Chen Ye

Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. To further enhance their factual grounding and reasoning fidelity, integrating LLMs with Knowledge Graphs (KGs) has emerged as a promising direction. Significant progress has been made in leveraging KGs to augment LLM reasoning through methods like Retrieval-Augmented Generation. However, effectively harnessing the synergy between LLMs and KGs for robust and reliable reasoning still presents critical challenges. Specifically: (1) LLMs struggle to effectively interpret and utilize the structured nature of KGs , due to the discrepancy between their text-based training and KG's symbolic representations; (2) querying and reasoning over structured knowledge in KGs remains inefficient for LLMs , hindering complex inference. To address these limitations, we introduce Meta-Knowledge enhanced Knowledge Graph (MKG), a novel framework that empowers LLMs to effectively leverage structured knowledge from KGs. MKG employs Meta-Knowledge , stored in a multi-store memory with a Self-Correcting Mechanism, to guide LLMs in KG retrieval and reasoning. Our experimental evaluations on complex question answering benchmarks demonstrate that MKG achieves significant performance gains, outperforms the baseline Original LLM, Retrieval-Augmented Generation (RAG), ReAct, GraphRAG and ToG frameworks by 25%, 17%, 11%, 3.3% and 2.6%, respectively.

This is the preface for the Special Issue “Big Data Mining and Analytics with Applications”, which was published in the MDPI journal Mathematics

Over the last decades, various cost-based optimizers have been proposed to generate optimal plans for SQL queries. These optimizers are key to achieving good performance in database systems and can speed up query execution. Still, they may need enormous expert efforts and perform poorly on complicated queries. Learning-based optimizers have been shown to achieve high-quality plans by learning from past experiences. However, these solutions treat each query separately and neglect the semantic equivalence among different queries. Intuitively, a high-quality plan may be obtained for a complicated query by discovering a simple equivalent query. Motivated by this, in this paper, we present Celo, a novel constraint-enhanced learned optimizer to directly integrate the equivalent information of queries into the learning-based model. We apply denial constraints to identify equivalent queries by replacing equivalent predicates. Given a query, we augment the query plans generated by the learning-based model with the high-quality plans of its equivalent queries. Then, a more potentially well-performed plan will be predicted among the augmented query plans. Extensive experiments using real-world datasets demonstrated that Celo outperforms the previous state-of-the-art (SOTA) results even with few constraints.

In the era of Big Data, integrating information from multiple sources has proven valuable in various fields. To ensure a high-quality supply of multi-source data, repairing different types of errors in the multi-source data becomes critical. This paper categorizes errors in multi-source data into entity information overlapping, attribute value conflicts, and attribute value inconsistencies. We first summarize existing repairing methods for these errors and then examine and review the study of the detection and repair of compound-type errors in multi-source data. Finally, we indicate further research directions in multi-source data repair.

Web tables are essential for applications such as data analysis. However, web tables are often incomplete and short of some critical information, which makes it challenging to understand the web table content. Automatically predicting column types for tables without metadata is significant for dealing with various tables from the Internet. This paper proposes a CNN-Text method to deal with this task, which fuses CNN prediction and voting processes. We present data augmentation and synthetic column generation approaches to improve the CNN’s performance and use extracted text to get better predictions. The experimental result shows that CNN-Text outperforms the baseline methods, demonstrating that CNN-Text is well qualified for the table column type prediction.