Distantly Supervised Document-Level Biomedical Relation Extraction with Neighborhood Knowledge Graphs (2023)

Abstract: We propose a novel distantly supervised document-level biomedical relation extraction model that uses partial knowledge graphs that include the graph neighborhood of the entities appearing in each input document. Most conventional distantly supervised relation extraction methods use only the entity relations automatically annotated by using knowledge base entries. They do not fully utilize the rich information in the knowledge base, such as entities other than the target entities and the network of heterogeneous entities defined in the knowledge base. To address this issue, our model integrates the representations of the entities acquired from the neighborhood knowledge graphs with the representations of the input document. We conducted experiments on the ChemDisGene dataset using Comparative Toxicogenomics Database (CTD) for document-level relation extraction with respect to interactions between drugs, diseases, and genes. Experimental results confirmed the performance improvement by integrating entities and their neighborhood biochemical information from the knowledge base.

## introduction the number of documents reporting new interactions and functional/pathway relationships between biochemical entities continues to increase rapidly, and manual registration and maintenance of knowledge bases are becoming increasingly difficult to keep up with the pace @xcite . automatic extraction of biochemical relationships from documents could help in maintaining. machine learning approaches have been the mainstream of relation extraction (re) for more than a decade due to their high performance. however, machine learning requires a large amount of labeled data that require time-consuming and costly manual efforts to construct @xcite @xcite . to alleviate the efforts, @xcite proposed distantly supervised re (dsre), which constructs a distantly supervised corpus in which an unlabeled corpus is automatically labeled using existing knowledge bases. unlike any manually labeled corpus, a distantly supervised corpus is directly connected to the knowledge base entries because it is labeled based on the target properties between entities described in the knowledge bases. however, existing dsre uses only the distantly supervised corpus and does not take advantage of other rich information in the knowledge bases, such as the features of the entities, entities that do not appear in the corpus, and a wide range of relationships between entities. to address the limitations and utilize the rich information in the knowledge base for re, we propose a model for biomedical dsre from documents that constructs neighborhood knowledge graphs and integrates them into re using the distantly supervised corpus. a neighborhood knowledge graph consists of the neighbors of the knowledge base entities appearing in an input document. when constructing neighborhood knowledge graphs, we eliminate links between target entity pairs during distantly supervised learning to avoid label leakage. by using neighbors, we can avoid processing a huge amount of information in the knowledge base in processing each entity pair. the contributions of this paper are summarized as follows: ## proposed model the overview of the proposed model is displayed in figure 1 . in our study, we propose to integrate the information in the neighborhood of the entities from the knowledge graphs that are present in the input document. ## experimental settings we trained models using chemdisgene @xcite , distantly supervised data (ctdderived) as the training and development data, and manually-labeled data (curated) as the test data. the ctd-derived data contain 76,942 and 1,521 medical references as training and development data, respectively. the curated data contain 523 medical references as test data. we used the microaveraged f1 measure as the evaluation metric. the task is to classify entity pairs of chemical, disease, ## results the performance of extracting relationships between drugs, diseases, and genes from the documents on the curated test data is shown in compared to the scores of @xcite , our baseline pubmedbert result showed slightly higher performance than pubmedbert, while our pubmedbert+bran model showed comparable performance, as shown in we also incorporated our neighborhood kg into the pubmedbert+bran model, but it did not show performance improvement. furthermore, compared to the scores of @xcite , our proposed model showed a performance lower than pubmedbert+ssr-pu+atlop. however, their model does not use the neighborhood knowledge graph, and our proposed method can be incorporated into their model. we will leave the adaptation of our approach to these models for future work. ## conclusion for future work, we will apply our model to state-of-the-art models. we will also investigate the way to utilize the extracted results in enhancing knowledge graphs to link the text and knowledge graph information more deeply and leverage the knowledge graph information more effectively.