Discovering emergent connections in quantum physics research via dynamic word embeddings
As the field of quantum physics evolves, researchers naturally form subgroups focusing on specialized problems. While this encourages in-depth exploration, it can limit the exchange of ideas across structurally similar problems in different subfields. To encourage cross-talk among these different sp...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adb00a |
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| author | Felix Frohnert Xuemei Gu Mario Krenn Evert van Nieuwenburg |
| author_facet | Felix Frohnert Xuemei Gu Mario Krenn Evert van Nieuwenburg |
| author_sort | Felix Frohnert |
| collection | DOAJ |
| description | As the field of quantum physics evolves, researchers naturally form subgroups focusing on specialized problems. While this encourages in-depth exploration, it can limit the exchange of ideas across structurally similar problems in different subfields. To encourage cross-talk among these different specialized areas, data-driven approaches using machine learning have recently shown promise to uncover meaningful connections between research concepts, promoting cross-disciplinary innovation. Current state-of-the-art approaches represent concepts using knowledge graphs and frame the task as a link prediction problem, where connections between concepts are explicitly modeled. In this work, we introduce a novel approach based on dynamic word embeddings for concept combination prediction. Unlike knowledge graphs, our method captures implicit relationships between concepts, can be learned in a fully unsupervised manner, and encodes a broader spectrum of information. We demonstrate that this representation enables accurate predictions about the co-occurrence of concepts within research abstracts over time. To validate the effectiveness of our approach, we provide a comprehensive benchmark against existing methods and offer insights into the interpretability of these embeddings, particularly in the context of quantum physics research. Our findings suggest that this representation offers a more flexible and informative way of modeling conceptual relationships in scientific literature. |
| format | Article |
| id | doaj-art-121aa296eaf342c781ab3ee76d648340 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-121aa296eaf342c781ab3ee76d6483402025-02-07T13:03:01ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016101502910.1088/2632-2153/adb00aDiscovering emergent connections in quantum physics research via dynamic word embeddingsFelix Frohnert0https://orcid.org/0000-0003-3717-6352Xuemei Gu1https://orcid.org/0000-0002-0734-432XMario Krenn2https://orcid.org/0000-0003-1620-9207Evert van Nieuwenburg3〈 a Q aL 〉 Applied Quantum Algorithms, Universiteit Leiden , Leiden, The NetherlandsMax Planck Institute for the Science of Light , Erlangen, GermanyMax Planck Institute for the Science of Light , Erlangen, Germany〈 a Q aL 〉 Applied Quantum Algorithms, Universiteit Leiden , Leiden, The NetherlandsAs the field of quantum physics evolves, researchers naturally form subgroups focusing on specialized problems. While this encourages in-depth exploration, it can limit the exchange of ideas across structurally similar problems in different subfields. To encourage cross-talk among these different specialized areas, data-driven approaches using machine learning have recently shown promise to uncover meaningful connections between research concepts, promoting cross-disciplinary innovation. Current state-of-the-art approaches represent concepts using knowledge graphs and frame the task as a link prediction problem, where connections between concepts are explicitly modeled. In this work, we introduce a novel approach based on dynamic word embeddings for concept combination prediction. Unlike knowledge graphs, our method captures implicit relationships between concepts, can be learned in a fully unsupervised manner, and encodes a broader spectrum of information. We demonstrate that this representation enables accurate predictions about the co-occurrence of concepts within research abstracts over time. To validate the effectiveness of our approach, we provide a comprehensive benchmark against existing methods and offer insights into the interpretability of these embeddings, particularly in the context of quantum physics research. Our findings suggest that this representation offers a more flexible and informative way of modeling conceptual relationships in scientific literature.https://doi.org/10.1088/2632-2153/adb00adynamic word embeddingquantum physicsforecasting research trajectories |
| spellingShingle | Felix Frohnert Xuemei Gu Mario Krenn Evert van Nieuwenburg Discovering emergent connections in quantum physics research via dynamic word embeddings Machine Learning: Science and Technology dynamic word embedding quantum physics forecasting research trajectories |
| title | Discovering emergent connections in quantum physics research via dynamic word embeddings |
| title_full | Discovering emergent connections in quantum physics research via dynamic word embeddings |
| title_fullStr | Discovering emergent connections in quantum physics research via dynamic word embeddings |
| title_full_unstemmed | Discovering emergent connections in quantum physics research via dynamic word embeddings |
| title_short | Discovering emergent connections in quantum physics research via dynamic word embeddings |
| title_sort | discovering emergent connections in quantum physics research via dynamic word embeddings |
| topic | dynamic word embedding quantum physics forecasting research trajectories |
| url | https://doi.org/10.1088/2632-2153/adb00a |
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