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Application of Artificial Intelligence Technology in Big Data Nining 6th

Application of Artificial Intelligence Technology in Big Dat...

引用

6th International Conference on Innovative computing, IC 2023

作者： Zhou, Xueyun Qi, Yongjun Tang, Hailin Department of Computer Science and Engineering Guangzhou College of Technology and Business Guangdong510850 China Faculty of Metadata and Computing Guangdong Baiyun University Guangdong510450 China Mongolian University of Science and Technology Bayanzurkh District Ulaanbaatar13341 Mongolia

ISBN: (纸本)9789819920914

Data mining is the process of expressing knowledge through data collection and processing. This is an important technique for efficiently extracting batch data. Artificial intelligence (AI) is a simulation technology of the human brain. It makes full use of computer technology and other subject technologies for intelligent learning and machine control. Both are advanced data management technologies, and both have broad application prospects. This article mainly introduces the clustering algorithm and TextRank algorithm. This paper uses AI technology to conduct research and application in big data mining (BDM), and establishes a mathematical model of the underlying TextRank algorithm. The model is solved by the TextRank algorithm, and the status of research and application of AI technology in BDM is assessed,. Experimental results show that the algorithm improves the research and application of embedded big data intelligent technology by 55% and reduces false positive rate and false positive percentage. Finally, by comparing the research and application analysis of artificial intelligence technology in large data storage. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Toward a Comparison Framework for Interactive Ontology Enrichment Methodologies 7

Toward a Comparison Framework for Interactive Ontology Enric...

引用

7th International Workshop on the Visualization and Interaction for Ontologies and Linked Data, VOILA! 2022

作者： Vrolijk, Jarno Reklos, Ioannis Vafaie, Mahsa Massari, Arcangelo Mohammadi, Maryam Rudolph, Sebastian Plantage Muidergracht 12 Amsterdam1018 TV Netherlands Department of Informatics King's College London Bush House 30 Aldwych LondonWC2B 4BG United Kingdom FIZ Karlsruhe - Leibniz Institute for Information Infrastructure Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Research Centre for Open Scholarly Metadata Department of Classical Philology and Italian Studies University of Bologna Bologna Italy Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Minderbroedersberg 4-6 Maastricht6211 LK Netherlands Computational Logic Group TU Dresden Dresden01062 Germany

The growing demand for well-modeled ontologies in diverse application areas increases the need for intuitive interaction techniques that support human domain experts in ontology modeling and enrichment tasks, such that quality expectations are met. Beyond the correctness of the specified information, the quality of an ontology depends on its (relative) completeness, i.e., whether the ontology contains all the necessary information to draw expected inferences. On an abstract level, the Ontology Enrichment problem consists of identifying and filling the gap between information that can be logically inferred from the ontology and the information expected to be inferable by the user. To this end, numerous approaches have been described in the literature, providing methodologies from the fields of Formal Semantics and Automated Reasoning targeted at eliciting knowledge from human domain experts. These approaches vary greatly in many aspects and their applicability typically depends on the specifics of the concrete modeling scenario at hand. Toward a better understanding of the landscape of methodological possibilities, this position paper proposes a framework consisting of multiple performance dimensions along which existing and future approaches to interactive ontology enrichment can be characterized. We apply our categorization scheme to a selection of methodologies from the literature. In light of this comparison, we address the limitations of the methods and propose directions for future work. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Ontology

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