Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

A novel approach of voterank-based knowledge graph for improvement of multi-attributes influence nodes on social networks

Pham Hai Van, Duong Pham Van, Tran Dinh Tuan, Lee Joo-Ho

Journal of Artificial Intelligence and Soft Computing Research

2023

Vol. 13. No. 3

165--180

Recently, measuring users and community influences on social media networks play significant roles in science and engineering. To address the problems, many researchers have investigated measuring users with these influences by dealing with huge data sets. However, it is hard to enhance the performances of these studies with multiple attributes together with these influences on social networks. This paper has presented a novel model for measuring users with these influences on a social network. In this model, the suggested algorithm combines Knowledge Graph and the learning techniques based on the vote rank mechanism to reflect user interaction activities on the social network. To validate the proposed method, the proposed method has been tested through homogeneous graph with the building knowledge graph based on user interactions together with influences in realtime. Experimental results of the proposed model using six open public data show that the proposed algorithm is an effectiveness in identifying influential nodes.

A New Approach of Question Answering based on Knowledge Graph in Traditional Medicine

Duong Pham Van, Trinh Tien-Dat, Pham Hai Van, Tuan Tran Manh, Son Le Hoang, Vu Huy-The, Nguyen Minh-Tien, Chuan Pham Minh

Annals of Computer Science and Information Systems

2022

Vol. 33

285--288

In recent years, it has been great interest for Question Answering (QA) systems applied to many areas placing a high value on the community. The study and development of such QA systems through chatbot tools in medicine raise great needs for clinicians in their daily activities. Chatbots use the knowledge that could be retrieved from a database, but with limited inference capability. In this paper, we propose a new QA system based on Knowledge Graph (knowledge graph) for Traditional Medicine. Data of the knowledge graph is obtained from two sources including those from diagnostic of treatment diagrams and those collected on well-known medical websites through the Internet. The knowledge graph is then formed by combining the entities and relationships using the Named Entity Recognition (NER) model. Diagnosis is made via the node similarity algorithm in the knowledge graph for symptom identification. The effectiveness of the system is demonstrated through theoretical analysis and real-world experimental outcomes.