets. Particularly, link prediction models need to set the scale of graphs before training, such as the number of nodes. Moreover, in the field of natural language generation, some works (Wang et al., 2019; Yu et al., 2022) tend to construct domain knowledge bases as external information to generate texts. However, building large knowledge bases for each discipline takes tremendous resources, which is unrealistic. To this end, it is preferable to design general and informative models which can be applied to numerous disciplines.
Thanks to the abundant training corpus of pretrained language models (PLMs) such as BERT (Devlin et al., 2018), T5 (Raffel et al., 2020), BART (Lewis et al., 2020), and GPT (Radford et al., 2018), PLM can be regarded as an implicit knowledge graph (Petroni et al., 2019; Wang et al., 2020), which has the ability of extrapolation. In this work, we integrate the whole academic information into the same representation space by leveraging the capability of PLM to break through disciplinary barriers. For idea exploration, we devise a PLM-based link prediction method, which only needs to train one set of model parameters. For idea verbalization, we use another sequence-to-sequence-based PLM endowed with academic knowledge from millions of highly-cited papers via unsupervised denoising training. Subsequently, we re-train the denoised PLM with co-occurrence citation quintuples in a supervised way. Our contributions are summarized as follows:
• New insights: we transform the idea generation into two sequential sub-tasks: temporal link prediction and idea verbalization. The former aims to model and predict potential concept connections, while the latter involves expressing these new connections in natural
language.
• Publicly-released datasets: we construct 240 evolving concept co-occurrence graphs with 20 high-level disciplines and topics. Each of them includes 23 annual snapshots ranging from 2000 to 2022. For idea verbalization, we propose a new data structure known as the co-occurrence citation quintuple that reveals how ideas appear. We curate nearly 10K high-quality co-occurrence citation quintuples, which originate from 29M papers with high citations.
• General system for all disciplines: we design a novel temporal link prediction method and train an idea verbalization model with a large number of academic papers. The two modules are integrated into a system to serve researchers from different fields. Note that the system updates the latest papers to encourage new ideas sustainably. Users are free to enter any academic query.
• Systematic experiments: we conduct extensive experiments, including automatic metrics and human assessment, to evaluate the performance of our link prediction method and idea verbalization model. The results show that our system has a promising prospect of helping researchers discover new ideas.

heading: 2 Preliminaries

heading: 2.1 Evolving Concept Co-occurrence Graph
text: Given a concept set C = {ci}Ni=1 consisting of N concepts and a paper corpus P = {pj}Mj=1 consisting of M papers, let Cp ⊂ C denote the set of concepts paper p ∈ P contains. When concepts cu and cv (cu ̸= cv) occur together in the same paper p at the same time, i.e., cu ∈ Cp, cv ∈ Cp, it is considered that cu and cv co-occur, that is, there is a connection between the two concepts. Let A ∈ RN×N represent the co-occurrence matrix of any two concepts, which is defined as follows:
A(cu, cv) = { 1, ∃p, cu ∈ Cp, cv ∈ Cp 0, otherwise (1)
A concept co-occurrence graph is a pair G = (C,E), where C is a set of concepts, and E is a set of edges representing the co-occurrence relationship between concepts. The co-occurrence matrix
A is the adjacent matrix of G. Let G = {Gt}Tet=Ts denote a set of concept co-occurrence graphs at different times ranging from Ts to Te, At represent the adjacent matrix of Gt. We call G evolving concept co-occurrence graph. Similar to citation network, G is a strictly evolving network (Skarding et al., 2021) where the connection of concepts has infinite duration. This implies that the edges in G never disappear. Exploring ideas aims to predict future co-occurrence relations in G.

heading: 2.2 Co-occurrence Citation Quintuple
text: Assuming that paper p contains concept cu and cv, p cites paper pi and pj (pi ̸= pj). Meanwhile, pi contains concept cu, and pj contains concept cv. Then, for papers pi, pj , and p, there exist cooccurrence citation relations corresponding to concepts cu and cv. Formally, let Rp denote the set of reference papers of p, and we define the set Q of co-occurrence citation quintuples as:
Q = {(pi, pj , cu, cv, p)|pi ∈ Rp, pj ∈ Rp, cu ∈ Cpi ∩ Cp, cv ∈ Cpj ∩ Cp, cu ̸= cv}, (2)
where p is called target paper, pi and pj are called reference papers. In practice, we bind sentences that mention related concepts to the quintuples, illustrating how an idea existing in p comes up. Figure 1 shows an example of such quintuple, which consists of two concepts text summarization and contrastive learning. In the training process, we use the corresponding texts of pi, pj , cu, and cv as input, and our model is expected to generate the idea sentence in p, which usually appears in the paper abstract or introduction section.

heading: 3 Datasets and Technical Details

heading: 3.1 Datasets
text: Our work relies on a daily updated database containing more than 220 million academic papers from 19 disciplines published between 1800 and 2023. The database also stores nearly 800K concept entities with descriptions. See Appendix A for the number of papers in each discipline.
To train our model for temporal link prediction, we first collect 240 essential and common queries from 19 disciplines and one special topic (COVID19). Then, we enter these queries into the paper database to fetch the most relevant papers between 2000 and 2021 with Elasticsearch, a modern text retrieval engine that stores and retrieves papers. Afterward, we use information extraction tools including AutoPhrase (Shang et al., 2018) to identify concepts. Only high-quality concepts that appear in our database will be preserved. Finally, we construct 240 evolving concept co-occurrence graphs, each containing 22 snapshots according to the co-occurrence relationship. The statistics of the concept co-occurrence graphs are provided in Appendix I.
Besides, we construct and release a dataset of co-occurrence citation quintuples, which is used to train text generation model for idea verbalization. We select nearly 9.5M highly-cited papers (500K per discipline) and their corresponding references (19.7M) to construct quintuples. The process of identifying and processing concepts is similar to constructing the concept co-occurrence graph. Heuristic rules are adopted to filter redundant and noisy sentences, further improving the quality of the quintuples used for idea generation. The statistics and more details of co-occurrence citation quintuples can be found in Appendix B, C, and J.

heading: 3.2 Framework Overview
text: The framework of our system in the production environment is illustrated in Figure 2. It starts by receiving the user’s query and retrieving the most relevant papers from database to construct an evolving concept co-occurrence graph in a real-time way. Meanwhile, the system maintains two dictionaries for storing the mapping relations between papers and concepts. Then, a BERT-based temporal model predicts potential connections of concepts such as cu and cv, which can be regarded as a new idea. Finally, these connected concepts, as well as their corresponding sentences of papers stored in the
above dictionary, are fed to our pretrained model T5 to verbalize an idea. Our system also allows users to select elements they are interested in to form a group of inputs (pi, pj , cu, cv) for idea verbalization. In the following parts, we will introduce two key components in detail.

heading: 3.3 Temporal Link Prediction
text: Our system dynamically constructs a unique evolving concept co-occurrence graph for each query according to the papers retrieved by the search engine. Under the circumstance, a general link prediction model with high transferability is required to predict new connections on different graphs, which means there exists only one set of model parameters. We take advantage of the masked language model (MLM) to tackle the link prediction problem on different graphs and propose a new temporal training method called PLM-LP (See Appendix D for the illustration of PLM-LP).
Given a concept pair cu, cv and a timestamp t, we concatenate these elements and prompt words pro(cu, cv, t) to obtain the following input sequence xtuv:
xtuv = [CLS] pro(cu, cv, t): in t, cu is [MASK] to cv .[SEP],
where pro is a prompt function defined in Equation 3 that generates a description of the given input, [MASK] is the mask token, [CLS] and [SEP] represent the tokens of the beginning and end of the input sequence, respectively. Our model is expected to fill the mask token with a relation token, i.e., “related” and “unrelated”, which are taken as the true label to indicate whether the two con-
cepts are connected. Considering that edges in the evolving concept co-occurrence graph do not disappear, we add prompts according to this feature. If there was an edge between cu and cv before time t, the pro(·) returns the word “Existing”, otherwise it returns “Unknown”:
pro(cu, cv, t) = { “Existing”,At−1(cu, cv) = 1 “Unknown”, otherwise
(3) In the data preprocessing, positive samples D+ = {xtuv|At(cu, cv) = 1, Ts ≤ t ≤ Te} are directly constructed according to the edges of each year. For negative samples D−, since the concept co-occurrence graph is sparse, we cannot simply take any two concepts that do not have a connection each year as negative samples, which is unreasonable and will lead to a sharp increase in the number of negative samples. Actually, we only need to focus on the samples in the most difficult cases. Therefore, given a concept cu ∈ C and its k-hop neighborhood concepts, we choose concepts that have no connection with cu in the next d years to construct negative samples. The set of negative samples is shown as follows:
D− = {xtuv|cv ∈ Nk(cu),At+d(cu, cv) = 0, k ≥ 2, Ts ≤ t < t+ d ≤ Te}, (4)
where Nk(cu) is the set of concepts at a distance less than or equal to k from cu, i.e., the k-hop neighborhood of cu. It is worth noting that the negative samples are used to construct input text
sequences with timestamp t rather than t+ d, and we do not generate negative samples in the last d timestamps. We fine-tune the parameters and vocabulary embeddings of BERT via predicting the masked token. Formally, we compute the crossentropy loss:
L = − ∑
d∈D+∪D− 1[MASK]=yd logP ([MASK] = yd|x t uv),
(5)
where yd ∈ {“related”, “unrelated”} is the label of the sample. It should be mentioned that KGBERT (Yao et al., 2019) and LP-BERT (Da Li et al., 2022) are similar to PLM-LP, but the settings they adopt are not applicable to the training of temporal data. Nevertheless, the PLM in our method can be replaced by other models.

heading: 3.4 Idea Verbalization
text: In our public beta system, we employ T5 (Raffel et al., 2020), a large pretrained sequence-tosequence model for idea verbalization. We select 2M highly-cited papers for unsupervised denoising training with the language model loss:
Llm = Ep[− logP (p|p̃; θ)], (6)
where p̃ represent the corrupted sentence of paper p. In the process of fine-tuning, given a co-occurrence citation quintuple q = (pi, pj , cu, cv, p), we first concatenate pi, pj , cu, and cv to a sequence Seq(q), using ⟨HEAD⟩, ⟨TAIL⟩, ⟨SEP⟩ to denote the head, tail of a concept pair, and the separator, respectively, which is shown as follows:
Seq(q) = ⟨HEAD⟩ cu ⟨TAIL⟩ cv ⟨SEP⟩ pi ⟨SEP⟩ pj .
We fine-tune the T5 model to find the optimal parameters θ∗ to encode the input sequence and verbalize it into an idea sequence, i.e., the item p in the quintuple. For this purpose, we use the maximum likelihood estimation objective:
θ∗ = argmax θ
∏
q
P (p|Seq(q); θ). (7)
During the inference process (production environment), we use the predicted connection of concepts cu, cv, and their corresponding sentences of papers pi, pj to construct the input sequence, which is encoded by our fine-tuned T5 to generate an idea sequence. Note that the idea verbalization model is also flexible in our framework, and it can be substituted by alternatives such as GPT(Radford et al.,
2018) with another configuration of fine-tuning. We will also provide premium subscribers with GPT-3.5 after the official release of our system.

heading: 4 Evaluation

heading: 4.1 Analysis of Temporal Link Prediction

heading: 4.1.1 Results of Link Prediction in 2021
text: PLM-LP is compared with 3 temporal model SEMNET (Krenn and Zeilinger, 2020), GCN-GAN (Lei et al., 2019), and EvolveGCN (Pareja et al., 2020), which are suitable for concept co-occurrence graph. SEMNET analyzes graph characteristics to recognize potential new edges with an MLP module. GCN-GAN and EvolveGCN utilize GCN and LSTM to model the structural and temporal information of a graph. In the experiment, their performance is evaluated on our constructed 240 concept co-occurrence graphs, where the last snapshot (the year 2021) is used as the test set. We report the accuracy of the adjacent matrix, precision, recall, and F1 score of all edges and new edges existing in the graph of 2021. New edges do not exist in the past snapshots and only come out in 2021.
Note that PLM-LP is trained with a single set of model parameters on these 240 graphs and then applied to different graphs for the test procedure. The hyper-parameters k and d in PLM-LP are set to 2 and 5, respectively. Apart from our proposed PLM-LP, we also introduce two variants. PLM-LP w/o pro. removes the prompt words pro(cu, cv, t). PLM-LP ind. is trained with independent parameters on different graphs. Results of these models in 20 disciplines/topics are provided in Appendix H. The average results are shown in Table 1. It can be observed that all these models are capable of identifying most edges existing in 2021, but the GCN-GAN and EvolveGCN gets undesirable performance to find new edges in 2021. Many cases have been predicted to be unconnected. We believe this is because most graphs are sparse, leading to overfitting. In our scenario, detecting new edges is more important than improving the accuracy of the adjacency matrix. Our proposed method can tackle the issue to a certain extent. As to the variants, it is difficult for PLM-LP w/o pro. to correctly predict all edges in 2021 due to the absence of prompt words. PLM-LP ind. is also inferior to PLM-LP, indicating that PLM can learn interdisciplinary knowledge with a set of training parameters.

heading: 4.1.2 Human Assessment of Link Prediction in the Future
text: We use all graph snapshots, including the year 2021, for training to mine potential connections that may appear in the future. Similarly, we select the top 20 pairs of concepts for each query. See Appendix G for the potential connections of different disciplines. We invited more than 10 experts from the field of computer science and geo-science (geology and geography) to evaluate the predicted results in their corresponding domains. The assessment is based on the experience of experts. The results are shown in Table 2. As expected, at least a third of the potential concept pairs predicted by the system are reasonable in the three disciplines, indicating that PLM-LP is able to explore new concepts across disciplines. We also test random pairs on geo-science, and there are no more than 10% of reasonable pairs.

