Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning

Fatima N Al-Aswadi,Huah Yong Chan,Keng Hoon Gan

doi:10.1007/978-3-030-70713-2_35

Fatima N Al-Aswadi, Huah Yong Chan + Show 1 more

Open Access

https://doi.org/10.1007/978-3-030-70713-2_35

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Publication Date: Jan 1, 2021
Citations: 10	License type: cc-by

Affiliation: Hodeidah University, Universiti Sains Malaysia

Abstract

With the large volume of unstructured data that increases constantly on the web, the motivation of representing the knowledge in this data in the machine-understandable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The current ontology repositories are quite limited either for their scope or for currentness. In addition, the current ontology extraction systems have many shortcomings and drawbacks, such as using a small dataset, depending on a large amount predefined patterns to extract semantic relations, and extracting a very few types of relations. The aim of this paper is to introduce a proposal of automatically extracting semantic concepts and relations from scientific publications. This paper suggests new types of semantic relations and points out of using deep learning (DL) models for semantic relation extraction.

Highlights

Constructing the ontologies is considered an important task to make this data in the machine-understandable form as well as human understandable form
The progress of WordNet is quite slow comparing with streaming data across the web, as well as it lacks many modern terms, such as cloud computing, deep learning or even netbook [3]
Another example of ontology repositories is YAGO (Yet Another Great Ontology) [4], it is an ontology that built on top of both WordNet and Wikipedia

Summary

INTRODUCTION

The substantial growth of unstructured data makes many applications of this data, such as information retrieval, information extraction or any other applications a hard and laborious task. The progress of WordNet is quite slow comparing with streaming data across the web, as well as it lacks many modern terms, such as cloud computing, deep learning or even netbook [3] Another example of ontology repositories is YAGO (Yet Another Great Ontology) [4], it is an ontology that built on top of both WordNet and Wikipedia. Most of them such as Textto-Onto and CRCTOL depend on predefined templates for relation extraction that lead to very low recall results Some of these tools use small dataset such as Textto-Onto which used only 21 web articles as the input dataset.

PROPOSED WORK

Design DBNs