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A Reference Process for Assessing the Reliability of Predictive Analytics Results

SN Computer Science 2024年第5期5卷 563页

作者： Staudinger, Simon Schuetz, Christoph G. Schrefl, Michael Institute of Business Informatics-Data and Knowledge Engineering Johannes Kepler University Linz Altenberger Straße 69 Linz 4040 Austria

Organizations employ data mining to discover patterns in historic data in order to learn predictive models. Depending on the predictive model the predictions may be more or less accurate, raising the question about the reliability of individual predictions. This paper proposes a reference process aligned with the CRISP-DM to enable the assessment of reliability of individual predictions obtained from a predictive model. The reference process describes activities along the different stages of the development process required to establish a reliability assessment approach for a predictive model. The paper then presents in more detail two specific approaches for reliability assessment: perturbation of input cases and local quality measures. Furthermore, this paper describes elements of a knowledge graph to capture important metadata about the development process and training data. The knowledge graph serves to properly configure and employ the reliability assessment approaches. © The Author(s) 2024.

关键词： business analytics business intelligence CRISP-DM data mining Decision support systems

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Enhancing Scientific knowledge Graph Generation Pipelines with LLMs and Human-in-the-Loop 4

Enhancing Scientific Knowledge Graph Generation Pipelines wi...

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4th International Workshop on Scientific knowledge: Representation, Discovery, and Assessment, Sci-K 2024

作者： Tsaneva, Stefani Dessì, Danilo Osborne, Francesco Sabou, Marta Institute of Data Process and Knowledge Management Vienna University of Economics and Business Austria Computer Science Department College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Knowledge Media Institute The Open University United Kingdom Department of Business and Law University of Milano Bicocca Italy

Scientific knowledge Graphs have recently become a powerful tool for exploring the research landscape and assisting scientific inquiry. It is crucial to generate and validate these resources to ensure they offer a comprehensive and accurate representation of specific research fields. However, manual approaches are not scalable, while automated methods often result in lower-quality resources. In this paper, we investigate novel validation techniques to improve the accuracy of automated KG generation methodologies, leveraging both a human-in-the-loop (HiL) and a large language model (LLM)-in-the-loop. Using the automated generation pipeline of the Computer Science knowledge Graph as a case study, we demonstrate that precision can be increased by 12% (from 75% to 87%) using only LLMs. Moreover, a hybrid approach incorporating both LLMs and HiL significantly enhances both precision and recall, resulting in a 4% increase in the F1 score (from 77% to 81%). © 2022 Copyright for this paper by its authors.

关键词： knowledge graph

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High-Utility Action Rules Mining 16

High-Utility Action Rules Mining

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16th International Rule Challenge and 6th Doctoral Consortium at RuleML+RR, RuleML+RR-Companion 2022

作者： Sýkora, Lukáš Kliegr, Tomáš Hrudková, Kateřina Department of Information and Knowledge Engineering Faculty of Informatics and Statistics Prague University of Economics and Business Czech Republic

High-utility action rule mining is a data mining task that aims to generate rules that provide the user with information on which actions might be the most profitable. The actions correspond to proposed changes in attribute values that may move some instances originally predicted to belong to the undesirable class to the desirable class. Through a list of conditions on attribute values, an action rule delimits a set of instances to which the proposed action applies to. If this change is implemented, the action rule implies that the classification of a part of these instances will change. The profit of an action rule is calculated based on a user-set utility table specifying the costs of actions and the benefit of moving an instance to the desirable class. In this paper, we report on a new extension of the ActionRules package written in Python that implements high-utility action rule mining. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Python

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A data DRIVEN TOOL TO SUPPORT DESIGN TEAM COMPOSITION MEASURING SKILLS DIVERSITY 24

A DATA DRIVEN TOOL TO SUPPORT DESIGN TEAM COMPOSITION MEASUR...

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24th International Conference on engineering Design, ICED 2023

作者： Chiarello, Filippo Spada, Irene Barandoni, Simone Giordano, Vito Fantoni, Gualtiero School of Engineering Department of Energy Systems Land and Construction Engineering University of Pisa Italy School of Engineering Department of Civil and Industrial Engineering University of Pisa Italy School of Engineering Department of Engineering Informatics University of Pisa Italy Department of Informatics University of Pisa Italy B4DS - Business Engineering for Data Science Lab University of Pisa Italy

Team composition in Project Based Learning is the first task for the class and has a great impact on the learning experience. Anyway, little space is dedicated in literature about team composition, considering their personal inclinations towards design tasks. For these reasons we propose a tool that aims to map the design skills of students to optimise team composition. The tool is based on a questionnaire grounded in the design theory and aims at measuring the willingness of students at performing certain design tasks. The results of the questionnaires are analysed using Principal Component Analysis to normalise each students' answers to the whole class, and to show the distribution of students in the space of engineering design skills. We present the design process of the tool, and a first experimentation on two classes of master's degree students in Management engineering and data Science, testing the tool on a total of 72 students. The results are promising and demonstrate the robusteness of the questionnaire and of the analytical method. Also, we propose next steps for our research activity, calling for other researchers to test our method in different contexts. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Students

