{'fraction_non_alphanumeric': 0.015991471215351813, 'fraction_numerical': 0.0, 'mean_word_length': 5.302013422818792, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Schema Matching as complex adaptive system

Many algorithms and approaches were proposed to deal with the problem of automatic schema matching and mapping. Yet, managing uncertainty and complexity for Schema Matching still remains as an open question. The challenges and difficulties caused by the complexity characterising the process of Schema Matching motivated us to investigate how the application of a bio-inspired emerging paradigm can lead us to understand, manage and ultimately overcome the inherent uncertainty in this process. The central idea of our work, is to consider the process of matching as a complex adaptive system and model it using the approach of agent-based modeling and simulation. The aim being the exploitation of the intrinsic properties of the agent-based models, such as emergence, stochasticity and self-organization, to help provide answers to better manage complexity and uncertainty of Schema Matching.