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knowledge Representation and Reasoning for Complex Time Expression in Clinical Text

data Intelligence 2022年第3期4卷 573-598页

作者： Danyang Hu Meng Wang Feng Gao Fangfang Xu Jinguang Gu School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065China Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content National Press and Publication Administration of the People’s Republic of ChinaBeijing 10038China Institute of Big Data Science and Engineering Wuhan University of Science and TechnologyWuhan 430065China

Temporal information is pervasive and crucial in medical records and other clinical text,as it formulates the development process of medical conditions and is vital for clinical decision ***,providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is *** order to capture complex temporal semantics in clinical text,we propose a novel Clinical Time Ontology(CTO)as an extension from OWL *** specifically,we identified eight timerelated problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time,cyclic time,irregular time,negations and other complex aspects of clinical ***,we extended Allen’s and TEO’s temporal relations and defined the relation concept description between complex and simple ***,we provided a formulaic and graphical presentation of complex time and complex time *** carried out empirical study on the expressiveness and usability of CTO using real-world healthcare ***,experiment results demonstrate that CTO could faithfully represent and reason over 93%of the temporal expressions,and it can cover a wider range of time-related classes in clinical domain.

关键词： Clinical text Temporal ontology Temporal relations OWL Negation of temporal relation

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Realizability of Rectangular Euler Diagrams

arXiv

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arXiv 2024年

作者： Dürrschnabel, Dominik Priss, Uta Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design Kassel Germany Faculty of Computer Science Ostfalia University of Applied Sciences Wolfenbüttel Germany

Euler diagrams are a tool for the graphical representation of set relations. Due to their simple way of visualizing elements in the sets by geometric containment, they are easily readable by an inexperienced reader. Euler diagrams where the sets are visualized as aligned rectangles are of special interest. In this work, we link the existence of such rectangular Euler diagrams to the order dimension of an associated order relation. For this, we consider Euler diagrams in one and two dimensions. In the one-dimensional case, this correspondence provides us with a polynomial-time algorithm to compute the Euler diagrams, while the two-dimensional case is linked to an NP-complete problem which we approach with an exponential-time *** Codes 06A07, 68R05 Copyright © 2024, The Authors. All rights reserved.

关键词： Formal concept analysis

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2018 IEEE Conference on Computational Intelligence and Games (CIG 2018)

2018 IEEE Conference on Computational Intelligence and Games...

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作者： Winands, Mark H. M. Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

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PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering

arXiv

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arXiv 2024年

作者： Xu, Fangzhi Lin, Qika Zhao, Tianzhe Han, Jiawei Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transforming each logical sample into reasoning paths and propose an architecture PathReasoner. It addresses the task from the views of both data and model. To expand the diversity of the logical samples, we propose an atom extension strategy supported by equivalent logical formulas, to form new reasoning paths. From the model perspective, we design a stack of transformer-style blocks. In particular, we propose a path-attention module to joint model in-atom and cross-atom relations with the high-order diffusion strategy. Experiments show that PathReasoner achieves competitive performances on two logical reasoning benchmarks and great generalization abilities. © 2024, CC BY.

关键词： Atoms

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An Empirical Study on the Tourism Image of Nanjing from the Perspective of International Students 7th

An Empirical Study on the Tourism Image of Nanjing from the ...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Han, Pu Zhang, Mingtao Yao, Mingxiu Hui, Zhenrong Chen, Meitong School of Management Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Provincial Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China

ISBN: (纸本)9783030786175

International students are a special group in Chinese cities, and their tourism image perception has important research significance for urban tourism construction. Based on the destination tourism image model proposed by Baloglu and McCleary, the tourism image of Nanjing is divided into three aspects: cognitive image, emotional image and overall image. Aiming at the international student group in Nanjing, the questionnaire survey was used to obtain the perception data of the group’s tourism image in Nanjing with SPSS. The empirical research on the tourism image of Nanjing was carried out from three aspects: tourism image perception factor, overall image perception and representative attractions. The study found that in the structured part of the dimension, international students are most satisfied with "urban environment" and least concerned about "attractions". Through the single factor measurement, the most satisfied one is the rich historical and cultural heritage, and the most dissatisfied one is the crowded urban environment. In the unstructured part of the international students on Nanjing tourism image, the overall satisfaction is high, in which the destination image perception of the "delicious food" is the best. In the perception of urban tourism image, the perception of "a beautiful city" is the best. The analysis of the characteristics of social population structure shows obvious individualized difference. Boys tend to Zhongshan mausoleum, and girls are more inclined to Confucius Temple Qinhuai scenery belt. © 2021, Springer Nature Switzerland AG.

