GREG: A Global Level Relation Extraction with Knowledge Graph Embedding

Autor: Yuna Hur, Kuekyeng Kim, Heuiseok Lim, Gyeongmin Kim
Rok vydání: 2020
Předmět:
Theoretical computer science
Meta learning (computer science)
Relation (database)
Computer science
relation extraction
meta learning
text summarization
02 engineering and technology
lcsh:Technology
Task (project management)
lcsh:Chemistry
020204 information systems
0202 electrical engineering
electronic engineering
information engineering
General Materials Science
natural language processing
lcsh:QH301-705.5
Instrumentation
Fluid Flow and Transfer Processes
lcsh:T
Process Chemistry and Technology
General Engineering
Unstructured data
Construct (python library)
Automatic summarization
Relationship extraction
lcsh:QC1-999
Computer Science Applications
machine learning
lcsh:Biology (General)
lcsh:QD1-999
knowledge graph
lcsh:TA1-2040
Embedding
020201 artificial intelligence & image processing
lcsh:Engineering (General). Civil engineering (General)
lcsh:Physics
Zdroj: Applied Sciences
Volume 10
Issue 3
Applied Sciences, Vol 10, Iss 3, p 1181 (2020)
ISSN: 2076-3417
DOI: 10.3390/app10031181
Popis: 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&mdash
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 &lsquo
local&rsquo
relation&rsquo
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&rsquo
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.
Databáze: OpenAIRE
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