# An Open Relation Extraction Method for Domain Text Based on Hybrid Supervised Learning
###### Abstract  
Current research on knowledge graph construction is focused chiefly on general-purpose fields, whereas constructing knowledge graphs in vertically segmented professional fields faces numerous difficulties. To solve the problems of complex relation types of domain entities, the lack of a large amount of annotated corpus, and the difficulty of extraction, this study proposed a method for constructing domain-annotated datasets based on publicly available texts on the web, which integrates remote supervision and semi-supervision. For the relational triad extraction of a given core entity (an entity lexicon defined semi-automatically by experts), an inflated gate attention network structure for increasing the perceptual field of the model is proposed. In addition, a relational extraction model, Ro-DGANet, was designed based on this structure, incorporating the idea of a probability graph. The Ro-DGANet model was experimentally evaluated on the publicly available Chinese datasets LIC2019 and CHIP2020 and compared with the mainstream relation extraction models, achieving the best results with F1 values of 82.99% and 66.39%, respectively. Finally, the Ro-DGANet model was applied to the relation extraction task of equipment components in industrial scenarios and to the relation extraction task of core knowledge points of programming languages. The analysis results show that the proposed method is applicable to open relation extraction among core entities in different domains with reliable performance and portability. dilate gated attention network; remote supervision; relation extraction +
Footnote †: journal: Medical  
The pipeline approach, in which entity recognition is achieved before the relation classification task;
2. A joint model of entity recognition plus relation classification. Currently, most research on knowledge graph construction is focused on general-purpose domains, whereas constructing knowledge graphs for vertically segmented specialist domains faces many difficulties. Compared with general-purpose domains, domain-specific relation extraction relies heavily on expert-defined entity relation. The number of relation types is minimal, and there is a lack of a high-quality annotation corpus, requiring a considerable investment of human and material resources. Most annotation tasks can only be performed by experienced frontline experts, particularly in the industrial sector. To reduce the cost of data annotation, [PERSON] [5] proposed a remotely supervised approach to automatically generate training data based on the idea that if an entity pair has a relation in an external knowledge base, all samples containing the entity pair are matched in total, and all matched samples will express this relation. However, the above approach generates a large amount of noisy data, which hinders the performance of the training model. Traditional relation extraction is aimed at extracting the categories of qualified relation and attributes [6]. For example, [PERSON] [7] extracted words or phrases directly from the text to represent entity relation types; these studies primarily used remotely-supervised external tags to identify relation phrases from external knowledge bases and then identify entity relation pairs. However, in the face of the massive amount of data accumulated in different domains, supervised extraction models based on manually annotated corpus can hardly solve the complex problem of specialist domain entities and relation types. Open relation extraction aims to extract relation facts from domain corpora, and [PERSON] [8] proposed using high-quality annotated data from predefined relation to achieve open relation extraction. However, the manual annotation of high-quality datasets is still required. To further reduce annotation costs while obtaining high-quality annotated samples, this study proposes a distant supervised [9; 10; 11] and semi-supervised domain dataset construction method to provide data support for constructing knowledge graphs in different professional domains.