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30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Sironi, Chiara F. Winands, Mark H.M. Games and AI Group Department of Data Science and Knowledge Engineering Maastricht University Netherlands

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Generative-Contrastive Heterogeneous Graph Neural Network

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IEEE Transactions on Big data 2025年

作者： Wang, Yu Sang, Lei Zhang, Yi Zhang, Yiwen Wu, Xindong Anhui University School of Computer Science and Technology Anhui Hefei 230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data (The Ministry of Education of China) Anhui Hefei 230601 China

Heterogeneous Graphs (HGs) effectively model complex relationships in the real world through multi-type nodes and edges. In recent years, inspired by self-supervised learning (SSL), contrastive learning (CL)-based Heterogeneous Graphs Neural Networks (HGNNs) have shown great potential in utilizing data augmentation and contrastive discriminators for downstream tasks. However, data augmentation remains limited due to the graph data's integrity. Furthermore, the contrastive discriminators suffer from sampling bias and lack local heterogeneous information. To tackle the above limitations, we propose a novel Generative-Contrastive Heterogeneous Graph Neural Network (GC-HGNN). Specifically, we propose a heterogeneous graph generative learning method that enhances CL-based paradigm. This paradigm includes: 1) A contrastive view augmentation strategy using a masked autoencoder. 2) Position-aware and semantics-aware positive sample sampling strategy for generating hard negative samples. 3) A hierarchical contrastive learning strategy aimed at capturing local and global information. Furthermore, the hierarchical contrastive learning and sampling strategies aim to constitute an enhanced contrastive discriminator under the generative-contrastive perspective. Finally, we compare our model with seventeen baselines on eight real-world datasets. Our model outperforms the latest baselines on node classification and link prediction tasks. To reproduce our work, we have open-sourced our code at https://***/wangyu0627/GC-HGNN. © 2015 IEEE.

关键词： Contrastive Learning Generative Learning Heterogeneous Graph Neural Networks Self-Supervised Learning

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WATuning:A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning

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Journal of Computer science & Technology 2021年第4期36卷 741-761页

作者： Jia-Ke Ge Yan-Feng Chai Yun-Peng Chai Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China College of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030027China

Configuration tuning is essential to optimize the performance of systems(e.g.,databases,key-value stores).High performance usually indicates high throughput and low *** present,most of the tuning tasks of systems are performed artificially(e.g.,by database administrators),but it is hard for them to achieve high performance through tuning in various types of systems and in various *** recent years,there have been some studies on tuning traditional database systems,but all these methods have some *** this article,we put forward a tuning system based on attention-based deep reinforcement learning named WATuning,which can adapt to the changes of workload characteristics and optimize the system performance efficiently and ***,we design the core algorithm named ATT-Tune for WATuning to achieve the tuning task of *** algorithm uses workload characteristics to generate a weight matrix and acts on the internal metrics of systems,and then ATT-Tune uses the internal metrics with weight values assigned to select the appropriate ***,WATuning can generate multiple instance models according to the change of the workload so that it can complete targeted recommendation services for different types of ***,WATuning can also dynamically fine-tune itself according to the constantly changing workload in practical applications so that it can better fit to the actual environment to make *** experimental results show that the throughput and the latency of WATuning are improved by 52.6%and decreased by 31%,respectively,compared with the throughput and the latency of CDBTune which is an existing optimal tuning method.

关键词： attention mechanism auto-tuning system reinforcement learning(RL) workload-aware

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Carbon-doped percentage effect on the mechanical properties of nanoporous silicon sample using molecular dynamics simulation

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Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Sawaran Singh, Narinderjit Singh Ali, Ali B.M. Ameen, Hawzhen Fateh M. Al-Zahiwat, Murtadha M. Salahshour, Soheil Emami, Nafiseh Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Petroleum Technology Erbil Technology College Erbil Polytechnic University Kurdistan Region Erbil Iraq Department of Petroleum Engineering Knowledge University Erbil Iraq Department of Chemical engineering College of Engineering University of Misan Amarah Iraq Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Türkiye Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Türkiye Research Center of Applied Mathematics Khazar University Baku Türkiye Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Porous materials have attracted considerable attention from researchers due to its many uses in molecular separation, heterogeneous catalysis, absorption technologies, and electronic improvements. These solid materials, often defined by their structural voids, are essential in several sectors. This research investigated the impact of carbon doping on the mechanical characteristics of nanoporous silicon matrices. The use of high-purity silicon doping is very beneficial in the semiconductor industry and is crucial for high-power devices and automotive applications. This study simulates a nanoporous silicon sample by molecular dynamics methods, adding carbon doping at different concentrations. The findings demonstrate that when the carbon doping concentration escalated from 1 % to 30 %, the mechanical resistance of the system decreased correspondingly. The ultimate tensile strength fell from 10.26 to 9.02 GPa. Furthermore, Young's modulus rose from 83.47 to 98.37 GPa. The decline in mechanical stability was associated with a drop in the model's total weight, which had considerable ramifications for industrial applications. Thus, incorporating C-doped nanoporous silicon into real applications not only lowered the weight of target materials but also improved their use. © 2025 The Author(s)

关键词： Carbon doping Mechanical properties Molecular dynamics simulation Nanoporous Porous materials

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CLASCN： Candidate Network Selection for Efficient Top-κ Keyword Queries over databases

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Journal of Computer science & Technology 2007年第2期22卷 197-207页

作者：张俊彭朝晖王珊聂惠静 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China Computer Science and Technology College Dalian Maritime University Dalian 116026 China

Keyword Search Over Relational databases （KSORD） enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN （Classification, Learning And Selection of Candidate Network） is developed to efficiently perform top-κ keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks （CNs） from trained keyword queries or executed user queries are classified and stored in the databases, and top-κ results from the CNs are learned for constructing CN Language Models （CNLMs）. The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-κ results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used （NAU） queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.