heading: 4.2 Analysis of Idea Verbalization

heading: 4.2.1 Benchmark Results
text: We release the co-occurrence citation quintuples for idea verbalization, which can be used as a benchmark for natural language generation. Our public beta system adopts PLM such as T5 and BART as the generation models that are fine-tuned on the quintuples. We also apply unsupervised denoising training on T5 with highly-cited papers, which makes the PLM itself learn more academic knowledge. All training and inference processes are carried out on NVIDIA GeForce RTX 3090. In the fine-tuning stage, we employ Adam as the opti-
mizer with 0.01 weight decay. The learning rate is set to 1e-4. For the inference, the beam size is set to 4. Similar to previous text generation work (Fan et al., 2018; Wang et al., 2019), we use BLEU (Papineni et al., 2002), METEOR (Banerjee and Lavie, 2005), and ROUGE_L (Lin, 2004) to measure the fluency and topic relevance of the generated ideas. Table 3 gives the benchmark results.
In fact, it is challenging to evaluate long text (Liu et al., 2016; Li et al., 2016), let alone idea verbalization, which may contain new opinions, insights, and methods. Additionally, the new content in the verbalized idea is likely to differ from the target paper in quintuples. Thus, we conduct the following experiments.

heading: 4.2.2 Turing Test
text: Similar to previous work (Wang et al., 2019), we recruited more domain experts and non-experts in the field of computer science, geo-science (geology and geography), and medicine to conduct the Turing test. Experts include professors, lecturers, postdoctoral researchers, and graduate students (at least two professors per discipline). Participants are asked to read the machine-generated outputs and human-written texts and choose the real humanwritten text from a set of N − 1 fake ones. Each participant is given instructions before the test. We also allow participants to use the Internet to retrieve technical terms during the test. For each discipline, there are two different modes of multiple-choice questions, one contains two options per question, and the other contains three options per question. We randomly select 15 questions per test from the
question bank for each participant to answer. We conduct six groups of Turing tests, whose experimental settings are shown in Table 4.
The results are displayed using a box plot in Figure 3. Overall, domain experts are more likely to achieve higher accuracy in these six groups of tests. Also, the results reveal that the accuracy of the 3-options question is lower than 30%, indicating that it is more difficult for participants to choose the human-written one from 3 options than from 2 options. Moreover, the accuracy of the 2-option questions is close to or even lower than that of random guessing, which means experts can hardly distinguish between human-written sentences and machine-generated sentences, although they tend to analyze texts from the perspective of logic and accuracy. One of the possible reasons is that the verbalized ideas contain more nonprofessional terms while maintaining fluency and reasonableness, which is more readable than academic papers.

heading: 4.2.3 Relevance & Plagiarism Analysis
text: We calculate the percentage of n-grams in the input sequence which appear in the verbalized idea of test data to analyze how relevant the idea is to the input sequence. Meanwhile, the percentage of n-grams can also be regarded as a plagiarism check. As seen from Table 5, about 40% of the input 1-grams exist in the output texts, which means the output can combine the knowledge of relevant concepts. Additionally, the percentages of 2 to 5-grams are all lower than 20%, that is, the verbalized ideas are not simply copied from the input but are paraphrased and fused into new knowledge.
We did not provide the n-gram overlap of the target paper p. From our perspective, p is more like an inspiration, which teaches our model a possible way of how two concepts can be combined together, rather than generating sentences exactly like p.

heading: 4.3 Case Study
text: In Appendix E, we provide a page of examples of input sequences, human-written texts, and verbalized ideas according to our test dataset of quintuples. To simulate the real situation, we randomly select cases including new connections PLM-LP predicts, which do not appear in our quintuple dataset. It is worth noting that we only take these two concepts as input and do not enter their corresponding sentences to avoid the impact of potential plagiarism.
Table 6 shows three verbalized ideas. For the first case, we can see our system integrates the critical characteristic of contrastive learning that requires no labels into the task of knowledge graph mining. However, it includes untested experimental results due to the denoising training from numerous papers (especially from paper abstracts and
introduction section), and we remove them with heuristic rules in the production environment. As to the second case, the verbalized idea mentions that intellectual capital, such as intellectual property rights, is closely related to income distribution. In the last case, our system believes that a gender pay gap exists in developing countries, which is more obvious during the economic crisis. These cases show that our system can well predict and verbalize ideas, and the generated results align with human intuition and value. Nevertheless, more details are required in natural and exact sciences.

heading: 5 Related Work

heading: 5.1 Graph Technology for Academic Discovery
text: There are a few graph technical methods to help researchers find new ideas. SEMNET (Krenn and Zeilinger, 2020) predicts research trends with an MLP in the field of quantum physics via constructing such co-occurrence graphs. Sarica et al. proposes a technology graph to stimulate idea generation in engineering design, which aims to discover new concepts in the white space surrounding a focal design domain according to the semantic distance. Besides, InfraNodus (Paranyushkin, 2019), a commercial tool for people in different industries, generates insights by detecting structural gaps in a text network, which is similar to mind maps.

heading: 5.2 Text Generation
text: Pretrained language models, including T5 (Raffel et al., 2020), BART (Lewis et al., 2020), and GPT (Radford et al., 2018) have become the mainstream modules of text generation since they contain billions of parameters and use a large number of corpus for training to achieve good performance. As to text generation for academic research, existing models can only be applied to a few disciplines with much fewer papers than ours. They also require a lot of resources to construct knowledge bases. For instance, PaperRobot (Wang et al., 2019) adopts external domain knowledge graphs to incrementally generate titles, abstracts, and conclusions of a paper. DRAW (Liu et al., 2021a) consists of reader, writer, and reviewer components to generate scientific texts. ChatGPT (OpenAI, 2022) generates human-level texts with proximal policy optimization, but it requires professional prompts to discover new ideas. Galactica (Taylor et al., 2022) is a large language model for science, which can be combined with our link prediction model to enhance its explainability for idea verbalization.

heading: 6 Conclusion
text: We model the emergence of a new idea as two sequential processes: temporal link prediction for exploration and text generation for verbalization. To achieve the objectives, we first construct and release two datasets with new data structures, including evolving concept co-occurrence graph and
co-occurrence citation quintuple. Then, we devise a new temporal link prediction method based on the masked language model, which can be applied to various evolving concept co-occurrence graphs of different disciplines. Finally, we finetune a PLM to verbalize ideas using the released quintuples. The pipeline has been integrated into a system free for researchers to obtain inspiration. From the experiments and the feedback of users, our system can provide useful information for idea discovery. In the future, we will release an academic oriented language model with the paradigm of prompt learning and instruction tuning to tackle both link prediction and text generation.
Limitations
Based on internal review and user feedback, we summarized the following limitations to improve and iteratively update our system and framework in the future. Problem Modeling: New concepts appear yearly in the real world, but the current system cannot generate new concepts. Generally, the emergence of new concepts often comes from the fusion of mature technologies. Thus, we model the idea exploration as link prediction. Note that it is not the only pathway to brew new ideas, but we have verified the effectiveness and rationality of this approach in the experiments. In addition, PLM can be taken as an implicit knowledge graph (Petroni et al., 2019; Wang et al., 2020), which is capable of tackling uncovered concepts in the evolving concept graphs. We will continue exploring the potential of PLM in knowledge discovery and innovation. Logic, Correctness, and Concreteness: Although the verbalized ideas can deceive many experts, they may still lack logic, correctness, and details, especially in natural and exact sciences. It is also a challenge for natural language generation. We plan to use more academic corpus and introduce constraint (Zhang et al., 2020) to alleviate such problems. Temporal Information: In PLM-LP, we simply take the year information as a token in the input sequence. We conduct additional experiments to show that the temporal information is not sensitive to PLM-LP, which can be attributed to the negative sampling and the nature of the strictly evolving network. Two Birds One Stone: The current system employs two different PLMs for link prediction and
idea verbalization, respectively. The development of prompt learning (Liu et al., 2021b) reveals that most NLP problems can be regarded as generation problems. In the future, we will introduce new training settings using a single PLM to address link prediction and idea verbalization simultaneously.
Ethics Statement
The datasets used in our research are collected through open-source approaches. The whole process is conducted legally, following ethical requirements. As for the Turing Test in our study, all participants are well informed about the purpose of experiments and the usage of test data, and we would not leak out or invade their privacy.
We see opportunities for researchers to apply the system to idea discovery, especially for interdisciplinary jobs. We encourage users to explore different combinations of subjects with the help of our system, making the most of its knowledge storage and thus maximizing the exploration ability of the system.
The main focus of the system is to provide a possible direction for future research, but the effect of human researchers will never be neglected.
The massive data from various disciplines behind the system makes it capable of viewing the knowledge of an area in a multi-dimensional perspective and thus helps promote the development of novel interdisciplinary. However, considering the risks of misinformation generated by NLP tools, the verbalization only contains possible insights into new ideas. Researchers must thoroughly consider whether an idea is feasible or leads to adverse societal effects.

heading: Acknowledgements
text: We would like to express our deepest gratitude to the scientists involved in the Deep-time Digital Earth program, whose contributions have been incredibly valuable. Additionally, we extend our thanks to Zhongmou He, Jia Guo, Zijun Di, Shengling Zhu, Yanpeng Li, Qi Li, Jiaxin Ding and Tao Shi from the IIOT Research Center at Shanghai Jiao Tong University for their unwavering support during the development of our system. This work was supported by NSF China (No.42050105, 62020106005, 62061146002, 61960206002), Shanghai Pilot Program for Basic Research - Shanghai Jiao Tong University.

heading: A Distribution of Papers
text: We are an academic service provider with a sufficient number of high-quality literature data sources (including publications and preprints). These sources are reliable and maintained by a team of professional engineers, ensuring the accuracy and persuasiveness of idea-discovery results. Our database contains more than 220 million academic papers from 19 disciplines between 1800 and 2023 and nearly 800K concept entities with corresponding descriptions. Figure 4 shows the number of papers in each discipline. Note that there are a large number of interdisciplinary papers. Our system will retrieve relevant papers from this database according to the queries and guide users to discover new ideas.

heading: B Statistics of Quintuples
text: Table 7 shows the statistics of co-occurrence citation quintuples, which originate from 9.5M target papers and 19.8M reference papers. Their citations
are greater than or equal to 2. In the data preprocessing, when a paper contains multiple sentences corresponding to a concept, we randomly picked up one sentence to construct a quintuple. We finally obtain 92,313 high-quality instances (73,852 for training, 9,230 for validation, and 9231 for testing) after applying a filter mechanism (Appendix C). The distribution of the quintuples and their corresponding concepts are shown in Figure 5. We can see that the numbers of quintuples and concepts of natural science are far more than those of social science, which can be attributed to the paper distribution and citation. In the future, we will lower the citation threshold to get more quintuples of social science.

heading: C Pipeline of Quintuple Construction
text: Figure 6 illustrates the pipeline of constructing quintuples. We select nearly 9.5M highly cited papers (500K per discipline) and their corresponding references (19.7M) to construct quintuples. We employ AutoPhrase (Shang et al., 2018), an information extraction tool to identify concepts. We execute the process of entity linking and alignment to disambiguate duplicate entities and remove lowquality concepts. Then, we retrieve corresponding sentences of papers that mention these concepts. Relevant sentences will be preserved. Additionally, we apply a rule-based filter to our retrieved contents, where sentences including experimental details, acknowledgments, and sentences with a large number of numerical conclusions, etc., are removed. Finally, we obtain 92,313 quintuples.

heading: D Framework of PLM-LP
text: The framework of the temporal link prediction model PLM-LP is illustrated in Figure 7. We first generate positive and negative samples according to the structure of evolving concept co-occurrence graphs. Note that we add prompt (“Existing” and “Unknown”) as the prefix of a sentence. The PLM aims to fill the mask token with a relation token, i.e., “related” and “unrelated”. We use a masked language model BERT as the base PLM. We finetune the parameters and vocabulary embeddings of BERT via minimizing cross-entropy loss. Note that we simply take the year information as a token in the input sequence. We conduct experiments to show that the temporal information is not sensitive to PLM-LP. In the future, we will design a novel temporal prompt to capture more temporal information.

heading: E Examples of Turing Test
text: Table 8 shows the examples (2-option questions) used in the Turing Test. All texts presented in the questions originate from the same quintuple, where the human-written text is extracted from the target paper, and the machine-generated text is the idea verbalized by our T5 model according to the concept pair and their corresponding texts. With randomness, repeating the verbalizing process can generate different outputs, which is helpful in preparing questions that need multiple machinegenerated texts. From these examples, we can see that machine-verbalized ideas can easily deceive domain experts.

heading: F Screenshot of User Interface
text: Our system (DeepReport) is available at website https://idea.acemap.cn. Figure 8 and 9 are screenshots of user interface (public beta version). As demonstrated in Figure 8, after the concept "Carbonate Rock" is entered in the searching box, texts relevant to the keyword are presented in the insights box. The system will then dynamically construct an evolving concept co-occurrence graph based on the query result, where each node represents a concept, and relations between concepts are represented by the co-occurrence edges. We provide animations to demonstrate the evolution of the concept graph. The result of temporal link prediction is shown as concept pairs in the lower left New Relations box, and verbalized idea for each pair is shown in a new dialog box. Researchers can select different concept pairs they are interested in and view the corresponding ideas, as illustrated in figure 9. The system also provides network analytic tools such as community detection algorithms and Sankey diagrams for deeper investigation. The response time of the whole system is within 20 seconds.

