论文标题
复杂金融主题的特定特定知识图
Query-Specific Knowledge Graphs for Complex Finance Topics
论文作者
论文摘要
在整个金融领域，研究人员通过广泛“搜索”相关信息来回答复杂的问题，以生成长形式的报告。该研讨会论文讨论了为复杂研究主题的特定特定文档和实体知识图（kgs）的构建。我们专注于编解码器数据集，该数据集（1）创建具有挑战性的问题，（2）构建长期的自然语言叙述，以及（3）迭代搜索和评估文档和实体的相关性。对于特定于查询的公斤的构建，我们表明最先进的排名系统具有改进的净空，并且由于缺乏上下文或明确的知识表示而导致特定的失败。我们证明实体和文档相关性是正相关的，基于实体的查询反馈提高了文档排名有效性。此外，我们使用检索构建了特定的kg，并使用编解码器的“地面真实图”进行评估，显示了精确和召回权衡。最后，我们指出了未来的工作，包括自适应KG检索算法和基于GNN的加权方法，同时突出了关键挑战，例如高质量数据，信息提取回忆以及复杂主题图的大小和稀疏性。
Across the financial domain, researchers answer complex questions by extensively "searching" for relevant information to generate long-form reports. This workshop paper discusses automating the construction of query-specific document and entity knowledge graphs (KGs) for complex research topics. We focus on the CODEC dataset, where domain experts (1) create challenging questions, (2) construct long natural language narratives, and (3) iteratively search and assess the relevance of documents and entities. For the construction of query-specific KGs, we show that state-of-the-art ranking systems have headroom for improvement, with specific failings due to a lack of context or explicit knowledge representation. We demonstrate that entity and document relevance are positively correlated, and that entity-based query feedback improves document ranking effectiveness. Furthermore, we construct query-specific KGs using retrieval and evaluate using CODEC's "ground-truth graphs", showing the precision and recall trade-offs. Lastly, we point to future work, including adaptive KG retrieval algorithms and GNN-based weighting methods, while highlighting key challenges such as high-quality data, information extraction recall, and the size and sparsity of complex topic graphs.
