Dependency-Aware Core Column Discovery for Table Understanding

In a relational table, core columns represent the primary subject entities that other columns in the table depend on. While discovering core columns is crucial for understanding a table's semantic column types, column relations, and entities, it is often overlooked. Previous methods typically rely on heuristic rules or contextual information, which can fail to accurately capture the dependencies between columns and make it difficult to preserve their relationships. To address these challenges, we introduce Dependency-aware Core Column Discovery (DaCo), an iterative method that uses a novel rough matching strategy to identify both inter-column dependencies and core columns. Unlike other methods, DaCo does not require labeled data or contextual information, making it suitable for practical scenarios. Additionally, it can identify multiple core columns within a table, which is common in real-world tables. Our experimental results demonstrate that DaCo outperforms existing core column discovery methods, substantially improving the efficiency of table understanding tasks.