heading: G Potential Connections PLM-LP Predicted
text: We apply PLM-LP to the constructed 240 evolving concept co-occurrence graphs. We use all graph snapshots, including the year 2021, for training to mine potential connections that may appear in the future. We select the top K pairs of concepts that are most likely to be connected by calculating the difference between the logits of labels, i.e.,
“related” and “unrelated”. Table 9 presents potential connections PLM-LP predicted in 20 disciplines and topics. The connections are shown as concept pairs with & concatenated. For each discipline, we only display six pairs as examples. In our human assessment, we recruited experts in the field of computer science and geo-science (geology and geography) to evaluate the predicted results in their corresponding domains. Their feedback reveals that at least a third of the potential concept pairs generated by the system are reasonable.

heading: H Comparison Results of Link Predictions on All Disciplines
text: PLM-LP is compared with three up-to-date temporal models: SEMNET (Krenn and Zeilinger, 2020), GCN-GAN (Lei et al., 2019), and EvolveGCN (Pareja et al., 2020), which are applicable to the concept co-occurrence graph. In the experiment, their performance is evaluated on our constructed 240 concept co-occurrence graphs, where the last snapshot (the year 2021) is used as the test set. We report the accuracy of the adjacent matrix, precision, recall, and F1 score of all edges and new edges existing in the graph of 2021. New edges do not exist in the past years and would only come out in 2021. Results of these models in 20 disciplines/topics are provided in Table 10. It should be mentioned that we show the average of 12 evolving concept co-occurrence graphs of each discipline. The results show that GCN-GAN and EvolveGCN are unable to discover new edges. Our proposed PLM-LP is superior to any other models in the task of idea exploration, where the given graphs are strictly evolving network (Skarding et al., 2021).

heading: I Statistics of Evolving Concept Co-occurrence Graph
text: We construct 240 evolving concept co-occurrence graphs (12 graphs per discipline/topics) with Elasticsearch and Autophrase (Shang et al., 2018) according to 240 essential and common queries and relevant papers. Each graph contains 22 temporal snapshots between 2000 and 2021. The statistics of the concept co-occurrence graphs are shown in Tables 11, 12, 13, 14, and 15. These tables provide the corresponding discipline, query, number of nodes (concepts), number of edges in 2021, and selected concepts. We will release the construction code and data set on GitHub for further research, including temporal link prediction, community detection, academic trends analysis, knowledge representation, etc.

heading: J About the Official Version of DeepReport
text: In mid-2023, our DeepReport system underwent a major update, encompassing both data and model improvements. On the data front, we introduced a new version of the quintuple data (V202306), resulting in enhanced quality and a larger-scale dataset. The statistical summary of the new quintuple data (V202306) is presented in Table 16.
Furthermore, we trained a new state-of-the-art model in a specialized domain, which remains internal to our organization. This model, along with the integration of openAI’s interface, was implemented to elevate the quality of our online services. The amalgamation of our proprietary large-scale model and the incorporation of openAI’s resources empowered our system to provide superior performance and better cater to the needs of our users.
The introduction of the improved quintuple dataset, coupled with the deployment of the new specialized domain model and the utilization of openAI’s interface, signifies a significant advancement in our DeepReport system. These updates enable us to deliver more accurate and reliable results, thereby enhancing the overall user experience. We remain committed to further refining our system to ensure it continues to meet the evolving demands of our users.

