GREG: A Global Level Relation Extraction with Knowledge Graph Embedding

Abstract

In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations-usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level 'local' relation's to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model's modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently.

TransET: Knowledge Graph Embedding with Entity Types

“…Esma et al [25] use the implicit type information contained in the dataset to train a joint model on the correctness and type consistency of the triplet. In addition, GREG [26] uses vector representations of mention-level local relations to construct knowledge graphs that can represent the input document. Since each knowledge graph embedding has its own idiosyncrasy, Su et al [27] proposed a committee model to form a committee of various knowledge graph embeddings to reflect various perspectives.…”

Development of Intelligent Information System for Digital Cultural Contents

“…To extract the relations, it searches whether keywords corresponding to each subject and object exist and then interprets their relations according to the verb. Relations across several sentences in the document are analyzed using two modules-an individual object recognition module and a knowledge graph-building module [17]. Figure 3 shows the structure of the contents of the triple auto generation model.…”

Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction

“…Tang et al (2020) propose a Hierarchical Inference Network (HIN), which can aggregate inference information from entity-level to sentence-level and then to document-level. Kim et al (2020) extract global level relations from a document by utilizing the knowledge graph constructed from local relations. It is important to note that the hierarchical inference mechanism from local level to global level is necessary for document-level RE.…”

“…Previous work in document-level RE employ hierarchical inference networks or Graph Convolutional Networks (GCN) (Kipf and Welling, 2017) to extract features from local level to global level for multihop relational reasoning (Wang et al, 2019;Kim et al, 2020;Guo et al, 2019;Sahu et al, 2019). How to construct a hierarchical inference network with GCN to encode rich global context information is crucial for document-level RE.…”