Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: When integrating information from multiple bsites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match.  have developed an object identification system called Active Atlas, which compares the objects' shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist beten two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings beten objects, as ll as domain-dependent transformations for recognizing format inconsistencies. This manual process is time consuming and error-prone. In our approach, Active Atlas learns to simultaneously tailor both mapping rules and a set of general transformations to a specific application domain, through limited user input. The experimental results demonstrate that  achieve higher accuracy and require less user involvement than previous methods across various application domains.
['Sheila Tejada', 'Craig A. Knoblock', 'Steven Minton']
I am writing a research paper on a idea Learning object identification rules for information integration
[{"from": "human", "value": "I am writing a research paper on a idea Learning object identification rules for information integration"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: When integrating information from multiple bsites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match.  have developed an object identification system called Active Atlas, which compares the objects' shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist beten two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings beten objects, as ll as domain-dependent transformations for recognizing format inconsistencies. This manual process is time consuming and error-prone. In our approach, Active Atlas learns to simultaneously tailor both mapping rules and a set of general transformations to a specific application domain, through limited user input. The experimental results demonstrate that  achieve higher accuracy and require less user involvement than previous methods across various application domains."}]