International Database Engineering and Applications Symposium

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The aim of the IDEAS series of symposiums is to address the engineering and application aspects of databases. The symposium provides an international forum for discussion of the problems of engineering data drivend systems involving not only database technology but the related areas of information retrieval, multimedia, human machine interface and communication. The goal of IDEAS is to foster closer interaction among the industrial, research and user communities and provides an excellent opportunity for them to meet, discuss ideas, examine the current ones and develop new solutions and research directions. Along with the technical sessions, the symposium also features prominent invited speakers. IDEAS series of symposiums are scheduled annually and have been held since 1997 in North America, Europe and Asia. It has attracted participants from governmental and non-governmental agencies, industries, and academia to exchange ideas and share experiences. Organized by Concordia University, Montreal, Canada; with the cooperation of BytePress and ConfSys. The conference proceedings will be published in the LNCS(Springer) series. Invited Talk: Empowering Large Language Models with Knowledge Structuring and Reasoning by Jiawei Han. Large language models (LLMs) have demonstrated significant potential in query answering, reasoning and decision support. Yet LLMs still suffer from hallucinations and lack fine-grained contextual domain-specific knowledge, limiting their power of in-depth reasoning/prediction in many scientific problem-solving applications. Traditional retrieval-augmented generation (RAG) methods attempt to address these limitations but frequently retrieve less relevant and unstructured information, undermining reasoning and prediction accuracy. We propose a retrieving-structuring-reasoning approach to mine structures from unstructured data, retrieving more relevant information upon a query, and construct task-specific graphs using the mined structures to facilitate structure-augmented LLM generation.

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