ArticleiScience2024
Semantic similarity is not enough: A novel NLP-based semantic similarity measure in geospatial context.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- GeoAI: Beyond mapping earth and cities through explainability, adaptability, and sustainability.iScience · 2026Article
- Consistent semantic representation learning for out-of-distribution molecular property prediction.Briefings in bioinformatics · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we addressed two primary challenges: firstly, the issue of domain shift, which pertains to changes in data characteristics or context that can impact model performance, and secondly, the discrepancy between semantic similarity and geographical distance. We employed topic modeling in conjunction with the BERT architecture. Our model was crafted to enhance similarity computations applied to geospatial text, aiming to integrate both semantic similarity and geographical proximity. We tested the model on two datasets, Persian Wikipedia articles and rental property advertisements. The findings demonstrate that the model effectively improved the correlation between semantic similarity and geographical distance. Furthermore, evaluation by real-world users within a recommender system context revealed a notable increase in user satisfaction by approximately 22% for Wikipedia articles and 56% for advertisements.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.