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Information extraction pipelines for knowledge graphs

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Bibliographic Details
Main Authors: Mohamad Yaser, Jaradeh, Kuldeep, Singh, Markus, Stocker
Format: Book
Language:English
Published: Springer 2023
Subjects:
KG
PLUMBER
Online Access:https://link.springer.com/article/10.1007/s10115-022-01826-x
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8279
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spelling oai:localhost:PNK-82792023-04-25T06:43:24Z Information extraction pipelines for knowledge graphs Mohamad Yaser, Jaradeh Kuldeep, Singh Markus, Stocker KG PLUMBER CC BY In the last decade, a large number of knowledge graph (KG) completion approaches were proposed. Albeit effective, these efforts are disjoint, and their collective strengths and weaknesses in effective KG completion have not been studied in the literature. We extend PLUMBER, a framework that brings together the research community’s disjoint efforts on KG completion. We include more components into the architecture of PLUMBER to comprise 40 reusable components for various KG completion subtasks, such as coreference resolution, entity linking, and relation extraction. Using these components, PLUMBER dynamically generates suitable knowledge extraction pipelines and offers overall 432 distinct pipelines. We study the optimization problem of choosing optimal pipelines based on input sentences. 2023-04-25T06:43:24Z 2023-04-25T06:43:24Z 2023 Book https://link.springer.com/article/10.1007/s10115-022-01826-x https://dlib.phenikaa-uni.edu.vn/handle/PNK/8279 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic KG
PLUMBER
spellingShingle KG
PLUMBER
Mohamad Yaser, Jaradeh
Kuldeep, Singh
Markus, Stocker
Information extraction pipelines for knowledge graphs
description CC BY
format Book
author Mohamad Yaser, Jaradeh
Kuldeep, Singh
Markus, Stocker
author_facet Mohamad Yaser, Jaradeh
Kuldeep, Singh
Markus, Stocker
author_sort Mohamad Yaser, Jaradeh
title Information extraction pipelines for knowledge graphs
title_short Information extraction pipelines for knowledge graphs
title_full Information extraction pipelines for knowledge graphs
title_fullStr Information extraction pipelines for knowledge graphs
title_full_unstemmed Information extraction pipelines for knowledge graphs
title_sort information extraction pipelines for knowledge graphs
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s10115-022-01826-x
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8279
_version_ 1764177437478879232
score 8.891053

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