A Lightweight Ontology Learning Method for Chinese Government Documents

Ontology plays a key role in such areas: knowledge engineering, artificial intelligence, information retrieval, semantic web and web service. It is important to recover knowledge associated with specific domains in relational database to semantics, especially, in Ontology learning field. Previous works showed that ontologies can learn from relational database. However, the presented approaches still have some limits. In this paper, we present an ontology learning method based on Object Relation Mapping (ORM) that presents how the source of the databases can be exploited to ontology and the details of object can be generated, such as class hierarchies, relationship and properties.

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Transnational Higher Education in China: Contexts, Characteristics and Concerns

Australian Journal of Education ◽

10.1177/000494410805200305 ◽

2008 ◽

Vol 52 (3) ◽

pp. 272-286 ◽

Cited By ~ 51

Author(s):

Rui Yang

Keyword(s):

Higher Education ◽

English Literature ◽

Chinese Government ◽

Transnational Higher Education ◽

Regulatory Frameworks ◽

Government Documents ◽

New Development ◽

Comprehensive Picture ◽

Higher Education In China ◽

Basic Features

Transnational higher education is a rapidly growing phenomenon that is under-researched and often even misunderstood. As the world's most promising market, China has the potential to dwarf all traditional offshore markets. Little research has been done to seriously analyse the fast growth in China. A sound understanding of the Chinese situation facilitates improvement of future provision of higher education by Australian universities, presently the most dominant force in China. This article incorporates Chinese and English literature, reviews the latest Chinese government documents, and delineates a comprehensive picture of transnational education provision in China. It locates the development in a wider social and policy context in China, examines the basic features of Chinese—foreign partnerships, and reveals some major issues of concern. It argues that China needs to form effective regulatory frameworks to govern this new development in higher education, especially in terms of quality assurance to ensure cultural appropriateness of the joint programs.

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An Unsupervised Keywords Extraction Approach for Chinese Government Documents

2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) ◽

10.1109/icaibd.2019.8837026 ◽

2019 ◽

Author(s):

Xin Fang ◽

Zesong Li ◽

Zeyuan Li ◽

Yajun Song

Keyword(s):

Chinese Government ◽

Government Documents

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A rhetorical approach to translation

Translation Spaces ◽

10.1075/ts.6.2.05qia ◽

2017 ◽

Vol 6 (2) ◽

pp. 270-290

Author(s):

Duoxiu Qian ◽

David Kaufer

Keyword(s):

Native Speaker ◽

Political Agenda ◽

Chinese Government ◽

The West ◽

Chinese Context ◽

The Past ◽

Government Documents ◽

Wide Scale ◽

The Many ◽

Effective Transmission

AbstractOver the past three decades, the Chinese government has repeatedly called for the effective transmission of its policies to the West through translation. Yet the effectiveness of translation and its evaluation has remained a ticklish issue, particularly for texts with a political agenda. Fidelity to literal denotative meaning at the grain of words and phrases is generally insufficient for the translation of such texts. Texts in these sensitive domains of the Chinese context call for exacting fidelity in tone, register, genre, stance, connotation, and, overall, rhetoric. The Chinese government, wishing to avoid misinterpretation, is concerned with sharing their policies with foreigners as closely as possible to the way the many authors of these policies understood them from the inside. In this paper, we think of a “rhetoric” of translation holistically as capturing the “inside contours” of words and phrases as understood by a native speaker. For this purpose, we present a rhetorical approach to translation that can help explain the translation standards of Chinese government documents marked for wide-scale distribution abroad. The approach and method can be applicable in the assessment of other translations when rhetoric or the overall effect is the major concern.

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An Ontology Learning Method Based on Document Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.1911 ◽

2011 ◽

Vol 121-126 ◽

pp. 1911-1915

Author(s):

Xian Min Wei

Keyword(s):

Clustering Algorithm ◽

Document Clustering ◽

The Body ◽

Learning Method ◽

Automatic Method ◽

Ontology Learning ◽

Ontology Construction ◽

Ontology Merging ◽

Construction Methods ◽

Final Output

Ontology learning is a series method and technology of semi-automatic ontology construction, which uses various data sources to create or expand in-built ontology by semi-automatic method to build a new ontology. Existing ontology construction methods are to collect a large number of conceptual terms based on a large number of field text and background corpus, and then to select field concepts to construct a body. The proposed Cluster-Merge algorithm is to use k-means clustering algorithm in the field document at first, then according to document clustering results to construct body by themself, at last accoring to the ontology similarity for ontology merging to get final output ontology. The experiment may prove that Cluster-Merge algorithm can improve the body resulting recall and precision.

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An Ontology Learning Method Enhanced by Frame Semantics

Seventh IEEE International Symposium on Multimedia (ISM'05) ◽

10.1109/ism.2005.32 ◽

2006 ◽

Cited By ~ 8

Author(s):

Enhong Chen ◽

Gaofeng Wu

Keyword(s):

Learning Method ◽

Ontology Learning ◽

Frame Semantics

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A Domain-Independent Ontology Learning Method Based on Transfer Learning

Electronics ◽

10.3390/electronics10161911 ◽

2021 ◽

Vol 10 (16) ◽

pp. 1911

Author(s):

Kai Xie ◽

Chao Wang ◽

Peng Wang

Keyword(s):

Transfer Learning ◽

Language Processing ◽

Critical Role ◽

Parameters Estimation ◽

Learning Performance ◽

Machine Learning Techniques ◽

Learning Method ◽

Ontology Learning ◽

Target Domain ◽

Real World Datasets

Ontology plays a critical role in knowledge engineering and knowledge graphs (KGs). However, building ontology is still a nontrivial task. Ontology learning aims at generating domain ontologies from various kinds of resources by natural language processing and machine learning techniques. One major challenge of ontology learning is reducing labeling work for new domains. This paper proposes an ontology learning method based on transfer learning, namely TF-Mnt, which aims at learning knowledge from new domains that have limited labeled data. This paper selects Web data as the learning source and defines various features, which utilizes abundant textual information and heterogeneous semi-structured information. Then, a new transfer learning model TF-Mnt is proposed, and the parameters’ estimation is also addressed. Although there exist distribution differences of features between two domains, TF-Mnt can measure the relevance by calculating the correlation coefficient. Moreover, TF-Mnt can efficiently transfer knowledge from the source domain to the target domain and avoid negative transfer. Experiments in real-world datasets show that TF-Mnt achieves promising learning performance for new domains despite the small number of labels in it, by learning knowledge from a proper existing domain which can be automatically selected.

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