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Traceable LLM-based validation of statements in knowledge graphs

arXiv

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arXiv 2024年

作者： Adam, Daniel Kliegr, Tomáš Prague University of Economics and Business Faculty of Informatics and Statistics Department of Information and Knowledge Engineering Prague Czech Republic

This article presents a method for verifying RDF triples using LLMs, with an emphasis on providing traceable arguments. Because the LLMs cannot currently reliably identify the origin of the information used to construct the response to the user query, our approach is to avoid using internal LLM factual knowledge altogether. Instead, verified RDF statements are compared to chunks of external documents retrieved through a web search or Wikipedia. To assess the possible application of this workflow on biosciences content, we evaluated 1,719 positive statements from the BioRED dataset and the same number of newly generated negative statements. The resulting precision is 88%, and recall is 44%. This indicates that the method requires human oversight. We demonstrate the method on Wikidata, where a SPARQL query is used to automatically retrieve statements needing verification. Overall, the results suggest that LLMs could be used for large-scale verification of statements in KGs, a task previously unfeasible due to human annotation costs. © 2024, CC BY.

关键词： knowledge graph

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Anomaly Detection in Robot Applications: Comparison of Rule-Based and Machine Learning Methods

Anomaly Detection in Robot Applications: Comparison of Rule-...

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International Conference on Cybernetics, Robotics and Control (CRC)

作者： Mara Vukadinovic Bernhard Reiterer Michael Rathmair Christoph G. Schuetz Joanneum Research Institute for Robotics and Flexible Production Klagenfurt Austria Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Austria

ISBN: (数字)9798331517489

ISBN: (纸本)9798331517496

The increasing use of robot applications in various industries requires close monitoring and management of the data generated by these systems. Therefore, it is essential to implement a monitoring system that identifies and reports abnormal situations in robots. The literature differentiates between rule-based and machine learning methods. Rule-based approaches rely on predefined rules to detect deviations from expected behavior. In contrast, machine learning algorithms acquire the ability to learn patterns on their own. This paper evaluates both approaches to determine whether machine learning algorithms can replace or enhance rule-based methods. The evaluation employs an actual production scenario that deals with three induced anomalies: Additional weight, drop of the manipulated object, and reduced speed.

关键词： Industries Machine learning algorithms Service robots Machine learning Production Monitoring Anomaly detection Cybernetics

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Towards Extraction of Validation Rules from OPC UA Companion Specifications 28

Towards Extraction of Validation Rules from OPC UA Companion...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Tufek, Nilay Sree Thuluva, Aparna Sai Philipp Just, Valentin Sabou, Marta Siemens AG Technology Department Munich Germany Vienna University of Technology Inst. Computer Engineering Austria Vienna University of Economics and Business Inst. Data Process Knowledge Management Austria

ISBN: (纸本)9798350339918

Interoperability is the key to industrial automation. OPC UA aims to provide interoperability between industrial machines at the network, protocol, and semantic layers. In order to achieve it, the implementation of a machine and its nodeset file should comply with the OPC UA standard. However, there are a few areas for improvement in technology to automatically check compliance since the compliance rules/validation rules are expressed in textual format in specification documents. In other words, they are not machine-interpretable. Converting the text-based specification into machine-readable form is a complex problem since each companion specification is domain-specific and written by a set of industry experts from all over the world from diverse language backgrounds. The specifications are majorly unique, with little commonality between them. Because of these reasons, it is challenging to develop a generic information extraction approach to retrieve rules that work for all specifications. In this study, we aim to handle this challenge to extract the textual rules from specifications automatically by applying Natural Language Processing and Machine Learning technologies. We present our methodology based on Named Entity Recognition for OPC UA documents for information extraction and text classification to identify rules. To achieve this goal, we created five named entity data sets from five selected OPC UA companion specifications. The sentences in the data set and the entities in the sentences were annotated by two OPC UA experts. We point out a deeper analysis of the data sets to highlight common and unique entities in them and show their usage in identifying the rules in the specifications. © 2023 IEEE.

关键词： Classification (of information)

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A NONPARAMETRIC ROBUST OPTIMIZATION APPROACH FOR CHANCE-CONSTRAINED KNAPSACK PROBLEM

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SIAM Journal on Optimization 2025年第2期35卷 739-766页

作者： Xu, Liang Zhang, Chao Xu, Zhou Long, Daniel Zhuoyu School of Business Administration Southwestern University of Finance and Economics Chengdu China Big Data Laboratory on Financial Security and Behavior SWUEF Laboratory of Philosophy and Social Sciences Ministry of Education Chengdu China School of Medical Informatics Chongqing Medical University Chongqing China Department of Logistics Management and Maritime Studies Hong Kong Polytechnic University Hong Kong Department of System Engineering and Engineering Management Chinese University of Hong Kong Hong Kong

A chance-constrained knapsack problem (CCKP) is a knapsack problem restricted by a chance constraint, which ensures that the total capacity constraint under uncertain volume can be violated only up to a given probability threshold. CCKP is challenging to solve due to its combinatorial nature and the involvement of its chance constraint. Existing solution methods for CCKP with tractability guarantees mainly focus on two approaches: (1) a full-information approach (stochastic programming) that assumes the uncertain volume follows certain distributions, such as normal or empirical distribution;(2) a partial-information approach (robust optimization) that adopts specific statistics of the unknown distribution, such as the mean and variance. The existing full-information approach lacks robustness under limited samples due to its strong assumption;the existing partial-information approach can be further improved, as the uncertainty set or distributional ambiguity set can be ameliorated. With these concerns in mind, we propose a nonparametric robust approach for CCKP by involving a novel nonparametric statistic to form a new distributional ambiguity set. Furthermore, we develop an upper bound on the violation probability of the chance constraint under the distributional ambiguity set to approximate CCKP by a deterministic robust counterpart. In terms of solution methodology, we decompose the deterministic robust counterpart into cardinality-constrained knapsack problems, which can be efficiently solved by the proposed dynamic programming algorithm. Computational results show that our proposed solution methods produce better solutions to CCKP compared with existing approaches. © 2025 Society for Industrial and Applied Mathematics.

关键词： Stochastic programming

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Initiating a One Digital Health Unified Terminology (ODH-UT) to Facilitate Community Expansion

Initiating a One Digital Health Unified Terminology (ODH-UT)...

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34th Medical informatics Europe Conference, MIE 2024

作者： Bauberg, Helit Nehama, Dana Blinder, Constantine Davidman, Roy Cohen, Eliyahu Tamburis, Oscar Darmoni, Stefan J. Grosjean, Julien Benis, Arriel Department of Data Science Holon Institute of Technology Israel Faculty of Industrial Engineering and Technology Management Holon Institute of Technology Israel Institute of Biostructures and Bioimaging National Research Council Naples Italy Department of Biomedical Informatics Rouen University Hospital France LIMICS Laboratory of Medical Informatics and Knowledge Engineering in e-Health INSERM U1142 Sorbonne Université France Department of Digital Medical Technologies Science Holon Institute of Technology Israel

ISBN: (纸本)9781643685335

One Digital Health (ODH) merges the Digital Health and One Health approaches to create a comprehensive framework for future health ecosystems. In this rapidly evolving field, a standardized vocabulary is not just a convenience, but a necessity to ensure efficient communication. This research proposes the development of a "One Digital Health-Unified Terminology" (ODH-UT) to facilitate communication among researchers and practitioners in Digital Health and One Health, addressing this crucial need. © 2024 The Authors.

关键词： Electronic health record

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Exploring the Role of Generative AI in Constructing knowledge Graphs for Drug Indications with Medical Context 15

Exploring the Role of Generative AI in Constructing Knowledg...

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15th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2024

作者： Alharbi, Reham Ahmed, Umair Dobriy, Daniil Lajewska, Weronika Menotti, Laura Saeedizade, Mohammad Javad Dumontier, Michel Department of Computer Science University of Liverpool Liverpool United Kingdom School of Science and Technology University of Camerino Camerino Italy Institute for Data Process and Knowledge Management Vienna University of Economics and Business Vienna Austria Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway Department of Information Engineering University of Padua Padova Italy Department of Computer Science Linköping University Linköping Sweden Institute of Data Science Maastricht University Maastricht Netherlands

The medical context for a drug indication provides crucial information on how the drug can be used in practice. However, the extraction of medical context from drug indications remains poorly explored, as most research concentrates on the recognition of medications and associated diseases. Indeed, most databases cataloging drug indications do not contain their medical context in a machine-readable format. This paper proposes the use of a large language model for constructing DIAMOND-KG, a knowledge graph of drug indications and their medical context. The study 1) examines the change in accuracy and precision in providing additional instruction to the language model, 2) estimates the prevalence of medical context in drug indications, and 3) assesses the quality of DIAMOND-KG against NeuroDKG, a small manually curated knowledge graph. The results reveal that more elaborated prompts improve the quality of extraction of medical context;71% of indications had at least one medical context;63.52% of extracted medical contexts correspond to those identified in NeuroDKG. This paper demonstrates the utility of using large language models for specialized knowledge extraction, with a particular focus on extracting drug indications and their medical context. We provide DIAMOND-KG as a FAIR RDF graph supported with an ontology. Openly accessible, DIAMOND-KG may be useful for downstream tasks such as semantic query answering, recommendation engines, and drug repositioning research. © 2024 Copyright for this paper by its authors.

关键词： knowledge graph

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