short-paper

Mixed-Initiative Feature Engineering Using Knowledge Graphs

Authors:

Martin Atzmueller,

Eric SternbergAuthors Info & Claims

K-CAP '17: Proceedings of the 9th Knowledge Capture Conference

Article No.: 46, Pages 1 - 4

https://doi.org/10.1145/3148011.3154473

Published: 04 December 2017 Publication History

Abstract

This paper proposes a mixed-initiative feature engineering approach using explicit knowledge captured in a knowledge graph complemented by a novel interactive visualization method. Using the explicitly captured relations and dependencies between concepts and their properties, feature engineering is enabled in a semi-automatic way. Furthermore, the results (and decisions) obtained throughout the process can be utilized for refining the features and the knowledge graph. Analytical requirements can then be conveniently captured for feature engineering -- enabling integrated semantics-driven data analysis and machine learning.

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Cited By

Li JSatheesh SHeindorf SMoussallem DSpeck RNgomo A(2024)AutoCL: AutoML for Concept LearningExplainable Artificial Intelligence10.1007/978-3-031-63787-2_7(117-136)Online publication date: 10-Jul-2024
https://doi.org/10.1007/978-3-031-63787-2_7
van den Hoogen JBloemheuvel SAtzmueller M(2023)Advanced Analytics on Complex Industrial DataData Science for Entrepreneurship10.1007/978-3-031-19554-9_9(177-203)Online publication date: 24-Mar-2023
https://doi.org/10.1007/978-3-031-19554-9_9
Nalepa GBobek SKutt KAtzmueller M(2021)Semantic Data Mining in Ubiquitous Sensing: A SurveySensors10.3390/s2113432221:13(4322)Online publication date: 24-Jun-2021
https://doi.org/10.3390/s21134322
Show More Cited By

Index Terms

Mixed-Initiative Feature Engineering Using Knowledge Graphs
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
2. Human-centered computing
  1. Visualization

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Published In

cover image ACM Conferences

K-CAP '17: Proceedings of the 9th Knowledge Capture Conference

December 2017

271 pages

ISBN:9781450355537

DOI:10.1145/3148011

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 04 December 2017

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K-CAP 2017

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SIGAI

K-CAP 2017: Knowledge Capture Conference

December 4 - 6, 2017

TX, Austin, USA

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Overall Acceptance Rate 55 of 198 submissions, 28%

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Cited By

Li JSatheesh SHeindorf SMoussallem DSpeck RNgomo A(2024)AutoCL: AutoML for Concept LearningExplainable Artificial Intelligence10.1007/978-3-031-63787-2_7(117-136)Online publication date: 10-Jul-2024
https://doi.org/10.1007/978-3-031-63787-2_7
van den Hoogen JBloemheuvel SAtzmueller M(2023)Advanced Analytics on Complex Industrial DataData Science for Entrepreneurship10.1007/978-3-031-19554-9_9(177-203)Online publication date: 24-Mar-2023
https://doi.org/10.1007/978-3-031-19554-9_9
Nalepa GBobek SKutt KAtzmueller M(2021)Semantic Data Mining in Ubiquitous Sensing: A SurveySensors10.3390/s2113432221:13(4322)Online publication date: 24-Jun-2021
https://doi.org/10.3390/s21134322
Vollert SAtzmueller MTheissler A(2021)Interpretable Machine Learning: A brief survey from the predictive maintenance perspective2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )10.1109/ETFA45728.2021.9613467(01-08)Online publication date: 7-Sep-2021
https://dl.acm.org/doi/10.1109/ETFA45728.2021.9613467
Atzmueller M(2019)Onto Model-based Anomalous Link Pattern Mining on Feature-Rich Social Interaction NetworksCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316707(1047-1050)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316707
Chen XQiao BZhang WWu WChintalapati MZhang DLin QLuo CLi XZhang HXu YDang YSui KZhang X(2019)Neural Feature Search: A Neural Architecture for Automated Feature Engineering2019 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2019.00017(71-80)Online publication date: Nov-2019
https://doi.org/10.1109/ICDM.2019.00017
Atzmueller MHayat NTrojahn MKroll D(2018)Explicative human activity recognition using adaptive association rule-based classification2018 IEEE International Conference on Future IoT Technologies (Future IoT)10.1109/FIOT.2018.8325603(1-6)Online publication date: Jan-2018
https://doi.org/10.1109/FIOT.2018.8325603
Sternberg EAtzmueller M(2018)Knowledge-Based Mining of Exceptional Patterns in Logistics Data: Approaches and Experiences in an Industry 4.0 ContextFoundations of Intelligent Systems10.1007/978-3-030-01851-1_7(67-77)Online publication date: 7-Oct-2018
https://doi.org/10.1007/978-3-030-01851-1_7
Atzmueller M(2018)Declarative Aspects in Explicative Data Mining for Computational SensemakingDeclarative Programming and Knowledge Management10.1007/978-3-030-00801-7_7(97-114)Online publication date: 27-Sep-2018
https://doi.org/10.1007/978-3-030-00801-7_7

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