Bilingual Publishing Group

Machine Learning Meets the Semantic Web DOI:  https://doi.org/10.30564/aia.v3i1.3178 Abstract Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting valuable external knowledge in various domains. A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes. The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs. This paper presents how Machine Learning (ML) meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning. The paper also highlights important aspects of this area of research, discussing open issues such as the bias hidden in KGs at different levels of graph representation. Keywords:  Knowledge graph, Semantic web, Ontology, Machine learning, Deep learning, Graph neural networks

An Ontology-based Ranking Model in Search Engines DOI:  https://doi.org/10.30564/jcsr.v1i2.972 Abstract As the tsunami of data has emerged, search engines have become the most powerful tool for obtaining scattered information on the internet. The traditional search engines return the organized results by using ranking algorithm such as term frequency, link analysis (PageRank algorithm and HITS algorithm) etc. However, these algorithms must combine the keyword frequency to determine the relevance between user’s query and the data in the computer system or internet. Moreover, we expect the search engines could understand users’ searching by content meanings rather than literal strings. Semantic Web is an intelligent network and it could understand human’s language more semantically and make the communication easier between human and computers. But, the current technology for the semantic search is hard to apply. Because some meta data should be annotated to each web pages, then the s...