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2023 Volume 38
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RESEARCH ARTICLE   Open Access    

Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus

  • Lero–Science Foundation Ireland Research Centre for Software, Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland

  • NatPro Center, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin 2, Ireland

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  • Corresponding author: Corresponding author: Yao Yao; Email: Yao.Yao@ncirl.ie 
  • Abstract: Efficient knowledge extraction from Big Data is quite a challenging topic. Recognizing relevant concepts from unannotated data while considering both context and domain knowledge is critical to implementing successful knowledge extraction. In this research, we provide a novel platform we call Active Learning Integrated with Knowledge Extraction (ALIKE) that overcomes the challenges of context awareness and concept extraction, which have impeded knowledge extraction in Big Data. We propose a method to extract related concepts from unorganized data with different contexts using multiple agents, synergy, reinforcement learning, and active learning.We test ALIKE on the datasets of the COVID-19 Open Research Dataset Challenge. The experiment result suggests that the ALIKE platform can more efficiently distinguish inherent concepts from different papers than a non-agent-based method (without active learning) and that our proposed approach has a better chance to address the challenges of knowledge extraction with heterogeneous datasets. Moreover, the techniques used in ALIKE are transferable across any domain with multidisciplinary activity.
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  • Cite this article

    Yao Yao, Junying Liu, Conor Ryan. 2023. Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus. The Knowledge Engineering Review 38(1), doi: 10.1017/S0269888923000085
    Yao Yao, Junying Liu, Conor Ryan. 2023. Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus. The Knowledge Engineering Review 38(1), doi: 10.1017/S0269888923000085
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RESEARCH ARTICLE   Open Access    

Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus

  • Lero–Science Foundation Ireland Research Centre for Software, Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland

  • NatPro Center, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Dublin 2, Ireland

  • Corresponding author: Corresponding author: Yao Yao; Email: Yao.Yao@ncirl.ie 
  • Received: 28 July 2022
  • Revised: 18 July 2023
  • Accepted: 09 October 2023
  • Published online: 08 November 2023
The Knowledge Engineering Review  38,  2023, 38(1)  |  Cite this article

Abstract: Abstract: Efficient knowledge extraction from Big Data is quite a challenging topic. Recognizing relevant concepts from unannotated data while considering both context and domain knowledge is critical to implementing successful knowledge extraction. In this research, we provide a novel platform we call Active Learning Integrated with Knowledge Extraction (ALIKE) that overcomes the challenges of context awareness and concept extraction, which have impeded knowledge extraction in Big Data. We propose a method to extract related concepts from unorganized data with different contexts using multiple agents, synergy, reinforcement learning, and active learning.We test ALIKE on the datasets of the COVID-19 Open Research Dataset Challenge. The experiment result suggests that the ALIKE platform can more efficiently distinguish inherent concepts from different papers than a non-agent-based method (without active learning) and that our proposed approach has a better chance to address the challenges of knowledge extraction with heterogeneous datasets. Moreover, the techniques used in ALIKE are transferable across any domain with multidisciplinary activity.

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    Acknowledgments

    • This work was supported with the financial support of the Science Foundation Ireland grant 13/RC/2094_P2 and co-funded under the European Regional Development Fund through the Southern & Eastern Regional Operational Programme to Lero—the Science Foundation Ireland Research Centre for Software (www.lero.ie). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 754489. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

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    • This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
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    Yao Yao, Junying Liu, Conor Ryan. 2023. Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus. The Knowledge Engineering Review 38(1), doi: 10.1017/S0269888923000085
    Yao Yao, Junying Liu, Conor Ryan. 2023. Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus. The Knowledge Engineering Review 38(1), doi: 10.1017/S0269888923000085
    shu

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