用智能体模拟方法解决数据模式自动匹配难题
Implementing Systemic Thinking for Automatic Schema Matching: An Agent-Based Modeling Approach
- 将模式匹配视为复杂自适应系统，用智能体建模模拟
- 实验显示匹配质量提升，所需人力显著减少
- 适合研究数据集成与智能系统设计的学者
针对自动模式匹配（ASM）问题，现有方法面临过程与结果高度复杂和不确定的挑战。本文提出将模式匹配视为复杂自适应系统（CAS），采用基于智能体的建模与仿真（ABMS）方法进行建模。该方法催生了一个名为Reflex-SMAS的原型工具。实验验证了该方法在两个方面均具可行性：(i) 效果性（提升匹配质量），(ii) 效率性（降低所需工作量）。本研究在自动模式匹配领域实现了一次重要范式转变。
原文摘要 · Abstract (English)
Several approaches are proposed to deal with the problem of the Automatic Schema Matching (ASM). The challenges and difficulties caused by the complexity and uncertainty characterizing both the process and the outcome of Schema Matching motivated us to investigate how bio-inspired emerging paradigm can help with understanding, managing, and ultimately overcoming those challenges. In this paper, we explain how we approached Automatic Schema Matching as a systemic and Complex Adaptive System (CAS) and how we modeled it using the approach of Agent-Based Modeling and Simulation (ABMS). This effort gives birth to a tool (prototype) for schema matching called Reflex-SMAS. A set of experiments demonstrates the viability of our approach on two main aspects: (i) effectiveness (increasing the quality of the found matchings) and (ii) efficiency (reducing the effort required for this efficiency). Our approach represents a significant paradigm-shift, in the field of Automatic Schema Matching.
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