Difference between revisions of "Keyword Suggestion Using Conceptual Graph Construction from Wikipedia Rich Documents"
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| − | '''Keyword Suggestion Using Conceptual Graph Construction from Wikipedia Rich Documents''' - scientific work related to Wikipedia quality published in 2008, written by Hadi Amiri, Abolfazl AleAhmad, Masoud Rahgozar and Farhad Oroumchian. | + | '''Keyword Suggestion Using Conceptual Graph Construction from Wikipedia Rich Documents''' - scientific work related to [[Wikipedia quality]] published in 2008, written by [[Hadi Amiri]], [[Abolfazl AleAhmad]], [[Masoud Rahgozar]] and [[Farhad Oroumchian]]. |
== Overview == | == Overview == | ||
| − | Conceptual graph is a graph in which nodes are concepts and the edges indicate the relationship between them. Creation of conceptual graphs is a hot topic in the area of knowledge discovery. Natural Language Processing (NLP) based conceptual graph creation is one of the efficient but costly methods in the field of information extraction. Compared to NLP based methods, Statistical methods have two advantages, namely, they are language independent and more computationally efficient. In this paper authors present an efficient statistical method for creating a conceptual graph from a large document collection. The documents which are used in this paper are from Wikipedia collection because of their rich and valid content. Moreover, authors use the final conceptual graph to suggest a list of similar keywords for each unique concept or combination of concepts. Also, authors will show the viability of approach by comparing its result to a similar system called the Wordy system. | + | Conceptual graph is a graph in which nodes are concepts and the edges indicate the relationship between them. Creation of conceptual graphs is a hot topic in the area of knowledge discovery. [[Natural Language Processing]] (NLP) based conceptual graph creation is one of the efficient but costly methods in the field of [[information extraction]]. Compared to NLP based methods, Statistical methods have two advantages, namely, they are language independent and more computationally efficient. In this paper authors present an efficient statistical method for creating a conceptual graph from a large document collection. The documents which are used in this paper are from [[Wikipedia]] collection because of their rich and valid content. Moreover, authors use the final conceptual graph to suggest a list of similar keywords for each unique concept or combination of concepts. Also, authors will show the viability of approach by comparing its result to a similar system called the Wordy system. |
Revision as of 10:53, 30 November 2019
Keyword Suggestion Using Conceptual Graph Construction from Wikipedia Rich Documents - scientific work related to Wikipedia quality published in 2008, written by Hadi Amiri, Abolfazl AleAhmad, Masoud Rahgozar and Farhad Oroumchian.
Overview
Conceptual graph is a graph in which nodes are concepts and the edges indicate the relationship between them. Creation of conceptual graphs is a hot topic in the area of knowledge discovery. Natural Language Processing (NLP) based conceptual graph creation is one of the efficient but costly methods in the field of information extraction. Compared to NLP based methods, Statistical methods have two advantages, namely, they are language independent and more computationally efficient. In this paper authors present an efficient statistical method for creating a conceptual graph from a large document collection. The documents which are used in this paper are from Wikipedia collection because of their rich and valid content. Moreover, authors use the final conceptual graph to suggest a list of similar keywords for each unique concept or combination of concepts. Also, authors will show the viability of approach by comparing its result to a similar system called the Wordy system.