关键词： Surveys

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Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

arXiv

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arXiv 2023年

作者： Xu, Fangzhi Lin, Qika Han, Jiawei Zhao, Tianzhe Liu, Jun Cambria, Erik The School of Computer Science and Technology Xi’an Jiao-tong University Shaanxi Xi’an710049 China The Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering National Engineering Lab for Big Data Analytics Shaanxi Xi’an710049 China The School of Computer Science and Engineering Nanyang Technological University Singapore

Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs) have emerged as a noteworthy innovation in natural language processing (NLP). However, the question of whether LLMs can effectively address the task of logical reasoning, which requires gradual cognitive inference similar to human intelligence, remains unanswered. To this end, we aim to bridge this gap and provide comprehensive evaluations in this paper. Firstly, to offer systematic evaluations, we select fifteen typical logical reasoning datasets and organize them into deductive, inductive, abductive and mixed-form reasoning settings. Considering the comprehensiveness of evaluations, we include 3 early-era representative LLMs and 4 trending LLMs. Secondly, different from previous evaluations relying only on simple metrics (e.g., accuracy), we propose fine-level evaluations in objective and subjective manners, covering both answers and explanations, including answer correctness, explain correctness, explain completeness and explain redundancy. Additionally, to uncover the logical flaws of LLMs, problematic cases will be attributed to five error types from two dimensions, i.e., evidence selection process and reasoning process. Thirdly, to avoid the influences of knowledge bias and concentrate purely on benchmarking the logical reasoning capability of LLMs, we propose a new dataset with neutral content. Based on the in-depth evaluations, this paper finally forms a general evaluation scheme of logical reasoning capability from six dimensions (i.e., Correct, Rigorous, Self-aware, Active, Oriented and No hallucination). It reflects the pros and cons of LLMs and gives guiding directions for future works. © 2023, CC BY.

关键词： Natural language processing systems

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Multi-view FMEA Re-validation: Efficient Risk and engineering knowledge Integration in Agile Production Systems engineering 1

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Model-Driven engineering and Software Development - 9th International Conference, MODELSWARD 2021, and 10th International Conference, MODELSWARD 2022, Revised Selected Papers

作者： Rinker, Felix Kropatschek, Sebastian Steuer, Thorsten Kiesling, Elmar Meixner, Kristof Waltersdorfer, Laura Sommer, Patrik Lüder, Arndt Winkler, Dietmar Biffl, Stefan CDL for Security and Quality Improvement in the Production System Lifecycle TU Wien Vienna Austria Institute of Information Systems Engineering Technische Universität Wien Vienna Austria Center for Digital Production Vienna Austria Institute of Data Process and Knowledge Engineering WU Wien Vienna Austria Neuman Aluminium CAG Holding GmbH Lilienfeld Austria IAF at FMB Otto-v.Gueriche University Magdeburg Germany

ISBN: (数字)9783031388217

ISBN: (纸本)9783031388200

In agile Production Systems engineering (PSE), multi-disciplinary teams work concurrently on various PSE artifacts in an iterative process that can be supported by common concept and Product-Process-Resource (PPR) modeling. However, keeping track of the interactions and effects of changes across engineering disciplines and their implications for risk assessment is exceedingly difficult in such settings. To tackle this challenge and systematically co-evolve Failure Mode and Effects Analysis (FMEA) and PPR models during PSE, it is necessary to propagate and validate changes across engineering artifacts. To this end, we design and evaluate a FMEA-linked-to-PPR assets (FMEA+PPR) meta model to represent relationships between FMEA elements and PSE assets and trace their change states and dependencies in the design and validation lifecycle. Furthermore, we design and evaluate the FMEA+PPR method to efficiently re-validate FMEA models upon changes in multi-view PSE models. We evaluate the model and method in a feasibility study on the quality of a joining process automated by a robot cell in automotive PSE. The study results indicate that the FMEA+PPR method is feasible and addresses requirements for FMEA re-validation better than alternative traditional approaches. Thereby, the FMEA+PPR approach facilitates a paradigm shift from traditional, isolated PSE and FMEA activities towards an integrated agile PSE method. © 2023, Springer Nature Switzerland AG.

