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Using the OM2R meta-data model for ontology mapping reuse for the ontology alignment challenge - A case study

Using the OM2R meta-data model for ontology mapping reuse fo...

7th International Workshop on Ontology Matching, OM 2012 - Collocated with the 11th International Semantic Web Conference, ISWC 2012

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

Ontology matching and mapping is of critical importance to effective consumption of distributed and heterogeneous data-sets in today's Web of data. Since 2004 the Ontology Alignment Evaluation Initiative (OAEI) provides a number of complex challenges to evaluate the performance of the increasing number of matching tools and methods. This leads to the question how the individual OAEI challenges and the individual alignment results can be documented best for effective online consumption, management and further analysis. In this paper, we argue that the current documentation of alignment creation lifecycle aspects within OAEI would benefit from more formal model support. In this paper we present a case study to show how our ontology-based meta-data model for ontology mapping reuse (OM2R) can be applied for the OAEI to document alignment challenges and some quantification on the likely benefits in terms of helping challenge administrators and participants create consistent documentation in terms of high correctness and less inconsistent statements as well as results that are explicit, predictable and easy to interpret.

关键词： Metadata

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Fine-Grained Cross-Modal Fusion Based Refinement for Text-to-Image Synthesis

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Chinese Journal of Electronics 2023年第6期32卷 1329-1340页

作者： SUN Haoran WANG Yang LIU Haipeng QIAN Biao Department of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of EducationHefei University of Technology

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel finegrained text-image fusion based generative adversarial networks(FF-GAN), which consists of two modules: Finegrained text-image fusion block(FF-Block) and global semantic refinement(GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.

关键词： Visualization Fuses Convolution Semantics Linguistics Benchmark testing Generative adversarial networks

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Domain decomposition and mortar mixed approach for nonlinear elliptic equations modeling flow in porous media

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Heliyon 2025年第4期11卷 e42724页

作者： Arshad, Muhammad Mehmood, Adil Salleh, Zabidin Akhtar, Sumaira Saleem Khan, Suliman Inc, Mustafa Department of Mathematics Abbottabad University of Science and Technology Abbottabad Pakistan Special Interest Group on Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21030 Malaysia Department of Mathematics Women University Mardan Khyber Pakhtunkhwa Pakistan State Key Laboratory of Mechanics and Control for Aerospace Structures College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Department of Mathematıcs Firat University Elazig 23119 Türkiye Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai 602105 India Department of Computer Engineering Biruni University Istanbul 34010 Türkiye

The equations describe the behavior of steady state flow in porous medium generally results in elliptic partial differential equations with coefficient represents the permeability of the medium. This article presents the extension of mortar mixed method for second order nonlinear elliptic equations that describes flow in porous media. The domain is decomposed into non-overlapping regions with each partitioned independently. The grids on subdomains are allowed to be non-matching across the subdomains internal boundaries. The fixed point argument (FPA) is employed to establish the existence and uniqueness of discrete problem, and optimal order error estimates are provided for approximations. The computational results are given to validate the theory. © 2025 The Author(s)

关键词： Darcy law Existence Mixed methods Mortar methods Nonlinear equations Numerical results

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A review of neuro-symbolic AI integrating reasoning and learning for advanced cognitive systems

Intelligent Systems with Applications

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Intelligent Systems with Applications 2025年 26卷

作者： Uzma Nawaz Mufti Anees-ur-Rahaman Zubair Saeed Knowledge and Data Science Research Centre Department of Computer and Software Engineering College of Electrical and Mechanical Engineering National University of Science and Technology Islamabad Pakistan Military College of Signals National University of Science and Technology Islamabad Pakistan Department of Electrical & Computer Engineering Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar

Neuro-symbolic AI represents the convergence of two principal paradigms in artificial intelligence: neural networks, which are efficient in data-driven learning, and symbolic reasoning, which offers explainability and logical inference. This hybrid methodology combines the adaptability of neural networks with symbolic AI's interpretability and formal reasoning abilities, which provide a practical framework for advanced cognitive systems. This paper analyzes the present condition of neuro-symbolic AI, emphasizing essential techniques that combine reasoning and learning. We explore models such as Logic Tensor Networks, Differentiable Logic Programs, and Neural Theorem Provers. The study analyzes their impact on the advancement of cognitive systems in natural language processing, robotics, and decision-making. The paper examines the challenges faced by neuro-symbolic AI, such as scalability, integration with multimodal data, and maintaining interpretability without compromising efficiency. By evaluating the strengths and weaknesses of many methodologies, we comprehensively understand the field's development and its potential to revolutionize intelligent systems. In addition, we identify emerging research areas, including the incorporation of ethical frameworks and the development of adaptive dynamic neuro-symbolic systems that respond in real-time. This review aims to guide future research by providing insights into the potential of neuro-symbolic AI to influence the development of the next generation of intelligent, explainable, and adaptive systems.

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A Dynamic Transformer Network for Vehicle Detection

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

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The narrative approach to personalisation

The narrative approach to personalisation

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作者： Conlan, Owen Staikopoulos, Athanasios Hampson, Cormac Lawless, Séamus O'Keeffe, Ian Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin Ireland

This article describes the narrative approach to personalisation. This novel approach to the generation of personalised adaptive hypermedia experiences employs runtime reconciliation between a personalisation strategy and a number of contextual models (e.g. user and domain). The approach also advocates the late binding of suitable content and services to the generated personalised pathway resulting in an interactive composition that comprises services as well as content. This article provides a detailed definition of the narrative approach to personalisation and showcases the approach through the examination of two use-cases: the personalised digital educational games developed by the ELEKTRA and 80Days projects;and the personalised learning activities realised as part of the AMAS project. These use-cases highlight the general applicability of the narrative approach and how it has been applied to create a diverse range of real-world systems. © 2013 Taylor and Francis group, LLC.

关键词： Hypermedia systems

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A web statistics based conflation approach to improve Arabic text retrieval

A web statistics based conflation approach to improve Arabic...

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2011 Federated Conference on computer science and Information Systems, FedCSIS 2011

作者： Ahmed, Farag Nürnberger, Andreas Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University of Magdeburg Germany

ISBN: (纸本)9781457700415

We present a language independent approach for conflation that does not depend on predefined rules or prior knowledge of the target language. The proposed unsupervised method is based on an enhancement of the pure n-gram model that is used to group related words based on a revised string-similarity measure. In order to detect and eliminate terms that are created by this process, but that are most likely not relevant for the query ("noisy terms"), an approach based on mutual information scores computed based on web statistical cooccurrences data is proposed. Furthermore, an evaluation of this approach is presented. © 2011 Polish Info Processing Soc.

关键词： Information use

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Evolving search user interfaces 3

Evolving search user interfaces

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3rd European Workshop on Human-computer Interaction and Information Retrieval, EuroHCIR 2013 - Co-located with the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013

作者： Gossen, Tatiana Nitsche, Marcus Nürnberger, Andreas Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany

When designing search user interfaces (SUIs), there is a need to target specific user groups. The cognitive abilities, fine motor skills, emotional maturity and knowledge of a sixty years old man, a fourteen years old teenager and a seven years old child differ strongly. These abilities influence the decisions made in the user interface (UI) design process of SUIs. Therefore, SUIs are usually designed and optimized for a certain user group. However, especially for young and elderly users, the design requirements change rapidly due to fast changes in users' abilities, so that a flexible modification of the SUI is needed. In this positional paper we introduce the concept of an evolving search user interface (ESUI). It adapts the UI dynamically based on the derived capabilities of the user interacting with it. We elaborate on user characteristics that change over time and discuss how each of them can influence the SUI design using an example of a girl growing from six to fourteen. We discuss the ways to detect current user characteristics. We also support our idea of an ESUI with a user study and present its first results. Copyright © 2013 for the individual papers by the papers' authors.

关键词： User interfaces

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Semantic interoperation to support context in adaptive applications

Semantic interoperation to support context in adaptive appli...

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2008 IEEE Network Operations and Management Symposium Workshops - NOMS 08

作者： O'Connor, Alexander Wade, Vincent Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin 2 Ireland

ISBN: (纸本)9781424420674

The challenge of supporting Ubiquitous Computing is to assist the user by helping their applications make better decisions with better information. Smart spaces and other information sources can provide this information, but it is unrealistic to expect all the information to be represented in the same way everywhere. This means that the goal of ubiquitous computing translates into a problem of information integration. This paper presents a new knowledge-driven approach to applying contextual information to user applications. The objective of the design is to use a semantic integration, represented in topic maps, to deliver knowledge found in available services to personalise and improve the functionality of an integrated application. A brief discussion of the different semantic technologies employed in the system is followed by a presentation of the architecture of the system and its novel combination of semantic technologies. Two Use Cases are discussed, one specifically related to Ubiquitous Environments. Finally, the work is related to other research in the areas of Semantic Mediation and Context Awareness. © 2008 IEEE.

关键词： Ubiquitous computing

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Mapping representation based on meta-data and spin for localization workflows 2

Mapping representation based on meta-data and spin for local...

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Joint 2nd International Workshop on Semantic Web Enterprise Adoption and Best Practice, WaSABi 2014 and 2nd International Workshop on Finance and Economics on the Semantic Web, FEOSW 2014 - Co-located with 11th European Semantic Web Conference, ESWC 2014

作者： Meehan, Alan Brennan, Rob Lewis, Dave O'Sullivan, Declan CNGL Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland

The localization industry currently deploys language translation workflows based on heterogeneous tool-chains. Standardized tool interchange formats such as XLIFF (XML Localization Interchange File Format) have had some impact on enabling more agile translation workflows. However the rise of new tools based on machine translation technology and the growing demand for enterprise linked data applications has created new interoperability challenges as workflows need to encompass a broader range of tools. In this paper we present an approach of representing mappings between RDF-based representations of multilingual content and meta-data. To represent the mappings, we use a combination of SPARQL Inferencing Notation (SPIN) and meta-data. Our approach allows the mapping representation to be published as Linked data. In contrast to other frameworks such as R2R, the mappings are executed via a standard SPARQL processor. The objective is to provide a more agile approach to translation workflows and greater interoperability between software tools by leveraging the ongoing innovation in the Multilingual Web field. Our use case is a Language Technology retraining workflow where publishing mappings leads to new opportunities for interoperability and end-to-end tool-chain analytics. We present the results from an initial experiment which compared our approach of executing and representing mappings to that of a similar approach - The R2R Framework.

关键词： Mapping

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