heading: K Frequently Asked Questions
text: • Q: Comparing to other concepts graphs, what is the advantage of the concept co-occurrence quintuples? A: This question goes to the core of our work. This allows us to capture not only the co-occurrence relationship between concepts, but also their citation relationships, which can provide additional insights into how ideas are related (or generated) in academic literature.
• Q: Why do you think transferring the concept links into natural languages is a necessary step in this assisting process? Your target users are academics. If they couldn’t generate a proper idea from the link of concepts into natural language, do you expect the machines could do it better? A: The ultimate goal of our existing and future work is to enable LLM to generate reasonable, interpretable, and traceable ideas, and we now focus on how to use structured knowledge (here we use concept co-occurrence graphs) to guide this process. Therefore, the verbalization process is necessary. Besides, our system is designed to inspire researchers to discover ideas, rather than to replace them. Since we leverage the knowledge of 19 disciplines to train the model, it is valuable for researchers in certain cross-fields to broaden their points of interest.
• Q: The evaluation of verbalized ideas cannot measure logic, correctness, and details. Since you have already found the experts, could you think of some possibilities to evaluation the academic quality of these generated ideas trough human assessment? A: From our perspective, the human assessment of verbalized ideas is similar to the process of paper review but not identical, and the latter is more subjective. We should follow three principles,
low resources, high efficiency, and objectivity to evaluate the generated ideas. For instance, predefined criteria and multiple rounds of voting can be of help. Meanwhile, we are trying to train human-in-the-loop-based system to assist experts in idea assessment.
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ud en t Ps yc ho lo gy T he Su ic id e In te rv en tio n 12 2 18 35 su ic id e pr ev en tio n, m en ta lh ea lth ,p ub lic he al th Ps yc ho lo gy M en ta lH ea lth O fC hi ld re n 12 0 17 79 m en ta lh ea lth ,h ea lth ca re ,m en ta li lln es s Ps yc ho lo gy H ow D oe s C og ni tiv is m D iff er Fr om B eh av io ri sm ? 50 95 co gn iti ve sc ie nc e, in fo rm at io n pr oc es si ng ,c ul tu ra ld
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en ce s Ps yc ho lo gy R ac is m ,B ia s, A nd D is cr im in at io n 81 68 4 af ri ca n am er ic an ,u ni te d st at es ,c iv il ri gh ts Ps yc ho lo gy D oe s G ro up Po la ri za tio n A ff ec tT he M in or ity ? 94 28 9 un ite d st at es ,a fr ic an am er ic an ,h ea lth ca re Ps yc ho lo gy Pe er Pr es su re O n A ca de m ic Pe rf or m an ce 93 79 9 hi gh sc ho ol ,p ee rg ro up ,p ee rp re ss ur e Ps yc ho lo gy W ha tI s T he R ol e O fC og ni tiv e Fl ex ib ili ty A nd In hi bi tio n In C om pl ex D yn am ic Ta sk s 10 1 74 4 w or ki ng m em or y, ex ec ut iv e fu nc tio n, fr on ta lc or te x Ps yc ho lo gy Se x D iff er en ce s In Fu nc tio na lC on ne ct iv ity B et w ee n R es tin g St at e B ra in N et w or ks In A ut is m Sp ec tr um D is or de r 13 3 24 77 au tis m sp ec tr um di so rd er ,m ag ne tic re so na nc e im ag in g, fu nc
tio na
lm ag
ne tic
re so
na nc
e im
ag in
g
E co
no m
ic s
G lo
ba liz
at io
n A
nd U
ne m
pl oy
m en
t 89
10 22
fin an
ci al
cr is
is ,e
co no
m ic
gr ow
th ,u
ni te
d st
at es
E co
no m
ic s
Su pp
ly C
ha in
M an
ag em
en t
84 10
23 ch
ai n
m an
ag em
en t,
su pp
ly ch
ai n
m an
ag em
en t,
su pp
ly ch
ai n
E co
no m
ic s
Vo la
til ity
A nd
T he
C ro
ss -S
ec tio
n O
fR ea
lE st
at e
E qu
ity R
et ur
ns D
ur in
g Pa
nd em
ic 58
42 6
re al
es ta
te in
ve st
m en
tt ru
st ,fi
na nc
ia lc
ri si
s, as
se ta
llo ca
tio n
E co
no m
ic s
H um
an C
ap ita
lA nd
C hi
na ’S
Fu tu
re G
ro w
th 91
12 50
ec on
om ic
gr ow
th ,h
um an
ca pi
ta l,
ec on
om ic
de ve
lo pm
en t
E co
no m
ic s
W ha
tC ri
tic al
A pp
ro ac
h To
N eo
cl as
si ca
lE co
no m
ic s
Is Su
pe ri
or ?
10 2
12 55
cl as
si ca
le co
no m
ic s,
ec on
om ic
th eo
ry ,e
co no
m ic
gr ow th E co no m ic s T he E co no m ic Po lic y U nc er ta in ty 11 4 12 18 ec on om ic gr ow th ,m on et ar y po lic y, un ite d st at es E co no m ic s U s E ar ni ng s In eq ua lit y 10 1 10 24 in co m e in eq ua lit y, un ite d st at es ,w ag e in eq ua lit y E co no m ic s W ha tI s T he A pp lic at io n O fF ix ed Po in tT he or y In Fi na nc ia lA nd E co no m ic Sc ie nc es ? 10 4 10 15 fin an ci al m ar ke t, ec on om ic th eo ry ,fi na nc ia lc ri si s E co no m ic s T he D ig ita lE co no m y 91 10 01 di gi ta le co no m y, di gi ta ld iv id e, ec on om ic gr ow th E co no m ic s H ow R at e H ik es C an E xa ce rb at e L ab or -M ar ke tI ne qu al ity 96 10 76 in co m e in eq ua lit y, un ite d st at es ,h um an ca pi ta l E co no m ic s W hy D o B uy er s Pa y D iff er en tP ri ce s Fo rC om pa ra bl e Pr od uc ts ? 74 20 2 su pp ly ch ai n, on lin e au ct io n, in fo rm at io n as ym m et ry E co no m ic s T he Im pa ct O fG lo ba liz at io n O n In co m e D is tr ib ut io n In E m er gi ng E co no m ie s 12 4 23 84 in co m e di st ri bu tio n, ec on om ic gr ow th ,d ev el op in g co un
tr ie
s
Ta bl
e 12
:S ta
tis tic
s of
qu er
ie s
an d
co rr
es po
nd in
g ev
ol vi
ng co
nc ep
tc o-
oc cu
rr en
ce gr
ap hs
in M
at he
m at
ic s,
H is
to ry
,P sy
ch ol
og y,
an d
E co
no m
ic s.
D is
ci pl
in e
Q ue
ry N
um .o
fN od
es N
um .o
fE dg
es (2
02 1)
Se le
ct ed
C on
ce pt s So ci ol og y G en de rI ne qu al ity In A m er ic a 10 2 15 34 ge nd er eq ua lit y, la
tin am
er ic
a, ec
on om
ic gr
ow th
So ci
ol og
y A
bo rt
io n
A nd
A bo
rt io
n R
ig ht
s 95
11 74
un ite
d st
at es
,h ea
lth ca
re ,r
ep ro
du ct
iv e
ri gh ts So ci ol og y So ci al N et w or ks A dd ic tio n 77 74 2 so ci al ne tw or k, so ci al ne tw or ks ,s oc ia ls up po rt So ci ol og y D oe s A n Im pr ov em en tI n R ur al In fr as tr uc tu re C on tr ib ut e To A lle vi at e Po ve rt y In Pa ki st an ? 12 4 17 41 po ve rt y re du ct io n, de ve lo pi ng co un tr ie s, ec on om
ic gr
ow th
So ci
ol og
y Sp
re ad
O fF
al se
N ew
s 66
18 8
fa ls
e al
ar m
,s oc
ia lm
ed ia
,m ac
hi ne
le ar
ni ng
So ci
ol og
y R
eg io
na lI
de nt
ity A
nd R
eg io
na lC
ha ng
e 89
79 8
eu ro
pe an
un io
n, ea
st as
ia ,e
co no
m ic
de ve
lo pm
en t
So ci
ol og
y W
ha tI
s T
he Im
pa ct
O fI
nt er
ge ne
ra tio
na lM
ob ili
ty O
n W
el l-
B ei
ng In
Ja pa
n 92
50 5
un ite
d st
at es
,h um
an ca
pi ta
l, in
co m
e in
eq ua
lit y
So ci
ol og
y So
ci al
M ed
ia A
nd M
ar ke
tin g
10 0
13 25
so ci
al m
ed ia
,m ed
ia m
ar ke
t, so
ci al
m ed
ia m
ar ke
tin g
So ci
ol og
y C
an Po
pu lis
m C
on tr
ib ut
e To
A M
or e
In cl
us iv
e C
iti ze
ns hi
p? 97
78 5
hu m
an ri
gh ts
,u ni
te d
st at
es ,g
lo ba
lc iti
ze ns
hi p
So ci
ol og
y A
dv an
ce m
en tO
fW om
en In
Sc ie
nc e
11 3
96 3
un ite
d st
at es
,n at
io na
ls ci
en ce
fo un
da tio
n, ca
re er
ad va
nc em
en t
So ci
ol og
y H
ow A
re Fu
nc tio
na lis
m A
nd C
on fli
ct T
he or
y Si
m ila
r? 80
33 3
co nfl
ic tr
es ol
ut io
n, co
nfl ic
tm an
ag em
en t,
in te
rn at
io na
lr el
at io ns So ci ol og y H ow D o So ci al N et w or ks In flu en ce E du ca tio na lP ro ce ss es 11 2 11 19 so ci al ne tw or k, so ci al ne tw or ks ,s oc ia li nfl ue nc e A rt M od er n A es th et ic s 47 15 5 tr ad iti on al ch in es e, ch in es e tr ad iti on al ,w an g gu ow ei A rt E as te rn A si an A rt 11 7 11 74 ea st as ia ,s ou th as ia ,c en tr al as ia A rt Su rr ea lis tA es th et ic s 34 12 3 an dr e br et on ,t w en tie th ce nt ur y, w or ld w ar A rt M od er n R oc k M us ic Tr en d 88 95 6 po pu la rm us ic ,r oc k m us ic ,m us ic in du st ry A rt In te ra ct io n D es ig n 83 73 6 in te ra ct io n de si gn ,i nt er fa ce de si gn ,u se re xp er ie nc e A rt V ic to ri an B ea ut y St an da rd s In A rt 72 39 5 vi ct or ia n pe ri od ,v ic to ri an er a, vi ct or ia n en gl an d A rt A re C ul tu ra lly V ib ra nt C om m un iti es H ea lth ie r? 98 48 9 un ite d st at es ,p ub lic he al th ,c ul tu ra lh er ita ge A rt T he R ol e O fC ul tu ra lI de nt ity In T he C re at io n O fA rt 11 2 10 02 cu ltu ra li de nt ity ,c on te m po ra ry ar t, ca se st ud y A rt H ow A rt D ev el op s T he Pe rs on al ity O fH um an B ei ng s? 10 8 50 4 hu m an de ve lo pm en t, vi su al ar ts ,p er so na ld ev el op m en t A rt H ow C an G ra ffi ti B e A cc ep te d A s A Fo rm O fS tr ee tA rt A nd W hi ch A ttr ib ut es C an B e C on tr ib ut ed To A rc hi te ct ur e? 79 49 2 pu bl ic sp ac e, st re et ar t, gr af fit ia rt A rt T he U se O fA rt s In te rv en tio ns Fo rM en ta lH ea lth A nd W el lb ei ng In H ea lth Se tti ng s 14 0 16 95 m en ta lh ea lth ,m en ta li lln es s, pu bl ic he al th A rt W ha tI s T he D iff er en ce B et w ee n Is la m ic A rt A nd C hr is tia n A rt In Te rm s O fF un ct io n In T he M id dl e A ge s? 12 6 13 57 m id dl e ag e, m id dl e ea st ,i sl am ic ar t B us in es s E m pl oy ee M ot iv at io n 77 68 7 jo b sa tis fa ct io n, hu m an re so ur ce ,d ep en de nt va ri ab le B us in es s In te rn at io na lT ra de Tr en ds In T he U sa 11 3 14 17 de ve lo pi ng co un tr ie s, fr ee tr ad e, un ite d st at es B us in es s H ow B lo ck ch ai n A nd C ry pt oc ur re nc y C an R ev ol ut io ni ze B us in es s? 85 66 3 bu si ne ss m od el ,b us in es s pr oc es s, bl oc kc ha in te ch no lo gy B us in es s W ha tI s T he Im pa ct O fI nt er m ed ia ri es O n A N eg ot ia tio n? 82 32 0 un ite d st at es ,d ev el op in g co un tr ie s, eu ro pe an un io n B us in es s In te rn at io na lB us in es s, Fu rt he rG lo ba lis at io n O rB ac kl as h? 11 7 94 9 in te rn at io na lt ra de ,d ev el op in g co un tr ie s, bu si ne ss en vi ro nm en t B us in es s T he M ar ke tG ro w th Fo rE le ct ri c V eh ic le s 92 15 99 el ec tr ic ve hi cl e, hy br id el ec tr ic ve hi cl e, hy br id el ec tr ic B us in es s C ur re nt Tr en ds In C on su m er B eh av io r 74 33 0 lit er at ur e re vi ew ,s oc ia lm ed ia ,o nl in e sh op pi ng B us in es s W hy Is T he Im po rt an ce O fT he C or re la tio n A na ly si s B et w ee n T he St oc k M ar ke tV al ua tio n A nd T he E co no m ic Si tu at io n O fB us in es s E nt iti es G ro w in g? 10 0 14 22 st oc k m ar ke t, fin an ci al m ar ke t, ca pi ta lm ar ke t B us in es s H ow To E va lu at e C os tI m pa ct s O n R ev er se L og is tic s U si ng A n E co no m ic O rd er Q ua nt ity (E oq )M od el W ith E nv ir on m en ta lA nd So ci al C on si de ra tio ns 61 33 0 su pp ly ch ai n m an ag em en t, su st ai na bl e de ve lo pm en t, ca rb on
em is
si on
B us
in es
s G
lo ba
lU ne
m pl
oy m
en t
94 10
85 ec
on om
ic gr
ow th
,u ni
te d
st at
es ,u
ne m
pl oy
m en
ti ns
ur an ce B us in es s C ap ita lis m A nd M ul tin at io na lC om pa ni es 80 97 5 m ul tin at io na lc or po ra tio n, fo re ig n di re ct in ve st m en t, hu m
an ca
pi ta l B us in es s N ex tF in an ci al C ri si s 98 18 97 gl ob al fin an ci al cr is is ,fi na nc ia lm ar ke t, fin an ci al cr is es Ph ys ic s M ic ro flu id ic s A nd M ic ro sy st em s 99 55 4 ce ll cu ltu re ,i nt eg ra te d ci rc ui t, sa m pl e pr ep ar at io n Ph ys ic s H ow Sw ar m R ob ot ic s C an B e U se d To D es cr ib e Pa rt ic le Sy st em ’S D ef or m at io n 66 47 4 pa rt ic le sw ar m op tim iz at io n, m ob ile ro bo t, sw ar m in te lli ge nc e Ph ys ic s D ar k M at te r 81 12 08 da rk m at te r, di re ct de te ct io n, st an da rd m od el Ph ys ic s G al ax y Fo rm at io n A nd E vo lu tio n 91 16 71 st ar fo rm at io n, ga la xy ev ol ut io n, ga la xy fo rm at io n Ph ys ic s B ig B an g (Q ua nt um )C os m ol og y 70 10 92 bi g ba ng ,c os m ol og ic al co ns ta nt ,d ar k m at te r Ph ys ic s G lo ba lN on lin ea rS ta bi lit y O fL ar ge D is pe rs iv e So lu tio ns To T he E in st ei n E qu at io ns 55 31 8 di ff er en tia le qu at io n, ca uc hy pr ob le m ,p ar tia ld iff er en tia le qu at io ns Ph ys ic s H ow D oe s T he M ag ne to re si st an ce R efl ec tT he In fo rm at io n O fF er m iS ur fa ce ? 55 56 4 fe rm is ur fa ce ,m ag ne tic fie ld ,f er m il ev el Ph ys ic s C on de ns ed M at te rP hy si cs A nd A co us tic s 92 11 56 co nd en se d m at te r, co nd en se d m at te rp hy si cs ,p ha se tr an si tio n Ph ys ic s T he D ev el op m en ts In Q ua nt um C om pu te rs 92 12 48 qu an tu m co m pu tin g, qu an tu m co m pu te rs ,q ua nt um co m pu ta tio n Ph ys ic s O pt ic al Ph ys ic s A nd Q ua nt um In fo rm at io n Sc ie nc e 11 3 22 16 qu an tu m in fo rm at io n, qu an tu m m ec ha ni cs ,q ua nt um ph ys ic s Ph ys ic s T he Sp ac eTi m e G eo m et ry B eh in d T he C on st an tS pe ed O fL ig ht 11 8 18 26 ge ne ra lr el at iv ity ,s pe ci al re la tiv ity ,c os m ol og ic al co ns ta nt Ph ys ic s A nt if er ro m ag ne tic Sp in tr on ic 55 47 0 m ag ne tic fie ld ,m ag ne tic m om en t, gr ou nd st at e
Ta bl
e 13
:S ta
tis tic
s of
qu er
ie s
an d
co rr
es po
nd in
g ev
ol vi
ng co
nc ep
tc o-
oc cu
rr en
ce gr
ap hs
in So
ci ol
og y,
A rt
,B us
in es
s, an
d Ph
ys ic
s.
D is
ci pl
in e
Q ue
ry N
um .o
fN od
es N
um .o
fE dg
es (2
02 1)
Se le
ct ed
C on
ce pt s Po lit ic al Sc ie nc e Po lit ic s A nd D ip lo m ac y 94 12 61 fo re ig n po lic y, pu
bl ic
di pl
om ac
y, un
ite d
st at es Po lit ic al Sc ie nc e D em oc ra cy A nd T he Pu bl ic In T he E ur op ea n U ni on 13 2 24 06 eu ro pe an un io n, m em be rs ta te ,e ur op ea n in te
gr at
io n
Po lit
ic al
Sc ie
nc e
W ha
tI s
T he
M os
tP ow
er fu
lA ct
O fP
ol iti
ca lP
ar tic
ip at
io n?
10 1
79 5
un ite
d st
at es
,p ol
iti ca
lp ar
tie s,
ci vi
ls oc
ie ty
Po lit
ic al
Sc ie
nc e
R ur
al R
ev ita
liz at
io n
10 8
58 3
ec on
om ic
de ve
lo pm
en t,
su st
ai na
bl e
de ve
lo pm
en t,
un ite
d st
at es
Po lit
ic al
Sc ie
nc e
O pp
or tu
ni tie
s A
nd C
ha lle
ng es
Fa ci
ng C
hi na
’S E
co no
m ic
“E xt
er na
lC ir
cu la
tio n”
94 78
8 ec
on om
ic de
ve lo
pm en
t, ec
on om
ic gr
ow th
,s us
ta in
ab le
de ve
lo pm
en t
Po lit
ic al
Sc ie
nc e
H ow
Po lit
ic ia
ns U
se So
ci al
M ed
ia ?
79 89
1 so
ci al
m ed
ia ,s
oc ia
ln et
w or
k, m
as s
m ed ia Po lit ic al Sc ie nc e T he B as ic s O fT he T he or et ic al Sy st em O fS oc ia lis m W ith C hi ne se C ha ra ct er is tic s 83 57 6 ch in es e ch ar ac te r, co m m un is tp ar ty ,d en g
xi ao
pi ng
Po lit
ic al
Sc ie
nc e
H ow
Po lit
ic al
O ri
en ta
tio n,
E co
no m
ic Pr
ec ar
ity ,A
nd Pa
rt ic
ip an
tD em
og ra
ph ic
s Im
pa ct
C om
pl ia
nc e
W ith
C O
V ID
-1 9
Pr ev
en tio
n M
ea su
re s
In A
D ut
ch R
ep re
se nt
at iv
e Sa
m pl
e? 11
9 73
7 ec
on om
ic de
ve lo
pm en
t, un
ite d
st at
es ,e
co no
m ic
gr ow
th
Po lit
ic al
Sc ie
nc e
C hi
ne se
C om
m un
is tP
ar ty
H ie
ra rc
hy 67
50 9
ch in
es e
co m
m un
is tp
ar ty
,c om
m un
is tp
ar ty
,c hi
ne se
co m
m un
is t
Po lit
ic al
Sc ie
nc e
L an
d Sy
st em
R ef
or m
at io
n 74
68 1
la nd
re fo
rm ,l
an d
te nu
re ,a
gr ar
ia n
re fo rm Po lit ic al Sc ie nc e V ie tn am W ar In te re st s A gg re ga tio n 11 6 20 16 vi et na m w ar ,u ni te d st at es ,s ou th vi et na m Po lit ic al Sc ie nc e R us si a A nd N at o R el at io ns hi ps 11 3 26 71 co ld w ar ,u ni te d st at es ,n or th at la nt ic Ph ilo so ph y Se lf C on sc io us ne ss 52 18 3 al te re d st at es ,g lo ba lw or ks pa ce th eo ry ,v
eg et
at iv
e st
at e
Ph ilo
so ph
y W
hy D
o W
e St
riv e
Fo rP
er fe
ct io
n If
It Is
N ot
A tta
in ab
le ?
26 74
hi gh
er ed
uc at
io n,
un ite
d st
at es
,h um
an ca
pi ta l Ph ilo so ph y D ea lis tic U nd er st an di ng O fE xi st en ce 41 68 co nc ep tu al m od el ,c on ce pt ua lf ra m ew or k,
ne ur
al ne
tw or k Ph ilo so ph y Ph ilo so ph ic al A nt hr op ol og y 49 25 2 ph ilo so ph ic al an th ro po lo gy ,c ul tu ra la nt hr op ol og y, m ed ic
al an
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ACL 2023 Responsible NLP Checklist

heading: A For every submission:
text: 3 A1. Did you describe the limitations of your work?
Limitation Section
3 A2. Did you discuss any potential risks of your work? 4.3 and Limitation Section and Ethics Statement Section
3 A3. Do the abstract and introduction summarize the paper’s main claims? 1
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? 2
B1. Did you cite the creators of artifacts you used? Not applicable. Left blank.
3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? In supplementary materials.
3 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Appendix A
7 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Our data are published academic papers and do not contain individual people or offensive content.
3 B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Appendix A, B, I
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 4.1
C 3 Did you run computational experiments? 4
3 C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? 4.2
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.
3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? 4.1, 4.2
3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 4.1, 4.2, Appendix H
3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? 4.1, 4.2
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? 4.1.2, 4.2.2
3 D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? 4.2.2
3 D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? 4.2.2
3 D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? 4 and Appendix B,C
3 D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Appendix B,C
3 D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? 4.2.2

