Multimodal Search in Chemical Documents and Reactions
February 24, 2025 Β· Declared Dead Β· π Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ayush Kumar Shah, Abhisek Dey, Leo Luo, Bryan Amador, Patrick Philippy, Ming Zhong, Siru Ouyang, David Mark Friday, David Bianchi, Nick Jackson, Richard Zanibbi, Jiawei Han
arXiv ID
2502.16865
Category
cs.IR: Information Retrieval
Citations
0
Venue
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
Last Checked
4 months ago
Abstract
We present a multimodal search tool that facilitates retrieval of chemical reactions, molecular structures, and associated text from scientific literature. Queries may combine molecular diagrams, textual descriptions, and reaction data, allowing users to connect different representations of chemical information. To support this, the indexing process includes chemical diagram extraction and parsing, extraction of reaction data from text in tabular form, and cross-modal linking of diagrams and their mentions in text. We describe the system's architecture, key functionalities, and retrieval process, along with expert assessments of the system. This demo highlights the workflow and technical components of the search system.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Information Retrieval
R.I.P.
π»
Ghosted
π
π
Old Age
Neural Graph Collaborative Filtering
R.I.P.
π»
Ghosted
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
R.I.P.
π»
Ghosted
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
R.I.P.
π
404 Not Found
Graph Neural Networks for Social Recommendation
R.I.P.
π»
Ghosted
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted