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Triadic Exploration and Exploration with Multiple Experts 16th

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Triadic Exploration and Exploration with Multiple Experts

16th International Conference on Formal Concept Analysis, ICFCA 2021

作者： Felde, Maximilian Stumme, Gerd Knowledge and Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Kassel Germany

ISBN: (纸本)9783030778668

Formal Concept Analysis (FCA) provides a method called attribute exploration which helps a domain expert discover structural dependencies in knowledge domains that can be represented by a formal context (a cross table of objects and attributes). Triadic Concept Analysis is an extension of FCA that incorporates the notion of conditions. Many extensions and variants of attribute exploration have been studied but only few attempts at incorporating multiple experts have been made. In this paper we present triadic exploration based on Triadic Concept Analysis to explore conditional attribute implications in a triadic domain. We then adapt this approach to formulate attribute exploration with multiple experts that have different views on a domain. © 2021, Springer Nature Switzerland AG.

关键词： Formal concept analysis

Force-Directed Layout of Order Diagrams Using Dimensional Reduction 16th

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Force-Directed Layout of Order Diagrams Using Dimensional Re...

16th International Conference on Formal Concept Analysis, ICFCA 2021

作者： Dürrschnabel, Dominik Stumme, Gerd Knowledge and Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Kassel Germany

ISBN: (纸本)9783030778668

Order diagrams allow human analysts to understand and analyze structural properties of ordered data. While an expert can create easily readable order diagrams, the automatic generation of those remains a hard task. In this work, we adapt force-directed approaches, which are known to generate aesthetically-pleasing drawings of graphs, to the realm of order diagrams. Our algorithm ReDraw thereby embeds the order in a high dimension and then iteratively reduces the dimension until a two-dimensional drawing is achieved. To improve aesthetics, this reduction is equipped with two force-directed steps where one step optimizes the distances of nodes and the other one the distances of lines in order to satisfy a set of a priori fixed conditions. By respecting an invariant about the vertical position of the elements in each step of our algorithm we ensure that the resulting drawings satisfy all necessary properties of order diagrams. Finally, we present the results of a user study to demonstrate that our algorithm outperforms comparable approaches on drawings of lattices with a high degree of distributivity. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

Towards explainable scientific venue recommendations

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arXiv 2021年

作者： Schaefermeier, Bastian Stumme, Gerd Hanika, Tom Knowledge and Data Engineering Group University of Kassel

Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the continuous emergence of additional venues. Previously proposed approaches for supporting authors in this process rely on complex recommender systems, e.g., based on Word2Vec or TextCNN. These, however, often elude an explanation for their recommendations. In this work, we propose an unsophisticated method that advances the state-of-the-art in two aspects: First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models;Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Recommender systems

Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education 25

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Exploring Heterogeneity and Uncertainty for Graph-based Cogn...

Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Pengyang Shao Yonghui Yang Chen Gao Lei Chen Kun Zhang Chenyi Zhuang Le Wu Yong Li Meng Wang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei Anhui China BNRist Tsinghua University Beijing China Department of Electronic Engineering BNRist Tsinghua University Beijing China Ant Group HangZhou Zhejiang China

ISBN: (纸本)9798400712456

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students' proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence ***, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD.

关键词： cognitive diagnosis

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Scaling Dimension

arXiv 2023年

作者： Ganter, Bernhard Hanika, Tom Hirth, Johannes TU Dresden Dresden Germany Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany

Conceptual Scaling is a useful standard tool in Formal Concept Analysis and beyond. Its mathematical theory, as elaborated in the last chapter of the FCA monograph, still has room for improvement. As it stands, even some of the basic definitions are in flux. Our contribution was triggered by the study of concept lattices for tree classifiers and the scaling methods used there. We extend some basic notions, give precise mathematical definitions for them and introduce the concept of scaling dimension. In addition to a detailed discussion of its properties, including an example, we show theoretical bounds related to the order dimension of concept lattices. We also study special subclasses, such as the ordinal and the interordinal scaling dimensions, and show for them first results and *** Codes 03G10 68T27 68T30 06B10 06A15 Copyright © 2023, The Authors. All rights reserved.

关键词： Formal concept analysis

What is the intrinsic dimension of your binary data? - and how to compute it quickly

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arXiv 2024年

作者： Hanika, Tom Hille, Tobias Intelligent Information Systems University of Hildesheim Hildesheim Germany Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Kassel Germany

Dimensionality is an important aspect for analyzing and understanding (high-dimensional) data. In their 2006 ICDM paper Tatti et al. answered the question for a (interpretable) dimension of binary data tables by introducing a normalized correlation dimension. In the present work we revisit their results and contrast them with a concept based notion of intrinsic dimension (ID) recently introduced for geometric data sets. To do this, we present a novel approximation for this ID that is based on computing concepts only up to a certain support value. We demonstrate and evaluate our approximation using all available datasets from Tatti et al., which have between 469 and 41271 extrinsic *** Codes 68T05 06-08 68T01 68T09 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

Realizability of Rectangular Euler Diagrams

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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

Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education

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arXiv 2024年

作者： Shao, Pengyang Yang, Yonghui Gao, Chen Chen, Lei Zhang, Kun Zhuang, Chenyi Wu, Le Li, Yong Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei China BNRist Tsinghua University Beijing China Department of Electronic Engineering BNRist Tsinghua University Beijing China Ant Group Zhejiang Hangzhou China

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students’ proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence Criterion. Finally, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD. © 2024, CC BY.

关键词： knowledge graph

HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API

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HardnessTesterV: A Web Machine Learning application for Vick...

International Conference on Electrical, Communication, and Computer engineering (ICECCE)

作者： Ayorinde Tayo Olanipekun Daniel Mashao Data Science Across Disciplines Research Group Institute for the Future of Knowledge Faculty of Engineering and the Built Environment University of Johannesburg South Africa Faculty of Engineering and the Built Environment University of Johannesburg South Africa

This research presents the HardnessTesterV app- a web application for predicting the Vicker hardness of Laser welded Metallic alloy using Flask API, HTML, and CSS to build the front end and back end. Vickers hardness is an important property in the manufacturing industry, and predicting it accurately is vital for material selection and design. The Web application takes some of the important processing parameters as the input features and uses a random forest Machine learning model to predict its Vickers hardness. Flask API was used to create a RESTful interface that provides a user-friendly platform for users to submit the combinations of the processing parameters and receive predictions. HTML and CSS were used to design the front end of the web application to provide a visually appealing interface for users. The application was deployed on a PythonAnywhere cloud server, making it easily accessible to users. The application was deployed on a cloud server, making it easily accessible to users worldwide. The accuracy of the predictions was evaluated using various metrics and the results showed that the developed model can accurately predict the Vickers hardness of metallic alloys. HardnessTesterV web app measures the Vickers hardness of 2507 Duplex stainless steel material. The web application is expected to be a valuable tool for engineers and researchers in the manufacturing industry for material selection and design.

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