Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You and Your Team.

Learn More →

Method for automatic key concepts extraction

Method for automatic key concepts extraction PurposeOntology of a domain mainly consists of a set of concepts and their semantic relations. It is typically constructed and maintained by using ontology editors with substantial human intervention. It is desirable to perform the task automatically, which has led to the development of ontology learning techniques. One of the main challenges of ontology learning from the text is to identify key concepts from the documents. A wide range of techniques for key concept extraction have been proposed but are having the limitations of low accuracy, poor performance, not so flexible and applicability to a specific domain. The propose of this study is to explore a new method to extract key concepts and to apply them to literature in the nuclear domain.Design/methodology/approachIn this article, a novel method for key concept extraction is proposed and applied to the documents from the nuclear domain. A hybrid approach was used, which includes a combination of domain, syntactic name entity knowledge and statistical based methods. The performance of the developed method has been evaluated from the data obtained using two out of three voting logic from three domain experts by using 120 documents retrieved from SCOPUS database.FindingsThe work reported pertains to extracting concepts from the set of selected documents and aids the search for documents relating to given concepts. The results of a case study indicated that the method developed has demonstrated better metrics than Text2Onto and CFinder. The method described has the capability of extracting valid key concepts from a set of candidates with long phrases.Research limitations/implicationsThe present study is restricted to literature coming out in the English language and applied to the documents from nuclear domain. It has the potential to extend to other domains also.Practical implicationsThe work carried out in the current study has the potential of leading to updating International Nuclear Information System thesaurus for ontology in the nuclear domain. This can lead to efficient search methods.Originality/valueThis work is the first attempt to automatically extract key concepts from the nuclear documents. The proposed approach will address and fix the most of the problems that are existed in the current methods and thereby increase the performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Electronic Library Emerald Publishing

Loading next page...
 
/lp/emerald-publishing/method-for-automatic-key-concepts-extraction-LveSowGnyx
Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0264-0473
DOI
10.1108/EL-01-2018-0012
Publisher site
See Article on Publisher Site

Abstract

PurposeOntology of a domain mainly consists of a set of concepts and their semantic relations. It is typically constructed and maintained by using ontology editors with substantial human intervention. It is desirable to perform the task automatically, which has led to the development of ontology learning techniques. One of the main challenges of ontology learning from the text is to identify key concepts from the documents. A wide range of techniques for key concept extraction have been proposed but are having the limitations of low accuracy, poor performance, not so flexible and applicability to a specific domain. The propose of this study is to explore a new method to extract key concepts and to apply them to literature in the nuclear domain.Design/methodology/approachIn this article, a novel method for key concept extraction is proposed and applied to the documents from the nuclear domain. A hybrid approach was used, which includes a combination of domain, syntactic name entity knowledge and statistical based methods. The performance of the developed method has been evaluated from the data obtained using two out of three voting logic from three domain experts by using 120 documents retrieved from SCOPUS database.FindingsThe work reported pertains to extracting concepts from the set of selected documents and aids the search for documents relating to given concepts. The results of a case study indicated that the method developed has demonstrated better metrics than Text2Onto and CFinder. The method described has the capability of extracting valid key concepts from a set of candidates with long phrases.Research limitations/implicationsThe present study is restricted to literature coming out in the English language and applied to the documents from nuclear domain. It has the potential to extend to other domains also.Practical implicationsThe work carried out in the current study has the potential of leading to updating International Nuclear Information System thesaurus for ontology in the nuclear domain. This can lead to efficient search methods.Originality/valueThis work is the first attempt to automatically extract key concepts from the nuclear documents. The proposed approach will address and fix the most of the problems that are existed in the current methods and thereby increase the performance.

Journal

The Electronic LibraryEmerald Publishing

Published: Feb 4, 2019

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$499/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month