A method of core concept extraction based on semantic‐weight ranking

2021 ◽  
Author(s):  
Han Zhong ◽  
Zhenhu Ning ◽  
Guijun Li ◽  
Zheng Li
Keyword(s):  
Core Concept ◽  
Concept Extraction

Method for automatic key concepts extraction

2019 ◽  
Vol 37 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Sudarsana Desul ◽  
Madurai Meenachi N. ◽  
Thejas Venkatesh ◽  
Vijitha Gunta ◽  
Gowtham R. ◽  
...  
Keyword(s):  
Coming Out ◽  
English Language ◽  
Hybrid Approach ◽  
Ontology Learning ◽  
Specific Domain ◽  
Content Type ◽  
Concept Extraction ◽  
Nuclear Domain ◽  
Wide Range ◽  
Key Concepts

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.

Rule management for integration of information systems

2000 ◽  
Vol 13 (5) ◽  
pp. 446-460 ◽  
Author(s):  
Injun Choi ◽  
Heeje Cho ◽  
Sungmoon Bae ◽  
Seokjin Hyun
Keyword(s):  
Information Systems ◽  
Rule Management

scholarly journals Clinical Concept Extraction for Document-Level Coding

2019 ◽  
Author(s):  
Sarah Wiegreffe ◽  
Edward Choi ◽  
Sherry Yan ◽  
Jimeng Sun ◽  
Jacob Eisenstein
Keyword(s):  
Concept Extraction ◽  
Clinical Concept ◽  
Document Level
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