heading: Future Work
text: If they couldn’t generate a proper idea from the link of concepts into natural language, do you expect the machines could do it better? A: The ultimate goal of our existing and future work is to enable LLM to generate reasonable, interpretable, and traceable ideas, and we now focus on how to use structured knowledge (here we use concept co-occurrence graphs) to guide this process. Therefore, the verbalization process is necessary. Besides, our system is designed to inspire researchers to discover ideas, rather than to replace them. Since we leverage the knowledge of 19 disciplines to train the model, it is valuable for researchers in certain cross-fields to broaden their points of interest. • Q: The evaluation of verbalized ideas cannot measure logic, correctness, and details. Since you have already found the experts, could you think of some possibilities to evaluation the academic quality of these generated ideas trough human assessment? A: From our perspective, the human assessment of verbalized ideas is similar to the process of paper review but not identical, and the latter is more subjective. We should follow three principles,
low resources, high efficiency, and objectivity to evaluate the generated ideas. For instance, predefined criteria and multiple rounds of voting can be of help. Meanwhile, we are trying to train human-in-the-loop-based system to assist experts in idea assessment. D is
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st at es C O V ID -1 9 E ff ec tiv e W ay s To Pr ev en tC O V ID -1 9 72 33 7 pu bl ic he al th ,r is k fa ct or ,h ea lth ca re C O V ID -1 9 C lin ic al Pr es en ta tio n O fC ov id 19 In D em en tia Pa tie nt s 13 0 20 34 va sc ul ar de m en tia ,f ro nt ot em po ra ld em en tia ,m ild co gn iti ve
im pa
ir m
en t
C O
V ID
-1 9
W ha
tI s
T he
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s O
fD ru
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12 8
72 7
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ic al
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C O
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-1 9
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or ta
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O fH
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k Pa
tie nt
s U
nd er
go in
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rg er
y 19
6 43
82 lo
gi st
ic re
gr es
si on
,o dd
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on fid
en ce
in te
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C om
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en ce
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n W
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68 23
6 so
ci al
ne tw
or k,
co m
pl ex
ne tw
or k,
co m
pu ta
tio na
lc om
pl ex ity C om pu te rS ci en ce B ia s A nd D is cr im in at io n In A rt ifi ci al In te lli ge nc e 81 85 7 ar tifi ci al in te lli ge nc e, ar tifi ci al ne ur al ne tw or k, ne ur al ne tw or k C om pu te rS ci en ce T he D ev el op m en tO fA rt ifi ci al In te lli ge nc e In C hi na 10 4 11 56 ar tifi ci al in te lli ge nc e, ne ur al ne tw or k, ar tifi ci al ne ur al ne tw or k C om pu te rS ci en ce In te rp re ta bi lit y O fA rt ifi ci al In te lli ge nc e 94 10 17 ar tifi ci al in te lli ge nc e, ne ur al ne tw or k, ar tifi ci al ne ur al ne tw or k C om pu te rS ci en ce C ur re nt Tr en ds O fC om pu te rG ra ph ic s 84 31 8 us er in te rf ac e, vi rt ua lr ea lit y, gr ap hi ca lu se ri nt er fa ce C om pu te rS ci en ce H ow To Im pr ov e T he A pp lic at io n O fM ac hi ne L ea rn in g In Pr od uc tD ev el op m en t 12 4 13 24 m ac hi ne le ar ni ng ,n eu ra ln et w or k, su pp or tv ec to rm ac hi ne C om pu te rS ci en ce C om m er ci al iz at io n O fA rt ifi ci al In te lli ge nc e 92 94 2 ar tifi ci al in te lli ge nc e, ne ur al ne tw or k, ar tifi ci al ne ur al ne tw or k C om pu te rS ci en ce N at ur al L an gu ag e Pr oc es si ng A nd Pr et ra in ed M od el 80 86 2 na tu ra ll an gu ag e, na tu ra ll an gu ag e pr oc es si ng ,m ac hi ne tr an
sl at
io n
C om
pu te
rS ci
en ce
T he
D ev
el op
m en
tO fC
om pu
te rV
is io
n 89
49 4
im ag
e pr
oc es
si ng
,m ac
hi ne
vi si
on ,v
is ua
la cu ity C om pu te rS ci en ce T he D ev el op m en tO fG ra ph N eu ra lN et w or k 88 62 3 ne ur al ne tw or k, ar tifi ci al ne ur al ne tw or k, gr ap
h th
eo ry
C om
pu te
rS ci
en ce
Is C
om pu
te rS
ci en
ce C
on si
de re
d Sc
ie nc
e O
rE ng
in ee
ri ng
? 95
80 1
m ec
ha ni
ca le
ng in
ee ri
ng ,l
if e
sc ie
nc e,
so ci
al sc
ie nc es C om pu te rS ci en ce H ow W ill A rt ifi ci al In te lli ge nc e D ev el op In T he Fu tu re ? 10 6 12 21 ar tifi ci al in te lli ge nc e, ne ur al ne tw or k, ar tifi ci al ne ur
al ne
tw or k G eo gr ap hy A Vo lc an ic E ru pt io n A s T he E ar th ’S D ev as ta tin g Fo rc e 13 4 13 09 vo lc an ic er up tio n, vo lc an ic as h, la va flo w G eo gr ap hy Fe at ur es A nd Q ua lit ie s O fC oa st al E ro si on 13 2 98 7 re m ot e se ns in g, cl im at e ch an ge ,s ed im en tt ra ns po rt G eo gr ap hy A ss es sm en tO fC lim at e Se ns iti vi ty 14 7 18 26 cl im at e ch an ge ,c lim at e se ns iti vi ty ,g re en ho us e ga s G eo gr ap hy G eo gr ap hy A nd E co no m ic D ev el op m en t 92 94 0 ec on om ic ge og ra ph y, ec on om ic de ve lo pm en t, ec on om ic gr ow th G eo gr ap hy Im pa ct O fC lim at e C ha ng e O n A gr om et eo ro lo gi ca lD is as te rs A nd Pe st s A nd D is ea se s 15 2 26 71 na tu ra ld is as te r, gl ob al w ar m in g, fo od se cu ri ty G eo gr ap hy H ow H um an A ct iv iti es C on tr ib ut e To C lim at e C ha ng e 15 7 26 88 cl im at e ch an ge ,g lo ba lw ar m in g, gl ob al cl im at e ch an ge G eo gr ap hy E ff ec tO fI ce L os s O n Se a L ev el R is e 15 1 31 49 se a le ve l, se a le ve lr is e, cl im at e ch an ge G eo gr ap hy A ni m al E xt in ct io n A nd W ay s O fP re ve nt in g T he H um an R ol e In It 10 7 71 8 an xi et y di so rd er s, pr ef ro nt al co rt ex ,m ed ia lp re fr on ta lc or te x G eo gr ap hy W ha tI s T he Im pa ct O fU rb an E xp an si on O n Pl an tD iv er si ty C ha ng e In K ar st R eg io ns O fS ou th w es tC hi na 11 8 12 88 so ut hw es tc hi na ,c lim at e ch an ge ,s ou th ch in a G eo gr ap hy A ss es sm en tM et ho d O fT he Se a Tu rt le -N es tin g H ab ita tO fS m al lR ee fI sl an ds 14 4 17 34 se a tu rt le ,g re en tu rt le ,c lim at e ch an ge G eo gr ap hy W ha tI s T he Im pa ct O fS ea L ev el R is e O n E co lo gi ca lI nf ra st ru ct ur e? 16 7 19 60 cl im at e ch an ge ,s to rm su rg e, tid e ga ug e G eo gr ap hy W ha tC au se Sh or tT er m Se a L ev el C ha ng e In C re ta ce ou s? 17 6 24 90 cl im at e ch an ge ,l at e cr et ac eo us ,e ar ly cr et ac eo us G eo lo gy C om po si tio n O fG eo sp he re 91 38 3 cl im at e ch an ge ,r ad io ac tiv e w as te ,g lo ba lw ar m in g G eo lo gy G la ci er M as s C ha ng e 15 9 18 59 m as s ba la nc e, cl im at e ch an ge ,d ig ita le le va tio n m od el G eo lo gy E vo lu tio n O fS ed im en ta ry R oc k Fo rm at io n O fA R oc k A ss oc ia tio n L ev el 11 1 17 49 se di m en ta ry ro ck ,s ou rc e ro ck ,t ra ce el em en t G eo lo gy Su pe ri m po se d M et am or ph is m O fC hi ne se C oa l 94 46 3 co al be d m et ha ne ,t ra ce el em en t, fu nc tio na lg ro up G eo lo gy W ha tI s T he C om pl et e Pr oc es s O fB as in Fo rm at io n? 10 7 14 14 so ur ce ro ck ,l at e cr et ac eo us ,e ar ly cr et ac eo us G eo lo gy E ff ec tO fT he C om bi na tio n C ha ra ct er is tic s O fR oc k St ru ct ur al Pl an e O n T he St ab ili ty O fA R oc kM as s Sl op e 53 32 8 fin ite el em en t, sh ea rs tr en gt h, op en pi t G eo lo gy H ow D o G eo lo gi ca lP la te s C ha ng e? 10 9 98 0 pl at e te ct on ic s, no rt h am er ic a, cl im at e ch an ge G eo lo gy A C as e St ud y A ss es sm en tO fS oi lL iq ue fa ct io n Po te nt ia l 68 32 3 ca se hi st or ie s, pe ak gr ou nd ac ce le ra tio n, sh ea rw av e ve lo ci ty G eo lo gy G lo ba lD is tr ib ut io n O fC ar bo na te 13 7 21 27 cl im at e ch an ge ,c ar bo n cy cl e, ca rb on di ox id e G eo lo gy W ha tI s T he Im pa ct O fM an O n G eo -E nv ir on m en t? 11 1 58 2 re m ot e se ns in g, su st ai na bl e de ve lo pm en t, cl im at e ch an ge G eo lo gy D iff er en ce s In T he In flu en ce O fT he Te ct on ic Se tti ng O fT he E ar th O n T he Fo rm at io n O fM ag m a 10 9 14 21 ra re ea rt h el em en t, ra re ea rt h, vo lc an ic ro ck G eo lo gy E vo lu tio n O fA rc he an C on tin en ta lC ru st 13 4 15 11 co nt in en ta lm ar gi n, oc ea ni c cr us t, pa rt ia lm el tin g
Ta bl
e 11
:S ta
tis tic
s of
qu er
ie s
an d
co rr
es po
nd in
g ev
ol vi
ng co
nc ep
tc o-
oc cu
rr en
ce gr
ap hs
in C
O V
ID -1
9, C
om pu
te rS
ci en
ce ,G
eo gr
ap hy
,a nd
G eo
lo gy. D is
ci pl
in e
Q ue
ry N
um. o
fN od
es N
um. o
fE dg
es (2
02 1)
Se le
ct ed
C on
ce pt s M at he m at ic s B ir ch A nd Sw in ne rt on -D ye rC on je ct ur e 84 42 7 be tu la pe nd ul a, gr ow
in g
se as
on ,s
ilv er
bi rc h M at he m at ic s To po lo gy A nd D iff er en tia lG eo m et ry 82 11 00 di ff er en tia lg eo m et ry ,d iff er en tia le qu at io
ns ,d
iff er
en tia
lf or
m s
M at
he m
at ic
s W
ha tI
s T
he G
eo m
et ri
c M
ea ni
ng Fo
rR ig
id ity
In R
ie m
an ni
an M
an if
ol ds
? 54
38 2
ri em
an ni
an ge
om et
ry ,l
ie gr
ou p,
se ct
io na
lc ur
va tu re M at he m at ic s R ec en tD ev el op m en ts In T he N av ie rSt ok es Pr ob le m 37 19 9 fin ite el em en t, flu id dy na m ic s, re yn ol ds nu m be r M at he m at ic s R ec en tD ev el op m en ts In R ie m an n H yp ot he si s 71 64 3 ri em an n hy po th es is ,z et a fu nc tio n, ri em an n ze ta
fu nc
tio n
M at
he m
at ic
s W
he n
Is A
Fi ni
te Ty
pe D
up in
H yp
er su
rf ac
e O
fA H
yp er
sp he
re Is
op ar
am et
ri c?
43 17
4 eu
cl id
ea n
sp ac
e, ri
em an
ni an
m an
if ol
d, ve
ct or
fie ld
M at
he m
at ic
s N
um er
ic al
A na
ly si
s A
nd Sc
ie nt
ifi c
C om
pu tin
g 12
