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A quantum‐like approach for text generation from knowledge graphs

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1455-1463页

作者： Jia Zhu Xiaodong Ma Zhihao Lin pasquale de meo Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhuaChina School of Computer Science South China Normal UniversityGuangzhouChina DICAM department University of MessinaMessinaItaly

Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation,such as knowledge graphs.Since all nodes are connected directly,node global representation encoding enables direct communication between two distant nodes while disregarding graph topology.Node local representation encoding,which captures the graph structure,considers the connections between nearby nodes but misses out onlong‐range relations.A quantum‐like approach to learning bettercontextualised node embeddings is proposed using a fusion model that combines both encoding strategies.Our methods significantly improve on two graph‐to‐text datasets compared to state‐of‐the‐art models in various experiments.

关键词： data mining knowledge‐based vision machine learning natural language processing text analysis

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Guest Editorial: Special issue on machine learning and deep learning algorithms for complex networks

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 1-2页

作者： pasquale de meo Qun Jin Jianguo Yao Michael Sheng department of Ancient and Modern Civilizations University of MessinaMessinaItaly department of Human Informatics and Cognitive Sciences Faculty of Human SciencesWaseda UniversityTokyoJapan School of Software Shanghai Jiao Tong UniversityShanghaiChina School of Computing Macquarie UniversitySydneyAustralia

In the latest years,researchers from the industry and academia extensively applied machine learning algorithms in a broad range of domains.The goal of this special issue is to illustrate the most recent applications of deep learning methods in a range of real-life domains and to show the practical utility of these techniques.A particular attention goes towards methods to process network data that is capable of modelling complex artificial and natural systems as the interactions of a multitude of simpler entities.

关键词： learning simpler illustrate

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A MULTI-AGENT SYSTEM FOR MANAGING THE QUALITY OF SERVICE IN TELECOMMUNICATIONS NETWORKS

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Journal of Systems Science and Systems Engineering 2005年第2期14卷 129-158页

作者： pasquale de meo Antonio IERA Giorgio TERRACINA Domenico URSINO Università ''Mediterranea'' di Reggio Calabria Via Graziella Località Feo di Vito89060 Reggio CalabriaItaly Dipartimento di Matematica Università della Calabria Via Pietro Bucci87036 Rende (CS)Italy

In this paper we propose X-MAQoS, a novel XML-based multi-agent system for the QoS management in telecommunications networks. This system is characterized by the following features: (i) it handles a user profile and exploits it jointly with suitable network resource management techniques to maximize user satisfaction; (ii) it is capable of operating in a large variety of telecommunications networks; (iii) it is semi-automatic; (iv) it exploits XML for guaranteeing a light, versatile and standard mechanism for information representation, storing and exchange. In this paper the basic features of the system are discussed in details. Furthermore, the main results of a performance evaluation study in UMTS environment, aiming at comparing X-MAQoS with alternative agent-based approaches for handling user access to telecommunications networks, are reported.

关键词： Intelligent agents XML QoS management in telecommunications Networks

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