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+# Introduction
+
+Mining biomedical text data, such as scientific literature and clinical notes, to generate hypotheses and validate discovery has led to many impactful clinical applications [@zhao2021recent; @lever2019cancermine]. One fundamental problem in biomedical text mining is entity normalization, which aims to map a phrase to a concept in the controlled vocabulary [@sung2020biomedical]. Accurate entity normalization enables us to summarize and compare biomedical insights across studies and obtain a holistic view of biomedical knowledge. Current approaches [@wright2019normco; @ji2020bert; @sung2020biomedical] to biomedical entity normalization often focus on normalizing more standardized entities such as diseases [@dougan2014ncbi; @li2016biocreative], drugs [@kuhn2007stitch; @pradhan2013task], genes [@szklarczyk2016string] and adverse drug reactions [@roberts2017overview]. Despite their encouraging performance, these approaches have not yet been applied to the more ambiguous entities, such as processes, pathways, cellular components, and functions [@smith2007obo], which lie at the center of life sciences. As scientists rely on these entities to describe disease and drug mechanisms [@yu2016translation], the inconsistent terminology used across different labs inevitably hampers the scientific communication and collaboration, necessitating the normalization of these entities.
+
+![Illustration of GraphPrompt. GraphPrompt classifies a test synonym (CD115 (human)) to an entity in the graph by converting the graph into prompt templates based on the zeroth-order neighbor ($\mathcal{T}^0$), first-order neighbors ($\mathcal{T}^1$), and second-order neighbors ($\mathcal{T}^2$).](imgpdf/intro.pdf){#figIntro width="50%"}
+
+The first immediate bottleneck to achieve the normalization of these under-explored entities is the lack of a high-quality and large-scale dataset, which is the prerequisite for existing entity normalization approaches [@wright2019normco; @ji2020bert; @sung2020biomedical]. To tackle this problem, we collected 70 types of biomedical entities from OBO Foundry [@smith2007obo], spanning a wide variety of biomedical areas and containing more than 2 million entity-synonym pairs. These pairs are all curated by domain experts and together form a high-quality and comprehensive controlled vocabulary for biomedical sciences, greatly augmenting existing biomedical entity normalization datasets [@dougan2014ncbi; @li2016biocreative; @roberts2017overview]. The tedious and onerous curation of this high-quality dataset further confirms the necessity of developing data-driven approaches to automating this process and motivates us to introduce this dataset to the NLP community.
+
+In addition to being the first large-scale dataset encompassing many under-explored entity types, this OBO-syn dataset presents a novel setting of graph-based entity normalization. Specifically, entities of the same type form a relational directed acyclic graph (DAG), where each edge represents a relationship (e.g., $\mathrm{is\_a}$) between two entities. Intuitively, this DAG could assist the entity normalization since nearby entities are biologically related, and thus more likely to be semantically and morphologically similar. Existing entity normalization and synonym prediction methods are incapable of considering the topological similarity from this rich graph structure [@wright2019normco; @ji2020bert; @sung2020biomedical], limiting their performance, especially in the few-shot and zero-shot settings. Recently, prompt-based learning has demonstrated many successful NLP applications [@radford2019language; @schick2020exploiting; @jiang2020can]. The key idea of using prompt is to circumvent the requirement of a large number of labeled data by creating masked templates and then converting supervised learning tasks to a masked-language model task [@liu2021pre]. However, it remains unknown how to convert a large graph into text templates for prompt-based learning. Representing graphs as prompt templates might effectively integrate the topological similarity and textural similarity by alleviating the over-smoothing caused by propagating textual features on the graph.
+
+In this paper, we propose GraphPrompt, a prompt-based learning method for entity normalization with the consideration of graph structures. The key idea of our method is to convert the graph structural information into prompt templates and solve a masked-language model task, rather than incorporating textual features into a graph-based framework. Our graph-based templates explicitly model the high-order neighbors (e.g., neighbors of neighbors) in the graph, which enables us to correctly classify synonyms that have relatively lower morphological similarity with the ground-truth entity (**Figure [1](#figIntro){reference-type="ref" reference="figIntro"}**). Experiments on the novel OBO-syn dataset demonstrate the superior performance of our method against existing entity normalization approaches, indicating the advantage of considering the graph structure. Case studies and the comparison to the conventional graph approach further reassure the effectiveness of our prompt templates, implicating opportunities on other graph-based NLP applications. Collectively, we introduce a novel biomedical entity normalization task, a large-scale and high-quality dataset, and a novel prompt-based solution to advance biomedical entity normalization.

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