7 24
89 sc
ie nt
ifi c
co m
pu tin
g, nu
m er
ic al
m et
ho ds
,n um
er ic
al an
al ys is M at he m at ic s T he Fu nd am en ta lG ro up O fA N on co m m ut at iv e Sp ac e 60 64 3 fie ld th eo ry ,q ua nt um m ec ha ni cs ,p ha se sp ac e M at he m at ic s D ou bl e Ph as e A ni so tr op ic V ar ia tio na lP ro bl em 75 47 1 bo un da ry co nd iti on ,fi ni te el em en t, ph as e tr an si tio n M at he m at ic s C om pl ex N et w or k A na ly si s O fN on lin ea rT im e Se ri es 81 87 9 ne ur al ne tw or k, ar tifi ci al ne ur al ne tw or k, dy na m ic al sy st em s M at he m at ic s P V er su s N p Pr ob le m A nd It A pp ro xi m at io n 51 26 6 ap pr ox im at io n al go ri th m ,c om bi na to ri al op tim iz at io n, ap pr ox
im at
io n
ra tio
M at
he m
at ic
s R
at io
na lit
y O
fR ig
id Q
ui ve
rG ra
ss m
an ni
an s
38 22
7 m
od ul
is pa
ce ,l
ie al
ge br
a, fix
ed po
in t
H is
to ry
T he
Si lk
R oa
d 79
42 8
si lk
ro ad
,c en
tr al
as ia
,b om
by x
m or i H is to ry W he n M us ic M at te re d? 73 42 5 m us ic ed uc at io n, cl as si ca lm us ic
,m us
ic al
in st
ru m
en t
H is
to ry
C hi
na A
nd T
he W
es t:
So ci
et y
A nd
C ul
tu re
94 34
9 w
es te
rn cu
ltu re
,u ni
te d
st at
es ,c
ul tu
ra ld
iv er
si ty
H is
to ry
G ov
er na
nc e
In A
nc ie
nt 59
22 5
an ci
en tg
re ek
,a nc
ie nt
gr ee
ce ,h
an dy
na st y H is to ry T he D om es tic Po lic y O fT he E ur op ea n W ei m ar R ep ub lic 12 5 17 34 eu ro pe an un io n, m em be rs ta te ,c ze ch re pu
bl ic
H is
to ry
W ha
tC ha
ng ed
A ft
er T
he O
ct ob
er R
ev ol
ut io
n? 92
72 0
oc to
be rr
ev ol
ut io
n, in
du st
ri al
re vo
lu tio
n, un
ite d
st at es H is to ry C ap ita lis m In A m er ic a 98 13 88 la tin am er ic a, la tin am er ic an ,u ni te d st at es H is to ry T he Im pa ct O fM ar iti m e Tr ad e O n W or ld C iv ili za tio n. 11 4 12 89 un ite d st at es ,f re e tr ad e, ea st as ia H is to ry T he R ei gn A nd L if e O fQ ue en E liz ab et h 93 60 0 he nr y vi ,e liz ab et h ii, ed w ar d vi H is to ry G ov er ni ng T he N ew N at io n 11 0 10 22 un ite d st at es ,u ni te d na tio ns ,c as e st ud y H is to ry B ri tis h C ol on ia lS tu di es 11 8 11 45 br iti sh em pi re ,w or ld w ar ,u ni te d st at es H is to ry So ci al M ov em en ts In A m er ic a 14 2 21 49 so ci al m ov em en t, un ite d st at es ,l at in am er ic a Ps yc ho lo gy L es bi an ,G ay ,B is ex ua l, Tr an sg en de r 54 48 9 se xu al or ie nt at io n, m en ta lh ea lth ,u ni te d st at es Ps yc ho lo gy Po si tiv e Ps yc ho lo gy 71 48 0 po si tiv e ps yc ho lo gy ,m en ta lh ea lth ,o rg an iz at io na lb
eh av
io r
Ps yc
ho lo
gy Ps
yc ho
lo gy
A nd
C ri
m in
ol og
y 77
81 2
cr im
in al
ju st
ic e,
so ci
al co
nt ro
l, cr
im in
al be
ha vi or Ps yc ho lo gy Pe rs on al Pe rc ep tio n A nd Se lf -C on sc io us ne ss 62 24 7 pe rs on al ity tr ai t, se lf co ns ci ou sn es s, co lle ge st
ud en t Ps yc ho lo gy T he Su ic id e In te rv en tio n 12 2 18 35 su ic id e pr ev en tio n, m en ta lh ea lth ,p ub lic he al th Ps yc ho lo gy M en ta lH ea lth O fC hi ld re n 12 0 17 79 m en ta lh ea lth ,h ea lth ca re ,m en ta li lln es s Ps yc ho lo gy H ow D oe s C og ni tiv is m D iff er Fr om B eh av io ri sm ? 50 95 co gn iti ve sc ie nc e, in fo rm at io n pr oc es si ng ,c ul tu ra ld
iff er
en ce s Ps yc ho lo gy R ac is m ,B ia s, A nd D is cr im in at io n 81 68 4 af ri ca n am er ic an ,u ni te d st at es ,c iv il ri gh ts Ps yc ho lo gy D oe s G ro up Po la ri za tio n A ff ec tT he M in or ity ? 94 28 9 un ite d st at es ,a fr ic an am er ic an ,h ea lth ca re Ps yc ho lo gy Pe er Pr es su re O n A ca de m ic Pe rf or m an ce 93 79 9 hi gh sc ho ol ,p ee rg ro up ,p ee rp re ss ur e Ps yc ho lo gy W ha tI s T he R ol e O fC og ni tiv e Fl ex ib ili ty A nd In hi bi tio n In C om pl ex D yn am ic Ta sk s 10 1 74 4 w or ki ng m em or y, ex ec ut iv e fu nc tio n, fr on ta lc or te x Ps yc ho lo gy Se x D iff er en ce s In Fu nc tio na lC on ne ct iv ity B et w ee n R es tin g St at e B ra in N et w or ks In A ut is m Sp ec tr um D is or de r 13 3 24 77 au tis m sp ec tr um di so rd er ,m ag ne tic re so na nc e im ag in g, fu nc
tio na
lm ag
ne tic
re so
na nc
e im
ag in
g
E co
no m
ic s
G lo
ba liz
at io
n A
nd U
ne m
pl oy
m en
t 89
10 22
fin an
ci al
cr is
is ,e
co no
m ic
gr ow
th ,u
ni te
d st
at es
E co
no m
ic s
Su pp
ly C
ha in
M an
ag em
en t
84 10
23 ch
ai n
m an
ag em
en t,
su pp
ly ch
ai n
m an
ag em
en t,
su pp
ly ch
ai n
E co
no m
ic s
Vo la
til ity
A nd
T he
C ro
ss -S
ec tio
n O
fR ea
lE st
at e
E qu
ity R
et ur
ns D
ur in
g Pa
nd em
ic 58
42 6
re al
es ta
te in
ve st
m en
tt ru
st ,fi
na nc
ia lc
ri si
s, as
se ta
llo ca
tio n
E co
no m
ic s
H um
an C
ap ita
lA nd
C hi
na ’S
Fu tu
re G
ro w
th 91
12 50
ec on
om ic
gr ow
th ,h
um an
ca pi
ta l,
ec on
om ic
de ve
lo pm
en t
E co
no m
ic s
W ha
tC ri
tic al
A pp
ro ac
h To
N eo
cl as
si ca
lE co
no m
ic s
Is Su
pe ri
or ?
10 2
12 55
cl as
si ca
le co
no m
ic s,
ec on
om ic
th eo
ry ,e
co no
m ic
gr ow th E co no m ic s T he E co no m ic Po lic y U nc er ta in ty 11 4 12 18 ec on om ic gr ow th ,m on et ar y po lic y, un ite d st at es E co no m ic s U s E ar ni ng s In eq ua lit y 10 1 10 24 in co m e in eq ua lit y, un ite d st at es ,w ag e in eq ua lit y E co no m ic s W ha tI s T he A pp lic at io n O fF ix ed Po in tT he or y In Fi na nc ia lA nd E co no m ic Sc ie nc es ? 10 4 10 15 fin an ci al m ar ke t, ec on om ic th eo ry ,fi na nc ia lc ri si s E co no m ic s T he D ig ita lE co no m y 91 10 01 di gi ta le co no m y, di gi ta ld iv id e, ec on om ic gr ow th E co no m ic s H ow R at e H ik es C an E xa ce rb at e L ab or -M ar ke tI ne qu al ity 96 10 76 in co m e in eq ua lit y, un ite d st at es ,h um an ca pi ta l E co no m ic s W hy D o B uy er s Pa y D iff er en tP ri ce s Fo rC om pa ra bl e Pr od uc ts ? 74 20 2 su pp ly ch ai n, on lin e au ct io n, in fo rm at io n as ym m et ry E co no m ic s T he Im pa ct O fG lo ba liz at io n O n In co m e D is tr ib ut io n In E m er gi ng E co no m ie s 12 4 23 84 in co m e di st ri bu tio n, ec on om ic gr ow th ,d ev el op in g co un
tr ie
s
Ta bl
e 12
:S ta
tis tic
s of
qu er
ie s
an d
co rr
es po
nd in
g ev
ol vi
ng co
nc ep
tc o-
oc cu
rr en
ce gr
ap hs
in M
at he
m at
ic s,
H is
to ry
,P sy
ch ol
og y,
an d
E co
no m
ic s. D is
ci pl
in e
Q ue
ry N
um. o
fN od
es N
um. o
fE dg
es (2
02 1)
Se le
ct ed
C on
ce pt s So ci ol og y G en de rI ne qu al ity In A m er ic a 10 2 15 34 ge nd er eq ua lit y, la
tin am
er ic
a, ec
on om
ic gr
ow th
So ci
ol og
y A
bo rt
io n
A nd
A bo
rt io
n R
ig ht
s 95
11 74
un ite
d st
at es
,h ea
lth ca
re ,r
ep ro
du ct
iv e
ri gh ts So ci ol og y So ci al N et w or ks A dd ic tio n 77 74 2 so ci al ne tw or k, so ci al ne tw or ks ,s oc ia ls up po rt So ci ol og y D oe s A n Im pr ov em en tI n R ur al In fr as tr uc tu re C on tr ib ut e To A lle vi at e Po ve rt y In Pa ki st an ? 12 4 17 41 po ve rt y re du ct io n, de ve lo pi ng co un tr ie s, ec on om
ic gr
ow th
So ci
ol og
y Sp
re ad
O fF
al se
N ew
s 66
18 8
fa ls
e al
ar m
,s oc
ia lm
ed ia
,m ac
hi ne
le ar
ni ng
So ci
ol og
y R
eg io
na lI
de nt
ity A
nd R
eg io
na lC
ha ng
e 89
79 8
eu ro
pe an
un io
n, ea
st as
ia ,e
co no
m ic
de ve
lo pm
en t
So ci
ol og
y W
ha tI
s T
he Im
pa ct
O fI
nt er
ge ne
ra tio
na lM
ob ili
ty O
n W
el l-
B ei
ng In
Ja pa
n 92
50 5
un ite
d st
at es
,h um
an ca
pi ta
l, in
co m
e in
eq ua
lit y
So ci
ol og
y So
ci al
M ed
ia A
nd M
ar ke
tin g
10 0
13 25
so ci
al m
ed ia
,m ed
ia m
ar ke
t, so
ci al
m ed
ia m
ar ke
tin g
So ci
ol og
y C
an Po
pu lis
m C
on tr
ib ut
e To
A M
or e
In cl
us iv
e C
iti ze
ns hi
p? 97
78 5
hu m
an ri
gh ts
,u ni
te d
st at
es ,g
lo ba
lc iti
ze ns
hi p
So ci
ol og
y A
dv an
ce m
en tO
fW om
en In
Sc ie
nc e
11 3
96 3
un ite
d st
at es
,n at
io na
ls ci
en ce
fo un
da tio
n, ca
re er
ad va
nc em
en t
So ci
ol og
y H
ow A
re Fu
nc tio
na lis
m A
nd C
on fli
ct T
he or
y Si
m ila
r? 80
33 3
co nfl
ic tr
es ol
ut io
n, co
nfl ic
tm an
ag em
en t,
in te
rn at
io na
lr el
at io ns So ci ol og y H ow D o So ci al N et w or ks In flu en ce E du ca tio na lP ro ce ss es 11 2 11 19 so ci al ne tw or k, so ci al ne tw or ks ,s oc ia li nfl ue nc e A rt M od er n A es th et ic s 47 15 5 tr ad iti on al ch in es e, ch in es e tr ad iti on al ,w an g gu ow ei A rt E as te rn A si an A rt 11 7 11 74 ea st as ia ,s ou th as ia ,c en tr al as ia A rt Su rr ea lis tA es th et ic s 34 12 3 an dr e br et on ,t w en tie th ce nt ur y, w or ld w ar A rt M od er n R oc k M us ic Tr en d 88 95 6 po pu la rm us ic ,r oc k m us ic ,m us ic in du st ry A rt In te ra ct io n D es ig n 83 73 6 in te ra ct io n de si gn ,i nt er fa ce de si gn ,u se re xp er ie nc e A rt V ic to ri an B ea ut y St an da rd s In A rt 72 39 5 vi ct or ia n pe ri od ,v ic to ri an er a, vi ct or ia n en gl an d A rt A re C ul tu ra lly V ib ra nt C om m un iti es H ea lth ie r? 98 48 9 un ite d st at es ,p ub lic he al th ,c ul tu ra lh er ita ge A rt T he R ol e O fC ul tu ra lI de nt ity In T he C re at io n O fA rt 11 2 10 02 cu ltu ra li de nt ity ,c on te m po ra ry ar t, ca se st ud y A rt H ow A rt D ev el op s T he Pe rs on al ity O fH um an B ei ng s? 10 8 50 4 hu m an de ve lo pm en t, vi su al ar ts ,p er so na ld ev el op m en t A rt H ow C an G ra ffi ti B e A cc ep te d A s A Fo rm O fS tr ee tA rt A nd W hi ch A ttr ib ut es C an B e C on tr ib ut ed To A rc hi te ct ur e? 79 49 2 pu bl ic sp ac e, st re et ar t, gr af fit ia rt A rt T he U se O fA rt s In te rv en tio ns Fo rM en ta lH ea lth A nd W el lb ei ng In H ea lth Se tti ng s 14 0 16 95 m en ta lh ea lth ,m en ta li lln es s, pu bl ic he al th A rt W ha tI s T he D iff er en ce B et w ee n Is la m ic A rt A nd C hr is tia n A rt In Te rm s O fF un ct io n In T he M id dl e A ge s? 12 6 13 57 m id dl e ag e, m id dl e ea st ,i sl am ic ar t B us in es s E m pl oy ee M ot iv at io n 77 68 7 jo b sa tis fa ct io n, hu m an re so ur ce ,d ep en de nt va ri ab le B