Explicit and Implicit Consumer Electronics Information Integration via Automatic Large Language Model Construction

Integrating diverse Consumer Electronics (CE) information is essential to enhance and optimize user experiences, but CE Information Integration (CEII) faces challenges arising from differences in entity descriptions. In recent years, Large Language Models (LLMs) have emerged as valuable tools for analyzing heterogeneous CE data due to their deep contextual embeddings. However, different LLMs capture various nuances and complexity levels within the CE data, and none of them can ensure their effectiveness in all heterogeneous scenarios. To address this issue, this paper proposes an automatic LLM Construction for CEII, which can determine both explicit and implicit CE entity mappings. First, a hybrid Genetic Programming is developed to determine explicit entity mappings using a new individual representation, an innovative fitness function, and a Probability-based Incremental Learning algorithm. Then, a novel Rough Set based matching method is presented to efficiently induce implicit CE entity mappings, which uses the entity feature relevance metric and the dominance-based matching approach. The experiment tests the performance of our approach with the OAEI’s KG and common KG datasets, and the practical CE datasets in the smart home context. The experimental results show the effectiveness of the developed approach, which significantly outperforms the state-of-the-art entity matching methods.