Predicting New Concept-Object Associations in Astronomy by Mining the Literature
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arXiv
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| Format: | Preprint |
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2026
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| author | Li, Jinchu Ting, Yuan-Sen Accomazzi, Alberto Ghosal, Tirthankar Ramachandra, Nesar |
| author_facet | Li, Jinchu Ting, Yuan-Sen Accomazzi, Alberto Ghosal, Tirthankar Ramachandra, Nesar |
| contents | We construct a concept-object knowledge graph from the full astro-ph corpus through July 2025. Using an automated pipeline, we extract named astrophysical objects from OCR-processed papers, resolve them to SIMBAD identifiers, and link them to scientific concepts annotated in the source corpus. We then test whether historical graph structure can forecast new concept-object associations before they appear in print. Because the concepts are derived from clustering and therefore overlap semantically, we apply an inference-time concept-similarity smoothing step uniformly to all methods. Across four temporal cutoffs on a physically meaningful subset of concepts, an implicit-feedback matrix factorization model (alternating least squares, ALS) with smoothing outperforms the strongest neighborhood baseline (KNN using text-embedding concept similarity) by 16.8% on NDCG@100 (0.144 vs 0.123) and 19.8% on Recall@100 (0.175 vs 0.146), and exceeds the best recency heuristic by 96% and 88%, respectively. These results indicate that historical literature encodes predictive structure not captured by global heuristics or local neighborhood voting, suggesting a path toward tools that could help triage follow-up targets for scarce telescope time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14335 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Predicting New Concept-Object Associations in Astronomy by Mining the Literature Li, Jinchu Ting, Yuan-Sen Accomazzi, Alberto Ghosal, Tirthankar Ramachandra, Nesar Instrumentation and Methods for Astrophysics Information Retrieval We construct a concept-object knowledge graph from the full astro-ph corpus through July 2025. Using an automated pipeline, we extract named astrophysical objects from OCR-processed papers, resolve them to SIMBAD identifiers, and link them to scientific concepts annotated in the source corpus. We then test whether historical graph structure can forecast new concept-object associations before they appear in print. Because the concepts are derived from clustering and therefore overlap semantically, we apply an inference-time concept-similarity smoothing step uniformly to all methods. Across four temporal cutoffs on a physically meaningful subset of concepts, an implicit-feedback matrix factorization model (alternating least squares, ALS) with smoothing outperforms the strongest neighborhood baseline (KNN using text-embedding concept similarity) by 16.8% on NDCG@100 (0.144 vs 0.123) and 19.8% on Recall@100 (0.175 vs 0.146), and exceeds the best recency heuristic by 96% and 88%, respectively. These results indicate that historical literature encodes predictive structure not captured by global heuristics or local neighborhood voting, suggesting a path toward tools that could help triage follow-up targets for scarce telescope time. |
| title | Predicting New Concept-Object Associations in Astronomy by Mining the Literature |
| topic | Instrumentation and Methods for Astrophysics Information Retrieval |
| url | https://arxiv.org/abs/2602.14335 |