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Extracting Non-taxonomic Relationships for Ontology Learning of Scientific Resources
Received date: 2016-07-17
Revised date: 2016-10-08
Online published: 2016-10-20
[Purpose/significance] Extracting non-taxonomic relationship is the most complex work of ontology learning. It is also an unsolved problem of ontology learning. Related research is mainly based on the domain vocabulary. This paper aims to study the scientific ontology construction, and the knowledge acquisition comes from scientific articles. This paper uses the structure of the scientific articles to extract non-taxonomic relationships. [Method/process] Firstly, based on the concept extraction of the previous work of our project, the paper classifies the concept into some categories in order to exclude some concept types with verb templates. Then, in order to define the type of the relationship, it uses C-value to extract verb represent the relationship. After that, it evaluates the relationship of the concepts, and uses MI to exclude some concepts. The experiments show that it is very effective. At last, it evaluates the relationship of concepts and verb to analyze the factors influencing the relationship, and selects a model. The experiment compares the associate rule methods. [Result/conclusion] It shows the model proposed by this paper is very effective and outperforms the associate rule methods, and it is especially significant with a spread of the corpus.
Jiang Ting , Sun Jianjun . Extracting Non-taxonomic Relationships for Ontology Learning of Scientific Resources[J]. Library and Information Service, 2016 , 60(20) : 112 -122 . DOI: 10.13266/j.issn.0252-3116.2016.20.014
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