关键词： relational database keyword search top-κ query candidate network

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Evaluation of ontology mapping representations

Evaluation of ontology mapping representations

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23rd Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB 2009

作者： Thomas, Hendrik O'Sullivan, Declan Brennan, Rob Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (纸本)1902956850

A common approach to mitigate the effects of ontology heterogeneity is to discover and express the specific correspondences between different ontologies. An open research question is: how should such ontology mappings be represented. In recent years several proposals for an ontology mapping representation have been published, but till today no format is officially standardized or generally accepted in the community. In this paper we will present a new evaluation framework for ontology mapping representations for a pragmatic state of the art overview of their characteristics. In particular we are interested how current ontology mapping representations can support the management of ontology mappings (sharing, re-use, alteration) as well as how suitable they are for different mapping tasks.

关键词： Ontology

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AMASE: A framework for composing adaptive and personalised learning activities on the web

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11th International Conference on Advances in Web-Based Learning, ICWL 2012

作者： Staikopoulos, Athanasios O'Keeffe, Ian Rafter, Rachael Walsh, Eddie Yousuf, Bilal Conlan, Owen Wade, Vincent Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (纸本)9783642336416

Personalised Web information systems have in recent years been evolving to provide richer and more tailored experiences for users than ever before. In order to provide even more interactive experiences as well as to address new opportunities, the next generation of Personalised Web information systems needs to be capable of dynamically personalising not just web media but web services as well. In particular, eLearning provides an example of an application domain where learning activities and personalisation are of significant importance for engaging and enhancing the learning experience of a learner. This paper presents a novel approach and technical framework called AMASE to support the dynamic generation and enactment of Personalised Learning Activities, which uniquely entails the personalisation of media content and the personalisation of services in a unified manner. In doing so, AMASE combines state of the art techniques from both adaptive web and adaptive workflow systems. © 2012 Springer-Verlag.

关键词： Web services

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Conformal Stacked Weather Forecasting 7

Conformal Stacked Weather Forecasting

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7th Workshop on Conformal and Probabilistic Prediction and Applications, COPA 2018

作者： Neeven, Jelmer Smirnov, Evgueni Department of Data Science and Knowledge Engineering Maastricht University Maastricht6200MD Netherlands

In this paper we propose to apply the stacking method to aggregating multi-output predictions from different weather-forecasting domains (websites). Depending on the aggregating procedure (non-conformal/conformal), the results can be bare multi-output predictions or multi-output prediction regions. The experiments show the applicability of the stacking method on real data related to eight weather-forecasting domains. © 2018 J. Neeven & E. Smirnov.

关键词： Weather forecasting

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Personalisation in the Wild - Providing Personalisation across Semantic, Social and Open-Web Resources 11

Personalisation in the Wild - Providing Personalisation acro...

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Proceedings of the 22nd ACM conference on Hypertext and hypermedia

作者： Steichen, Ben O'Connor, Alexander Wade, Vincent Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (纸本)9781450302562

One of the key motivating factors for information providers to use personalisation is to maximise the benefit to the user in accessing their content. However, traditionally such systems have focussed on mainly corporate or professionally authored content and have not been able to leverage the benefits of other material already on the web, written about that subject by other authors. Such information includes open-web information as well as usergenerated content such as forums, blogs, tags, etc. By providing personalised compositions and presentations across these heterogeneous information sources, a potentially richer user experience can be created, leveraging the mutual benefits of professionally authored content as well as open-web information and active user communities. This paper presents novel techniques and architectures that extend the personalisation reserved for corporate or professionally developed content with that of user generated content and pages in the wild. Complementary affordances of Personalised Information Retrieval and Adaptive Hypermedia are leveraged in order to provide Adaptive Retrieval and Composition of Heterogeneous INformation sources for personalised hypertext Generation (ARCHING). The approach enables adaptive selection and navigation according to multiple adaptation dimensions and across a variety of heterogeneous data sources. The architectures have been applied in a real-life personalised customer care scenario and a user study evaluation involving authentic information needs has been conducted. The evidence clearly shows that the system successfully blends a user's search experience with adaptive selection and navigation techniques and that the user experience is improved in terms of both task assistance and user satisfaction. © 2011 ACM.

关键词： Semantics

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Evaluation of a semantic-oriented approach to cross-lingual ontology mapping

Evaluation of a semantic-oriented approach to cross-lingual ...

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EKAW 2010 Poster and Demo Track

作者： Fu, Bo Brennan, Rob O'Sullivan, Declan Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

Most ontology mapping research has focused on the matching of ontologies written in the same natural language, and developing tools and techniques that support this monolingual ontology mapping process. However, as knowledge modelling is not restricted to the usage of a single natural language, mapping systems must be able to operate upon ontologies that are labelled in diverse natural languages. This paper outlines a semanticoriented cross-lingual ontology mapping framework that makes use of several information sources to influence the selection of ontology label translations in the process of generating high quality mapping results, and presents a high-level overview of the evaluation strategy of the proposed framework.

关键词： Ontology

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