us in es s In te rn at io na lT ra de Tr en ds In T he U sa 11 3 14 17 de ve lo pi ng co un tr ie s, fr ee tr ad e, un ite d st at es B us in es s H ow B lo ck ch ai n A nd C ry pt oc ur re nc y C an R ev ol ut io ni ze B us in es s? 85 66 3 bu si ne ss m od el ,b us in es s pr oc es s, bl oc kc ha in te ch no lo gy B us in es s W ha tI s T he Im pa ct O fI nt er m ed ia ri es O n A N eg ot ia tio n? 82 32 0 un ite d st at es ,d ev el op in g co un tr ie s, eu ro pe an un io n B us in es s In te rn at io na lB us in es s, Fu rt he rG lo ba lis at io n O rB ac kl as h? 11 7 94 9 in te rn at io na lt ra de ,d ev el op in g co un tr ie s, bu si ne ss en vi ro nm en t B us in es s T he M ar ke tG ro w th Fo rE le ct ri c V eh ic le s 92 15 99 el ec tr ic ve hi cl e, hy br id el ec tr ic ve hi cl e, hy br id el ec tr ic B us in es s C ur re nt Tr en ds In C on su m er B eh av io r 74 33 0 lit er at ur e re vi ew ,s oc ia lm ed ia ,o nl in e sh op pi ng B us in es s W hy Is T he Im po rt an ce O fT he C or re la tio n A na ly si s B et w ee n T he St oc k M ar ke tV al ua tio n A nd T he E co no m ic Si tu at io n O fB us in es s E nt iti es G ro w in g? 10 0 14 22 st oc k m ar ke t, fin an ci al m ar ke t, ca pi ta lm ar ke t B us in es s H ow To E va lu at e C os tI m pa ct s O n R ev er se L og is tic s U si ng A n E co no m ic O rd er Q ua nt ity (E oq )M od el W ith E nv ir on m en ta lA nd So ci al C on si de ra tio ns 61 33 0 su pp ly ch ai n m an ag em en t, su st ai na bl e de ve lo pm en t, ca rb on
em is
si on
B us
in es
s G
lo ba
lU ne
m pl
oy m
en t
94 10
85 ec
on om
ic gr
ow th
,u ni
te d
st at
es ,u
ne m
pl oy
m en
ti ns
ur an ce B us in es s C ap ita lis m A nd M ul tin at io na lC om pa ni es 80 97 5 m ul tin at io na lc or po ra tio n, fo re ig n di re ct in ve st m en t, hu m
an ca
pi ta l B us in es s N ex tF in an ci al C ri si s 98 18 97 gl ob al fin an ci al cr is is ,fi na nc ia lm ar ke t, fin an ci al cr is es Ph ys ic s M ic ro flu id ic s A nd M ic ro sy st em s 99 55 4 ce ll cu ltu re ,i nt eg ra te d ci rc ui t, sa m pl e pr ep ar at io n Ph ys ic s H ow Sw ar m R ob ot ic s C an B e U se d To D es cr ib e Pa rt ic le Sy st em ’S D ef or m at io n 66 47 4 pa rt ic le sw ar m op tim iz at io n, m ob ile ro bo t, sw ar m in te lli ge nc e Ph ys ic s D ar k M at te r 81 12 08 da rk m at te r, di re ct de te ct io n, st an da rd m od el Ph ys ic s G al ax y Fo rm at io n A nd E vo lu tio n 91 16 71 st ar fo rm at io n, ga la xy ev ol ut io n, ga la xy fo rm at io n Ph ys ic s B ig B an g (Q ua nt um )C os m ol og y 70 10 92 bi g ba ng ,c os m ol og ic al co ns ta nt ,d ar k m at te r Ph ys ic s G lo ba lN on lin ea rS ta bi lit y O fL ar ge D is pe rs iv e So lu tio ns To T he E in st ei n E qu at io ns 55 31 8 di ff er en tia le qu at io n, ca uc hy pr ob le m ,p ar tia ld iff er en tia le qu at io ns Ph ys ic s H ow D oe s T he M ag ne to re si st an ce R efl ec tT he In fo rm at io n O fF er m iS ur fa ce ? 55 56 4 fe rm is ur fa ce ,m ag ne tic fie ld ,f er m il ev el Ph ys ic s C on de ns ed M at te rP hy si cs A nd A co us tic s 92 11 56 co nd en se d m at te r, co nd en se d m at te rp hy si cs ,p ha se tr an si tio n Ph ys ic s T he D ev el op m en ts In Q ua nt um C om pu te rs 92 12 48 qu an tu m co m pu tin g, qu an tu m co m pu te rs ,q ua nt um co m pu ta tio n Ph ys ic s O pt ic al Ph ys ic s A nd Q ua nt um In fo rm at io n Sc ie nc e 11 3 22 16 qu an tu m in fo rm at io n, qu an tu m m ec ha ni cs ,q ua nt um ph ys ic s Ph ys ic s T he Sp ac eTi m e G eo m et ry B eh in d T he C on st an tS pe ed O fL ig ht 11 8 18 26 ge ne ra lr el at iv ity ,s pe ci al re la tiv ity ,c os m ol og ic al co ns ta nt Ph ys ic s A nt if er ro m ag ne tic Sp in tr on ic 55 47 0 m ag ne tic fie ld ,m ag ne tic m om en t, gr ou nd st at e
Ta bl
e 13
:S ta
tis tic
s of
qu er
ie s
an d
co rr
es po
nd in
g ev
ol vi
ng co
nc ep
tc o-
oc cu
rr en
ce gr
ap hs
in So
ci ol
og y,
A rt
,B us
in es
s, an
d Ph
ys ic
s. D is
ci pl
in e
Q ue
ry N
um. o
fN od
es N
um. o
fE dg
es (2
02 1)
Se le
ct ed
C on
ce pt s Po lit ic al Sc ie nc e Po lit ic s A nd D ip lo m ac y 94 12 61 fo re ig n po lic y, pu
bl ic
di pl
om ac
y, un
ite d
st at es Po lit ic al Sc ie nc e D em oc ra cy A nd T he Pu bl ic In T he E ur op ea n U ni on 13 2 24 06 eu ro pe an un io n, m em be rs ta te ,e ur op ea n in te
gr at
io n
Po lit
ic al
Sc ie
nc e
W ha
tI s
T he
M os
tP ow
er fu
lA ct
O fP
ol iti
ca lP
ar tic
ip at
io n?
10 1
79 5
un ite
d st
at es
,p ol
iti ca
lp ar
tie s,
ci vi
ls oc
ie ty
Po lit
ic al
Sc ie
nc e
R ur
al R
ev ita
liz at
io n
10 8
58 3
ec on
om ic
de ve
lo pm
en t,
su st
ai na
bl e
de ve
lo pm
en t,
un ite
d st
at es
Po lit
ic al
Sc ie
nc e
O pp
or tu
ni tie
s A
nd C
ha lle
ng es
Fa ci
ng C
hi na
’S E
co no
m ic
“E xt
er na
lC ir
cu la
tio n”
94 78
8 ec
on om
ic de
ve lo
pm en
t, ec
on om
ic gr
ow th
,s us
ta in
ab le
de ve
lo pm
en t
Po lit
ic al
Sc ie
nc e
H ow
Po lit
ic ia
ns U
se So
ci al
M ed
ia ?
79 89
1 so
ci al
m ed
ia ,s
oc ia
ln et
w or
k, m
as s
m ed ia Po lit ic al Sc ie nc e T he B as ic s O fT he T he or et ic al Sy st em O fS oc ia lis m W ith C hi ne se C ha ra ct er is tic s 83 57 6 ch in es e ch ar ac te r, co m m un is tp ar ty ,d en g
xi ao
pi ng
Po lit
ic al
Sc ie
nc e
H ow
Po lit
ic al
O ri
en ta
tio n,
E co
no m
ic Pr
ec ar
ity ,A
nd Pa
rt ic
ip an
tD em
og ra
ph ic
s Im
pa ct
C om
pl ia
nc e
W ith
C O
V ID
-1 9
Pr ev
en tio
n M
ea su
re s
In A
D ut
ch R
ep re
se nt
at iv
e Sa
m pl
e? 11
9 73
7 ec
on om
ic de
ve lo
pm en
t, un
ite d
st at
es ,e
co no
m ic
gr ow
th
Po lit
ic al
Sc ie
nc e
C hi
ne se
C om
m un
is tP
ar ty
H ie
ra rc
hy 67
50 9
ch in
es e
co m
m un
is tp
ar ty
,c om
m un
is tp
ar ty
,c hi
ne se
co m
m un
is t
Po lit
ic al
Sc ie
nc e
L an
d Sy
st em
R ef
or m
at io
n 74
68 1
la nd
re fo
rm ,l
an d
te nu
re ,a
gr ar
ia n
re fo rm Po lit ic al Sc ie nc e V ie tn am W ar In te re st s A gg re ga tio n 11 6 20 16 vi et na m w ar ,u ni te d st at es ,s ou th vi et na m Po lit ic al Sc ie nc e R us si a A nd N at o R el at io ns hi ps 11 3 26 71 co ld w ar ,u ni te d st at es ,n or th at la nt ic Ph ilo so ph y Se lf C on sc io us ne ss 52 18 3 al te re d st at es ,g lo ba lw or ks pa ce th eo ry ,v
eg et
at iv
e st
at e
Ph ilo
so ph
y W
hy D
o W
e St
riv e
Fo rP
er fe
ct io
n If
It Is
N ot
A tta
in ab
le ?
26 74
hi gh
er ed
uc at
io n,
un ite
d st
at es
,h um
an ca
pi ta l Ph ilo so ph y D ea lis tic U nd er st an di ng O fE xi st en ce 41 68 co nc ep tu al m od el ,c on ce pt ua lf ra m ew or k,
ne ur
al ne
tw or k Ph ilo so ph y Ph ilo so ph ic al A nt hr op ol og y 49 25 2 ph ilo so ph ic al an th ro po lo gy ,c ul tu ra la nt hr op ol og y, m ed ic
al an
th ro
po lo gy Ph ilo so ph y R es ea rc h O n G ad am er ’S Ph ilo so ph y 36 17 8 so ci al sc ie nc e, w es te rn ph ilo so ph y, ch in es e ph ilo so ph y Ph ilo so ph y M et ap ro be s, M et ap hy si ca lA nd Sk et ch y Ph ilo so ph y 59 31 9 w es te rn ph ilo so ph y, m od er n ph ilo so ph y, gr ee k ph ilo so ph y Ph ilo so ph y W hy Is B ea ut y A ss oc ia te d W ith M or al ity ? 55 17 9 m or al ph ilo so ph y, hu m an na tu re ,u ni ve rs ity pr es s Ph ilo so ph y A po ca ly ps e A nd T he E nd s O fT he W or ld 50 20 5 un ite d st at es ,w or ld w ar ii, co ld w ar Ph ilo so ph y C ul tu ra lG en es is A nd D yn am ic s O fC ul tu re 68 29 5 cu ltu ra ld iv er si ty ,c ul tu ra li de nt ity ,n at io na lc ul tu re Ph ilo so ph y Is C on fu ci an is m A R el ig io us Ph ilo so ph y O rE th ic s 86 61 2 ch in es e ph ilo so ph y, m or al ph ilo so ph y, hu m an na tu re Ph ilo so ph y W he re D oe s Y ou rS el fW or th C om e Fr om ? 65 28 9 m en ta lh ea lth ,h ig h sc ho ol ,b od y im ag e Ph ilo so ph y W he re D o Y ou Fi nd M ea ni ng In Y ou rL if e? 54 23 8 m en ta lh ea lth ,c ol le ge st ud en ts ,r eg re ss io n an al ys is B io lo gy E nd an ge re d Sp ec ie s R ec ov er y 77 61 5 en da ng er ed sp ec ie s, un ite d st at es ,c ri tic al ly en da ng er ed B io lo gy G en e M od ifi ca tio n A nd D is ea se 13 1 17 91 ge ne ex pr es si on ,h is to ne m od ifi ca tio n, ge ne th er ap y B io lo gy C ri sp rA nd G en et ic E ng in ee ri ng 79 56 9 ge ne tic en gi ne er in g, ge ne tic al ly en gi ne er ed ,g en et ic al ly m od ifi ed B io lo gy T he E ff ec tO fP la nt G en om e E di tin g 91 10 59 ar ab id op si s th al ia na ,g en e ex pr es si on ,r na ed iti ng B io lo gy B io sy nt he si s, Tr an sp or tA nd B io lo gi ca lF un ct io ns O fA sc or bi c A ci d In Pl an ts. 13 0 15 37 as co rb ic ac id ,a m in o ac id ,a ra bi do ps is th al ia na B io lo gy H ow To E st im at e Fe m al e M al ar ia M os qu ito A ge B y Q ua nt if yi ng Y -L in ke d G en es In St or ed M al e Sp er m at oz oa 11 4 77 3 pu bl ic he al th ,a ed es ae gy pt i, ye llo w fe ve r B io lo gy E vo lu tio n O fT er re st ri al Pl an t 94 50 2 cl im at e ch an ge ,f os si lr ec or d, ca rb on di ox id e B io lo gy T he E ff ec tO fS yn er gi st ic In te ra ct io n B et w ee n E ar th w or m s A nd M ic ro or ga ni sm s O n T he C om po st in g Pr oc es s. 95 63 8 so lid w as te ,s ew ag e sl ud ge ,h ea vy m et al B io lo gy D ev el op m en tO fA Fi lte rD ev ic e Fo rT he Pr ev en tio n O fA qu at ic B ac te ri al D is ea se U si ng A Si ng le -C ha in V ar ia bl e Fr ag m en t( Sc fv )- C on ju ga te d A ffi ni ty Si lk 12 1 78 0 w es te rn bl ot ,p ol ym er as e ch ai n re ac tio n, flo w cy to m et ry B io lo gy H um an C dn a C lo ne s 10 3 84 8 op en re ad in g fr am e, po ly m er as e ch ai n re ac tio n, ce ll lin e B io lo gy In te ra ct io ns B et w ee n G en es 13 1 95 9 si ng le nu cl eo tid e po ly m or ph is m ,l og is tic re gr es si on ,d im en si on al ity re
du ct
io n
B io
lo gy
A gi
ng ,L
if es
pa n
A nd
M et
ab ol
is m
13 1
22 19
ox id
at iv
e st
re ss
,g en
e ex
pr es
si on
,d ie
ta ry
re st
ri ct
io n
M ed
ic in
e D
ev el
op m
en tO
fX en
ot ra
ns pl
an ta
tio n
64 46
5 st
em ce
ll, cl
in ic
al tr
ia ls
,c el
