Temat: semantic search - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Dynamic semantic description and search methods for heterogeneous learning resources
Autorzy:: Lai, Xiaocong
Pan, Ying
Jiang, Xueling
Powiązania:: https://bibliotekanauki.pl/articles/2173685.pdf
Data publikacji:: 2022
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: heterogeneous data
learning resources
semantic description
semantic search
dane niejednorodne
zasoby edukacyjne
opis semantyczny
wyszukiwanie semantyczne
Opis:: Learning resources are massive, heterogeneous, and constantly changing. How to find the required resources quickly and accurately has become a very challenging work in the management and sharing of learning resources. According to the characteristics of learning resources, this paper proposes a progressive learning resource description model, which can describe dynamic heterogeneous resource information on a fine-grained level by using information extraction technology, then a semantic annotation algorithm is defined to calculate the semantic of learning resource and add these semantic to the description model. Moreover, a semantic search method is proposed to find the required resources, which calculate the content with the highest similarity to the user query, and then return the results in descending order of similarity. The simulation results show that the method is feasible and effective.
Źródło:: Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e139434
0239-7528
Pojawia się w:: Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Usage of deep learning in recent applications
Autorzy:: Dubey, A.
Powiązania:: https://bibliotekanauki.pl/articles/24200557.pdf
Data publikacji:: 2022
Wydawca:: Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
Tematy:: conceptual based information retrieval
ontology
semantic search
wyszukiwanie informacji oparte na pojęciach
ontologia
wyszukiwanie semantyczne
Opis:: Purpose: Deep learning is a predominant branch in machine learning, which is inspired by the operation of the human biological brain in processing information and capturing insights. Machine learning evolved to deep learning, which helps to reduce the involvement of an expert. In machine learning, the performance depends on what the expert extracts manner features, but deep neural networks are self-capable for extracting features. Design/methodology/approach: Deep learning performs well with a large amount of data than traditional machine learning algorithms, and also deep neural networks can give better results with different kinds of unstructured data. Findings: Deep learning is an inevitable approach in real-world applications such as computer vision where information from the visual world is extracted, in the field of natural language processing involving analyzing and understanding human languages in its meaningful way, in the medical area for diagnosing and detection, in the forecasting of weather and other natural processes, in field of cybersecurity to provide a continuous functioning for computer systems and network from attack or harm, in field of navigation and so on. Practical implications: Due to these advantages, deep learning algorithms are applied to a variety of complex tasks. With the help of deep learning, the tasks that had been said as unachievable can be solved. Originality/value: This paper describes the brief study of the real-world application problems domain with deep learning solutions.
Źródło:: Archives of Materials Science and Engineering; 2022, 115, 2; 49--57
1897-2764
Pojawia się w:: Archives of Materials Science and Engineering
Dostawca treści:: Biblioteka Nauki

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