Semantically enabled data mashups using ontology learning method for Web APIs

Author(s):  
Yong-Ju Lee ◽  
Jeong-Hong Kim
2011 ◽  
Vol 58-60 ◽  
pp. 1523-1528
Author(s):  
Hai Zhong Qian ◽  
Su Bin Shen

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.


2011 ◽  
Vol 121-126 ◽  
pp. 1911-1915
Author(s):  
Xian Min Wei

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.


Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1911
Author(s):  
Kai Xie ◽  
Chao Wang ◽  
Peng Wang

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.


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

2020 ◽  
Vol 4 (2) ◽  
pp. 174
Author(s):  
Meida Rachmawati ◽  
Suzana Widjajanti ◽  
Ahmad Ahmad ◽  
Aslan Aslan

This article aimed to promote English in elementary school students through a fun learning method, called the Fun English Camp. Several studies had been conducted to encounter the best solution to handle this issue. The researchers used PRISMA Protocol as an instrument to collect the data that has been widely used in the process of selecting relevant articles. The researchers reviewed twenty five scientific publications, related to Fun English Camp that has become an English learning approach for beginner students. Through a review of twenty five scientific publications, for instance book and journal, the researchers got scientific evidence that introduction of a learning method with the term Fun English camp has an impact on promoting language learning for elementary school children in Indonesia. Thus, the fun English camp method can be an interesting method to be applied by elementary school curriculum design in Indonesia. Keywords: English Camps, Learning Method, Fun English Learning


Author(s):  
Ni Putu Dian Permata Prasetyaningrum

Surabaya Shipping Polytechnic emphasizes on certain areas of expertise that Taruna must possess. This is the basis after graduating from shipping polytechnics, cadets must have expertise and skills. The purpose of this study was to study the effect of inquiry, discovery learning, and creativity levels on the ability to write descriptive essays on nautical and technical cadets at Surabaya Shipping Polytechnic. This type of research is research. This research uses quantitative methods using experiments. The location used in this research is Surabaya Shipping Polytechnic. The subjects in this study were the cadets of the Nautika A, Nautika B, Teknika A, and Teknika B. classes. Based on the results of the research and discussion, the following conclusions are obtained: There are those that can be solved looking for description essays in the cadets. learning discovery method. The test results show better investigation methods than the discovery of learning, There is a difference in the ability to write a description essay about cadets who have a high level of creativity with cadets who have a low level of creativity, the test results show better who have a high level of creativity, there are related with learning methods and descriptions of the ability to write essay descriptions, the test results show learning methods and creativity descriptions of the ability to write essay descriptions.


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