Improve model card with pipeline tag and paper details

README.md

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-license: mit
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+---
+license: mit
+pipeline_tag: graph-ml
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+# Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs
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+This repository contains the model presented in the paper [Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs](https://huggingface.co/papers/2501.01644).
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+## Abstract
+Biomedical Knowledge Graphs (BKGs) integrate diverse datasets to elucidate complex relationships within the biomedical field. Effective link prediction on these graphs can uncover valuable connections, such as potential novel drug-disease relations. We introduce a novel multimodal approach that unifies embeddings from specialized Language Models (LMs) with Graph Contrastive Learning (GCL) to enhance intra-entity relationships while employing a Knowledge Graph Embedding (KGE) model to capture inter-entity relationships for effective link prediction. To address limitations in existing BKGs, we present PrimeKG++, an enriched knowledge graph incorporating multimodal data, including biological sequences and textual descriptions for each entity type. By combining semantic and relational information in a unified representation, our approach demonstrates strong generalizability, enabling accurate link predictions even for unseen nodes. Experimental results on PrimeKG++ and the DrugBank drug-target interaction dataset demonstrate the effectiveness and robustness of our method across diverse biomedical datasets. Our source code, pre-trained models, and data are publicly available at this https URL
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+## Code and Data
+The paper states that source code, pre-trained models, and data are publicly available. However, a direct link to a code repository was not provided in the context. Please refer to the paper for more details on accessing these resources.