# Fine-Grained Tasks for Crowdsourced Entity Resolution
## 8 Conclusions  
In this paper, we propose a crowdsourced entity resolution approach with fine-grained tasks for structured datasets, treating entity resolution as a problem of partitioning records based on attribute relationships. To achieve this, we introduce fine-grained tasks that replace traditional record pair matching with attribute pair matching, supported by an attribute scoring task. This approach reduces the cost of individual record pair matching while extracting attribute matching relationships to identify entities by propagating these relationships throughout the dataset. To resolve data efficiently and minimize unnecessary tasks, we propose a density-based task selection order and a benefit-state-based task set selection method. The experimental results demonstrate that our approach significantly enhances entity resolution accuracy while effectively reducing the costs for datasets with a high entity density. The findings from our experiments highlight both the model's theoretical and practical contributions. Theoretically, the proposed fine-grained task model introduces a new perspective on decomposing the entity resolution process, which may inspire further research in modular task design and optimization. Practically, our approach addresses key challenges in crowdsourced data resolution, offering a scalable and cost-effective solution for datasets with a high degree of attribute interdependence. By improving both resolution accuracy and efficiency, this method is particularly valuable for industries relying on large-scale data integration, such as e-commerce, healthcare, and financial services. Despite its strengths, this study has some limitations. First, the effectiveness of the proposed methods relies heavily on the quality and completeness of the attribute data, which may vary across datasets. Second, the crowdsourcing framework assumes the availability of reliable workers; any lack of expertise or inconsistent effort could affect the outcomes. Third, while the density-based task selection reduces unnecessary tasks,  
Figure 11: Number of iterations at different costs. Figure 12: Number of tasks with different cost constraints. it may sometimes overlook less-dense but significant attribute relationships, potentially impacting the resolution's comprehensiveness. These limitations underscore the need for robust quality control mechanisms and adaptive task selection strategies to enhance the framework's applicability across diverse contexts. Future research can explore extending this work to human-AI or group-AI collaborative frameworks. By integrating AI models for initial attribute scoring or candidate record selection, the reliance on human workers could be further reduced, improving their scalability. Moreover, leveraging advanced machine learning techniques, such as large language models or graph neural networks, to predict attribute relationships and resolve entities in semi-automated settings could significantly enhance both accuracy and efficiency. Investigating dynamic task allocation strategies in hybrid human-AI environments and designing interfaces that facilitate seamless collaboration between humans and AI agents are also promising directions. Finally, applying the proposed methods to real-world datasets across diverse domains would provide valuable insights into its adaptability and effectiveness in practical scenarios. Conceptualization, S.Y. and T.N. ; methodology, H.M. and X.L. ; software, H.M. and T.N. ; validation, S.Y., T.N. and X.L. ; formal analysis, S.Y. and X.L. ; investigation, X.L. and T.N. ; resources, T.N. ; data curation, H.M. and T.N. ; writing--original draft preparation, H.M.; writing--review and editing, T.N; visualization, H.M.; supervision, H.M.; project administration, S.Y. ; funding acquisition, all authors. All authors have read and agreed to the published version of the manuscript.