ll in e M ed ic in e N eu ro na lR eg en er at io n 12 7 19 62 sp in al co rd ,d or sa lr oo t, st em
ce ll
M ed
ic in
e T
he R
ol e
O fV
ita m
in D
In T
he Pa
th og
en es
is O
fA lle
rg ic
R hi
ni tis
11 7
10 84
m as
tc el
l, im
m un
e re
sp on
se ,a
to pi
c de
rm at
iti s
M ed
ic in
e Tu
m or
Im m
un ity
A nd
Ta rg
et ed
T he
ra py
11 9
29 65
im m
un e
re sp
on se
,t um
or m
ic ro
en vi
ro nm
en t,
de nd
ri tic
ce ll
M ed
ic in
e E
ar ly
D et
ec tio
n O
fC an
ce r
10 9
10 19
br ea
st ca
nc er
,c ol
or ec
ta lc
an ce
r, pr
os ta
te ca
nc er
M ed
ic in
e Is
M ed
ic al
R es
ea rc
h O
n A
ni m
al s
E th
ic al
10 8
16 42
m ed
ic al
et hi
cs ,r
es ea
rc h
et hi
cs ,e
th ic
s co
m m
itt ee
M ed
ic in
e A
ss oc
ia tio
n B
et w
ee n
M ig
ra in
e A
nd R
is k
O fO
cu la
rM ot
or C
ra ni
al N
er ve
Pa ls
y 12
7 22
27 cr
an ia
ln er
ve ,c
as e
re po
rt ,m
ag ne
tic re
so na
nc e
im ag
in g
M ed
ic in
e In
te st
in al
Fl or
a C
or re
la te
s W
ith C
hr on
ic L
iv er
D is
ea se
17 6
27 98
al co
ho lic
liv er
di se
as e,
no na
lc oh
ol ic
fa tty
liv er
di se
as e,
he pa
to ce
llu la
rc ar
ci no
m a
M ed
ic in
e Tr
ea tm
en tO
fA lz
he im
er ’S
D is
ea se
12 8
11 46
cl in
ic al
tr ia
ls ,c
og ni
tiv e
fu nc
tio n,
an im
al m
od el
M ed
ic in
e M
ed ic
al H
um an
ita ri
an M
is si
on s
In T
he D
ev el
op in
g W
or ld
11 3
17 90
hu m
an ita
ri an
as si
st an
ce ,h
um an
ita ri
an ai
d, un
ite d
na tio ns M ed ic in e A rt ifi ci al In te lli ge nc e In V ac ci ne A nd D ru g D es ig n 99 10 58 ar tifi ci al in te lli ge nc e, ne ur al ne tw or k, m ac hi ne le ar ni ng M ed ic in e W ha tT he E ff ec tO fW ei gh tb ea ri ng A nd Fo ot Po si tio ni ng O n 3D D is ta lT ib io fib ul ar Jo in tP ar am et er s? 67 38 5 so ft tis su e, co m pu te d to m og ra ph y, sa gi tta lp la ne
Ta bl
e 14
:S ta
tis tic
s of
qu er
ie s
an d
co rr
es po
nd in
g ev
ol vi
ng co
nc ep
tc o-
oc cu
rr en
ce gr
ap hs
in Po
lit ic
al Sc
ie nc
e, Ph
ilo so
ph y,
B io
lo gy
,a nd
M ed
ic in
e. D is
ci pl
in e
Q ue
ry N
um. o
fN od
es N
um. o
fE dg
es (2
02 1)
Se le
ct ed
C on
ce pt s M at er ia ls Sc ie nc e In te rc on ve rs io n O fM ul tif er ro ic D om ai ns A nd D om ai n W al ls 55 32 0 el ec tr ic fie ld ,m ag ne
tic fie
ld s,
ro om
te m
pe ra
tu re
M at
er ia
ls Sc
ie nc
e To
po lo
gi ca
lI ns
ul at
or s
49 26
0 m
ag ne
tic fie
ld ,h
al le
ff ec
t, ba
nd st
ru ct
ur e
M at
er ia
ls Sc
ie nc
e Pr
og ra
m m
ab le
M at
te r
93 23
4 so
ut h
af ri
ca ,h
ig he
re du
ca tio
n, he
al th
ca re
M at
er ia
ls Sc
ie nc
e Se
le ct
iv e
L as
er Si
nt er
in g
75 48
6 ad
di tiv
e m
an uf
ac tu
ri ng
,r ap
id pr
ot ot
yp in
g, sc
an ni
ng el
ec tr
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sc op y M at er ia ls Sc ie nc e Pe ro vs ki te Fe rr oe le ct ri c M at er ia l 60 81 3 ph as e tr an si tio n, ro om te m pe ra tu re ,e le ct ri c fie ld M at er ia ls Sc ie nc e H ig hTe m pe ra tu re Su pe rc on du ct iv ity 93 98 1 cr iti ca lt em pe ra tu re ,m ag ne tic fie ld ,c ur re nt de ns ity M at er ia ls Sc ie nc e E le ct ro m ag ne tic W av e A bs or bi ng M at er ia l 70 60 0 el ec tr om ag ne tic w av e, el ec tr om ag ne tic in te rf er en ce ,e le ct ro m ag ne tic fie ld M at er ia ls Sc ie nc e U nl ea de d E ne rg y St or ag e C er am ic s 63 46 3 en er gy st or ag e, re ne w ab le en er gy ,e ne rg y de ns ity M at er ia ls Sc ie nc e Pr ep ar at io n O fC om po si te St ru ct ur es O fT ita ni um D io xi de N an ot ub e A rr ay s. 12 3 18 76 el ec tr on m ic ro sc op y, sc an ni ng el ec tr on m ic ro sc op y, tit an iu m ox id e M at er ia ls Sc ie nc e E ff ec tO fT he G ra in A rr an ge m en ts O n T he T he rm al St ab ili ty O fP ol yc ry st al lin e N ic ke lR ic h L ith iu m -B as ed B at te ry C at ho de s 94 15 66 lit hi um io n, th er m al st ab ili ty ,e ne rg y de ns ity M at er ia ls Sc ie nc e E ry th ri to lP ha se C ha ng e T he rm al St or ag e Su bc oo lin g A nd T he rm al C on du ct iv ity Im pr ov em en t 95 18 39 th er m al co nd uc tiv ity ,p ha se ch an ge ,t he rm al en er gy st or ag e M at er ia ls Sc ie nc e T he D ev el op m en tO fN an om at er ia ls 89 59 5 ca rb on na no tu be ,q ua nt um do t, m et al ox id e E nv ir on m en ta lS ci en ce N oi se A nd L ig ht Po llu tio n 85 66 4 no is e po llu tio n, ai rp ol lu tio n, en vi ro nm en ta ln oi se E nv ir on m en ta lS ci en ce M ic ro pl as tic Im pa ct s O n E co sy st em 12 0 10 29 fo od w eb ,c lim at e ch an ge ,t ro ph ic le ve l E nv ir on m en ta lS ci en ce R em ov al O fR ef ra ct or y O rg an ic Po llu ta nt s 84 86 6 or ga ni c co m po un ds ,w as te w at er tr ea tm en t, or ga ni c m at te r E nv ir on m en ta lS ci en ce R em ot e Se ns in g A nd G eo gr ap hi c In fo rm at io n Sy st em s 11 2 84 5 re m ot e se ns in g, im ag e pr oc es si ng ,c ha ng e de te ct io n E nv ir on m en ta lS ci en ce In flu en ce O fH yd ro dy na m ic s O n N ut ri en tC yc lin g A nd A lg al G ro w th In Ta ih u L ak e 97 90 7 w at er qu al ity ,m ei lia ng ba y, or ga ni c m at te r E nv ir on m en ta lS ci en ce A tm os ph er ic E nv ir on m en ta lC ap ac ity A cc ou nt in g A nd To ta lP ol lu ta nt C on tr ol 10 6 12 52 ai rp ol lu tio n, ai rq ua lit y, ai rp ol lu ta nt s E nv ir on m en ta lS ci en ce E nv ir on m en ta lS ci en ce A nd Su st ai na bl e D ev el op m en t 13 5 26 48 su st ai na bl e de ve lo pm en t, en vi ro nm en ta ls us ta in ab ili ty ,e nv ir on m en ta ls
ci en ce E nv ir on m en ta lS ci en ce G en er at io n A nd D ir ec tO bs er va tio n O fA Tr ip le tA ry ln itr en iu m Io n 69 51 0 ex ci te d st at e, gr ou nd st at e, el ec tr on tr an sf er E nv ir on m en ta lS ci en ce W ha tA re T he G oa ls O fE nv ir on m en ta lS ci en ce St ud ie s? 12 6 18 94 en vi ro nm en ta ls ci en ce ,c as e st ud y, su st ai na bl e de ve lo pm en t E nv ir on m en ta lS ci en ce W hy C he m is ts C an ’T Q ui tP al la di um 51 21 6 ro om te m pe ra tu re ,o rg an ic sy nt he si s, tr an si tio n m et al E nv ir on m en ta lS ci en ce G lo ba lW ar m in g A nd C lim at e C ha ng e 14 1 22 87 gl ob al w ar m in g, gr ee nh ou se ga s, ca rb on di ox id e E nv ir on m en ta lS ci en ce M er cu ry Po llu tio n E lim in at io n 10 9 82 3 he av y m et al ,a ir po llu tio n, fo od ch ai n C he m is tr y Ph ot oe le ct ro ch em ic al B io se ns or 10 2 85 3 su rf ac e pl as m on re so na nc e, qu an tu m do t, gl uc os e ox id as e C he m is tr y A xi al C hi ra lC om po un ds 48 31 4 op tic al ly ac tiv e, am in o ac id ,c ir cu la rd ic hr oi sm C he m is tr y O rg an ic C he m is tr y A nd D is co ve ry 73 89 2 or ga ni c ch em is t, dr ug di sc ov er y, or ga ni c sy nt he si s C he m is tr y N itr og en H et er oc yc lic C ar be ne C at al ys is 44 28 6 tr an si tio n m et al ,h om og en eo us ca ta ly si s, ro om te m pe ra tu re C he m is tr y N on co va le nt In te ra ct io n 89 10 87 hy dr og en bo nd ,h yd ro ge n bo nd in g, de ns ity fu nc tio na l C he m is tr y H yd ro ph ob ic E ff ec tP he no m en on 11 7 94 7 co nt ac ta ng le ,a qu eo us so lu tio n, hy dr og en bo nd C he m is tr y C ol lo id T he or y 84 68 8 io ni c st re ng th ,l ig ht sc at te ri ng ,p or ou s m ed ia C he m is tr y H ow To M ak e A Fr ui tie r, M or e Fl or al C ho co la te 74 30 8 co co a bu tte r, fa tty ac id ,c oc oa be an C he m is tr y O rg an ic C he m ic al R ea ct iv ity Fu nc tio ni ng 85 74 3 fu nc tio na lg ro up ,o rg an ic co m po un d, de ns ity fu nc tio na lt he or y C he m is tr y W hy D o Tr an si tio n C ry st al s (H yb ri d C ry st al s) C on du ct E le ct ri ci ty ? 82 10 84 el ec tr ic al co nd uc tiv ity ,s in gl e cr ys ta l, ph as e tr an si tio n C he m is tr y C an E le ct ri c Fi el ds D riv e C he m is tr y Fo rA n A qu eo us M ic ro dr op le t? 88 43 2 el ec tr ic fie ld ,a qu eo us so lu tio n, m ag ne tic fie ld C he m is tr y W ha tA re T he D ow ns tr ea m Pr od uc ts G en er at ed Fr om C oa l? 10 5 10 88 ca rb on di ox id e, na tu ra lg as ,b itu m in ou s co al E ng in ee ri ng Fl ex ib le Su rg ic al R ob ot 64 26 7 su rg ic al in st ru m en t, da vi nc i, la pa ro sc op ic su rg er y E ng in ee ri ng In se ct L ik e M ic ro A ir V eh ic le 71 32 8 w in d tu nn el ,a ir po llu tio n, el ec tr ic ve hi cl es E ng in ee ri ng T he U se O fA iA nd M ac hi ne L ea rn in g In E ng in ee ri ng 12 4 20 09 ar tifi ci al in te lli ge nc e, m ac hi ne le ar ni ng ,n eu ra ln et w or k E ng in ee ri ng C iv il E ng in ee ri ng 85 44 8 ci vi le ng in ee r, st ru ct ur al en gi ne er in g, en vi ro nm en ta le ng in ee ri ng E ng in ee ri ng A er od yn am ic s A nd Fl ui d M ec ha ni cs 74 94 9 co m pu ta tio na lfl ui d dy na m ic s, flu id dy na m ic s, w in d tu nn el E ng in ee ri ng C ro w ds ou rc in g In So ft w ar e E ng in ee ri ng 10 1 10 98 so ft w ar e en gi ne er ,s of tw ar e de ve lo pm en t, re qu ir em en ts en gi ne er in g E ng in ee ri ng T he E ff ec tO fS tr es s R el ea se O n T he St ab ili ty O fE xc av at io n W or ks 34 13 0 po w er st at io n, fin ite el em en t, st re ss co nc en tr at io n E ng in ee ri ng Sp ec ifi cs O fE ng in ee ri ng M at er ia ls 10 3 55 7 co m po si te m at er ia l, m at er ia ls sc ie nc e, ci vi le ng in ee ri ng E ng in ee ri ng R is k C au se d B y T he Pr op ag at io n O fE ar th qu ak e L os se s T hr ou gh T he E co no m y 87 63 3 ri sk as se ss m en t, ri sk m an ag em en t, se is m ic ha za rd E ng in ee ri ng H ea tT ra ns fe rI n L ow Te m pe ra tu re 89 14 46 he at tr an sf er ,h ea tt ra ns fe rc oe ffi ci en t, he at flu x E ng in ee ri ng E ff ec tO fP is to n St ru ct ur al St iff ne ss O n D yn am ic Pe rf or m an ce 60 25 9 fin ite el em en t, fin ite el em en ta na ly si s, fin ite el em en tm et ho d E ng in ee ri ng E ff ec ts O fT hi ck ne ss R ed uc tio n In C ol d R ol lin g Pr oc es s O n T he Fo rm ab ili ty O fS he et M et al s U si ng A nfi s 70 67 3 ro om te m pe ra tu re ,g ra in si ze ,h ea tt re at m en t
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