scholarly journals Research on OWL Ontology Learning Method Based on Relational Schema

2019 ◽  
Author(s):  
Chun-hua LIAO ◽  
Guang-yao XIONG ◽  
Chun-lei CHEN
Keyword(s):  
Learning Method ◽  
Ontology Learning ◽  
Relational Schema ◽  
Owl Ontology

Bridging the Mapping between Ontology and Relation Schemas Based on ORMapping Technology

2011 ◽  
Vol 58-60 ◽  
pp. 1523-1528
Author(s):  
Hai Zhong Qian ◽  
Su Bin Shen
Keyword(s):  
Artificial Intelligence ◽  
Information Retrieval ◽  
Semantic Web ◽  
Web Service ◽  
Relational Database ◽  
Knowledge Engineering ◽  
Object Relation ◽  
Learning Method ◽  
Ontology Learning ◽  
Class Hierarchies

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.

An Ontology Learning Method Based on Document Clustering

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.

scholarly journals A Domain-Independent Ontology Learning Method Based on Transfer Learning

Electronics ◽  
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.

scholarly journals Research and Application on Domain Ontology Learning Method Based on LDA

2017 ◽  
Vol 12 (4) ◽  
pp. 265-273 ◽  
Author(s):  
Wang Hong ◽  
◽  
Zhang Hao ◽  
Shi Jinchuan
Keyword(s):  
Domain Ontology ◽  
Learning Method ◽  
Ontology Learning
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