scholarly journals Knowledge systematization for ontology learning methods

2018 ◽  
Vol 126 ◽  
pp. 2194-2207 ◽  
Author(s):  
Agnieszka Konys
2021 ◽  
Author(s):  
Yue Niu ◽  
Hongjie Zhang

With the growth of the internet, short texts such as tweets from Twitter, news titles from the RSS, or comments from Amazon have become very prevalent. Many tasks need to retrieve information hidden from the content of short texts. So ontology learning methods are proposed for retrieving structured information. Topic hierarchy is a typical ontology that consists of concepts and taxonomy relations between concepts. Current hierarchical topic models are not specially designed for short texts. These methods use word co-occurrence to construct concepts and general-special word relations to construct taxonomy topics. But in short texts, word cooccurrence is sparse and lacking general-special word relations. To overcome this two problems and provide an interpretable result, we designed a hierarchical topic model which aggregates short texts into long documents and constructing topics and relations. Because long documents add additional semantic information, our model can avoid the sparsity of word cooccurrence. In experiments, we measured the quality of concepts by topic coherence metric on four real-world short texts corpus. The result showed that our topic hierarchy is more interpretable than other methods.


2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.


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.


2017 ◽  
Vol 4 (1) ◽  
pp. 12
Author(s):  
Dessy Lutfiasari ◽  
Mahmudah Mahmudah

The use of the current method of learning very big influence on the growth and development of students' creativity and interest for all subjects to be taught, especially in the use of partograf. From interviews to the 10 students of IV semester Prodi Midwifery (D-III) Kadiri University is known that 4 (40%) of students said it was understood, 4 (40%) of other students say they are confused and 2 (20%) of them said that he was a student not familiar with partograf. This shows the lack of understanding of students in filling partograph. The research objective is to determine the effectiveness of the use of learning methods for skills training simulation with filling partograph the second semester students in Midwifery (D-III) Kadiri University Faculty of Health Sciences in 2015. The research design used is pre experiment with design Static Group Comparison/Posttest Only Control Group Design. The population studied were all students of the second semester in Midwifery (D-III) Faculty of Health Sciences University of Kadiri numbered 50 students and sampling techniques Federer totaled 32 students. This is a research instrument partograph sheet. Results of the study were analyzed using the Mann Whitney test with a significance level of 0.05 were used.The results showed 7 respondents (46.7%) are adept at using partograf with simulation teaching methods and 6 respondents (40.0%) are adept at using partograph with practice learning methods. Data were analyzed by Mann Whitney test obtained ρ = 0.965; α = 0.05 means that H0 is accepted and H1 rejected. This means there is no difference in the effective use of learning methods for skills training simulation with partograph filling. Based on the results of this study are expected to choose the method of learning as a learning method in charging partograph because both methods equally effective.; Keywords: simulation methods, drilling methods, partograph filling


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