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.

scholarly journals Ontology Learning pada Teks Tidak Terstruktur

Electrician ◽  
2020 ◽  
Vol 14 (2) ◽  
pp. 46-51
Author(s):  
Rajif Agung Yunmar
Keyword(s):  
Relation Extraction ◽  
Semantic Search ◽  
Expert Evaluation ◽  
Ontology Learning ◽  
Concept Extraction

Informasi yang tersebar pada berbagai sumber di internet banyak ditujukan hanya untuk manusia saja. Sementara itu, muncul kebutuhan agar informasi tersebut tidak hanya bisa dibaca dan dipahami oleh manusia saja, tetapi juga oleh mesin. Informasi dalam format yang dapat dipahami oleh mesin dapat digunakan untuk berbagai keperluan, misalnya: menjadi basis pengetahuan untuk penalaran, sharing knowledge antar mesin, semantic search, visualisasi informasi, dsb. Ontology learning adalah metode yang dapat mengekstrak informasi dari teks tidak terstruktur pada suatu dokumen atau halaman web untuk kemudian diubah menjadi basis pengetahuan dalam format yang dapat dipahami oleh mesin, yaitu dalam bentuk ontologi. Metode tersebut terdiri dari beberapa tahapan, yaitu: preprocessing, ekstraksi konsep, ekstraksi relasi, dan evaluasi. Preprocessing menyiapkan korpus uji sehingga siap untuk masuk kedalam metode ekstraksi konsep, yang menggunakan algoritma entropy concept extraction, pada bagian ekstraksi relasi digunakan algoritma subcat relation extraction, sedangkan pada bagian evaluasi ontologi menggunakan metode expert evaluation. Hasil akhir menunjukkan akurasi pada nilai 89.84% untuk ekstraksi konsep, 93.02% untuk ekstraksi relasi, dengan kepercayaan terhadap ekstraksi relasi pada prosentase 71.15%. Kata kunci: ontology learning, entropy concept extraction, subcat relation extraction.

scholarly journals Single Term Concepts from English Translated Qur’an Using Statistical Methods

2018 ◽  
Vol 7 (2.14) ◽  
pp. 13
Author(s):  
Rohana Ismail ◽  
Nurazzah Abd. Rahman ◽  
Zainab Abu Bakar
Keyword(s):  
Statistical Method ◽  
Statistical Methods ◽  
Ontology Development ◽  
Knowledge Based Systems ◽  
Ontology Learning ◽  
Concept Extraction ◽  
Knowledge Based ◽  
Single Term ◽  
Development Concept ◽  
F Measure

Ontology is essential for the success of knowledge based systems because it has the opportunity to share vocabulary, integrate knowledge easily and discover new instances or relations.  However, the development of ontology via manual is time consuming and tedious task. Thus, ontology learning comes to play it roles. The ontology learning tries to extract ontological elements to support the ontology development. Concept extraction is one of the important tasks in ontology learning. The Hajj domain of Quranic study, concepts have not fully discovered. Hence, this paper tries to discover concepts by extracting the single terms from Qur’an translated version. It provides result on extracting the single terms as concepts by using statistical methods. Apart from that, it has been experimented for English Translated Quran by Hilali Khan. Result shows that the performance of using tf method as a statistical method is significant with the f-measure value is 0.509. Based on the tf, the comparisons have been made for other statistical methods such as tfidf, Avetf and Ridf. 

Research of Conceptual Relation Extraction Based on Improved Hierarchical Clustering Method

2014 ◽  
Vol 8 (1) ◽  
pp. 355-360
Author(s):  
Caiyun Xie ◽  
Junyun Wu
Keyword(s):  
Relation Extraction ◽  
Main Task ◽  
Semantic Retrieval ◽  
Ontology Learning ◽  
Clustering Method ◽  
Concept Hierarchy ◽  
Concept Extraction ◽  
Relationship Extraction ◽  
Taxonomic Relation ◽  
Conceptual Relation

The main task of Ontology learning is concept extraction and conceptual relation extraction. This paper mainly studies the latter. Conceptual relation consists of taxonomic relation and non-taxonomic relation. It introduces hierarchy clustering method, and uses concept hierarchy clustering method which chooses different clustering standards in each hierarchy to obtain the taxonomic relation. It improves the accuracy of the relationship extraction. For extracting the nontaxonomic relation, this paper uses a extended association rule, this method can get concrete names of relation, and confirms the domain and range. In the end, the paper uses the introduced method of Ontology Learning to constructing a domain ontology in the law. And it completes the implementation of an Ontology-based semantic retrieval system. The final effect of this system application demonstrates that this Ontology learning method is efficient.

An Improved Algorithm of Concept Extraction in Ontology Learning

2013 ◽  
Vol 303-306 ◽  
pp. 1581-1584
Author(s):  
Jun Yun Wu ◽  
Cai Yun Xie
Keyword(s):  
Traditional Method ◽  
Ontology Learning ◽  
Concept Extraction ◽  
Relationship Of ◽  
The Relationship ◽  
Improved Algorithm

This paper introduces a traditional method of concept extraction. Considered some defects which this method will miss some concepts having synonyms and the relationship of “is-a”. It gives the improved algorithm to extract them. The result of the experiment shows the feasibility of this method.

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