Adaptive rule adaptation in unstructured and dynamic environments

Alireza Tabebordbar*, Amin Beheshti, Boualem Benatallah, Moshe Chai Barukh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

3 Citations (Scopus)

Abstract

Rule-based systems have been used to augment machine learning based algorithms for annotating data in unstructured and dynamic environments. Rules can alleviate many of shortcomings inherent in pure algorithmic approaches. Rule adaptation is a challenging and error-prone task: in a rule-based system, there is a need for an analyst to adapt rules in order to keep them applicable and precise. In this paper, we present an approach for adapting data annotation rules in unstructured and constantly changing environments. Our approach offloads analysts from adapting rules and autonomically identifies the optimal modification for rules using a Bayesian multi-armed-bandit algorithm. We conduct experiments on different curation domains and compare the performance of our approach with systems relying on analysts. The experimental results show a comparative performance of our approach compared to analysts in adapting rules.
Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2019
Subtitle of host publication20th International Conference Hong Kong, China, January 19–22, 2020 Proceedings
EditorsReynold Cheng, Nikos Mamoulis, Yizhou Sun, Xin Huang
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages326-340
Number of pages15
ISBN (Electronic)9783030342234
ISBN (Print)9783030342227
DOIs
Publication statusPublished - 2019
EventInternational Conference on Web Information Systems Engineering (20th : 2019) - , Hong Kong
Duration: 26 Nov 201930 Nov 2019

Publication series

NameLecture Notes in Computer Science
Volume11881
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Web Information Systems Engineering (20th : 2019)
Abbreviated titleWISE 2019
CountryHong Kong
Period26/11/1930/11/19

Keywords

  • Rule adaptation
  • Data annotation
  • Rule based systems
  • Data curation

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  • Cite this

    Tabebordbar, A., Beheshti, A., Benatallah, B., & Barukh, M. C. (2019). Adaptive rule adaptation in unstructured and dynamic environments. In R. Cheng, N. Mamoulis, Y. Sun, & X. Huang (Eds.), Web Information Systems Engineering – WISE 2019: 20th International Conference Hong Kong, China, January 19–22, 2020 Proceedings (pp. 326-340). (Lecture Notes in Computer Science; Vol. 11881). Cham, Switzerland: Springer, Springer Nature. https://doi.org/10.1007/978-3-030-34223-4_21