关键词： Iterative methods

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Personalized Recommendation Based on Entity Attributes and Graph Features 12

Personalized Recommendation Based on Entity Attributes and G...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Zhu, Yi Dong, Bingbing Sha, Zhiqing Yangzhou University School of Information Engineering Yangzhou China Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institute of Big Knowledge Science Hefei University of Technology Hefei China

ISBN: (纸本)9781665438582

With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the information which users really need on the website. Many recent studies have introduced additional attribute information about users and/or items to the rating matrix for alleviating the problem of data sparsity. In order to make full use of the attribute information and scoring matrix, deep learning based recommendation methods are proposed, especially the autoencoder model has attracted much attention because of its strong ability to learn hidden features. However, most of the existing autoencoder- based models require that the dimension of the input layer is equal to the dimension of the output layer, which may increase model complexity and certain information loss when using attribute information. In addition, as users' awareness of privacy protection increases, user attribute information is difficult to obtain. To address the above problems, in this paper, we propose a hybrid personalized recommendation model, which uses a semi-autoencoder to jointly embed the item's score vector and internal graph features (short for Co-Agpre). Specifically, we regard the user-item historical interaction matrix as a bipartite graph, and the Laplacian of the user-item co-occurrence graph is utilized to obtain the graph features of the item for solving the problem of sparse attributes. Then a semi-autoencoder is introduced to learn the hidden features of the item and perform rating prediction. The proposed model can flexibly use information from different sources to reduce the complexity of the model. Experiments on two real-world datasets demonstrate the effectiveness of the proposed Co-Agpre compared with state-of-the-art methods. © 2021 IEEE.

关键词： Websites

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Deep learning for robust end-to-end tone mapping 30

Deep learning for robust end-to-end tone mapping

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30th British Machine Vision Conference, BMVC 2019

作者： Montulet, Rico Briassouli, Alexia Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

Low-light images require localised processing to enhance details, contrast and lighten dark regions without affecting the appearance of the entire image. A range of tone mapping techniques have been developed to achieve this, with the latest state-of-the-art methods leveraging deep learning. In this work, a new end-to-end tone mapping approach based on Deep Convolutional Adversarial Networks (DCGANs) is introduced along with a data augmentation technique, and shown to improve upon the latest state-of-the-art on benchmarking datasets. We carry out comparisons using the MIT-Adobe FiveK (MIT-5K) and the LOL datasets, as they provide benchmark training and testing data, which is further enriched with data augmentation techniques to increase diversity and robustness. A U-net is used in the generator and a patch-GAN in the discriminator, while a perceptually-relevant loss function based on VGG is used in the generator. The results are visually pleasing, and shown to improve upon the state-of-the-art Deep Retinex, Deep Photo Enhancer and GLADNet on the most widely used benchmark dataset MIT-5K and LOL, without additional computational requirements. © 2019. The copyright of this document resides with its authors.

关键词： Image enhancement

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Towards Approximating Personality Cues Through Simple Daily Activities 20th

Towards Approximating Personality Cues Through Simple Daily ...

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20th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2020

作者： Gibellini, Francesco Higler, Sebastiaan Lucas, Jan Luli, Migena Stallmann, Morris Dotti, Dario Asteriadis, Stylianos Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9783030406042

The goal of this work is to investigate the potential of making use of simple activity and motion patterns in a smart environment for approximating personality cues via machine learning techniques. Towards this goal, we present a novel framework for personality recognition, inspired by both Computer Vision and Psychology. Results show a correlation between several behavioral features and personality traits, as well as insights of which type of everyday tasks induce stronger personality display. We experiment with the use of Support Vector Machines, Random Forests and Gaussian Process classification achieving promising predictive ability, related to personality traits. The obtained results show consistency to a good degree, opening the path for applications in psychology, game industry, ambient assisted living, and other fields. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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