Information retrieval (IR) methods for KGQA consist of two stages: subgraph
extraction and answer reasoning. We argue current subgraph extraction methods
underestimate the importance of structural dependencies among evidence facts.
We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural
dependencies during subgraph extraction. We implement EPR by indexing the
atomic adjacency pattern of resource pairs. Given a question, we perform dense
retrieval to obtain atomic patterns formed by resource pairs. We then enumerate
their combinations to construct candidate evidence patterns. These evidence
patterns are scored using a neural model, and the best one is selected to
extract a subgraph for downstream answer reasoning. Experimental results
demonstrate that the EPR-based approach has significantly improved the F1
scores of IR-KGQA methods by over 10 points on ComplexWebQuestions and achieves
competitive performance on WebQuestionsSP.

信息检索中的知识图谱问答方法包括两个阶段：子图提取和答案推理。我们认为当前的子图提取方法低估了证据事实之间的结构依赖的重要性。我们提出了证据模式检索（EPR）来在子图提取过程中明确地建模结构依赖关系。通过对资源对的原子相邻模式进行索引，我们实现了 EPR。给定一个问题，我们进行密集检索，以获取由资源对形成的原子模式。然后，我们枚举它们的组合来构造候选证据模式。使用神经模型对这些证据模式进行评分，并选择最佳模式来提取下游答案推理所需的子图。实验结果表明，基于 EPR 的方法显著提高了在 ComplexWebQuestions 上 IR-KGQA 方法的 F1 得分，且在 WebQuestionsSP 上取得了竞争性的性能。

通过证据模式检索增强知识图谱上的复杂问题回答

Enhancing Complex Question Answering over Knowledge Graphs through  Evidence Pattern Retrieval

This paper proposes ISDC, a novel feedback-guided iterative system of
difference constraints (SDC) scheduling algorithm for high-level synthesis
(HLS). ISDC leverages subgraph extraction-based low-level feedback from
downstream tools like logic synthesizers to iteratively refine HLS scheduling.
Technical innovations include: (1) An enhanced SDC formulation that effectively
integrates low-level feedback into the linear-programming (LP) problem; (2) A
fanout and window-based subgraph extraction mechanism driving the feedback
cycle; (3) A no-human-in-loop ISDC flow compatible with a wide range of
downstream tools and process design kits (PDKs). Evaluation shows that ISDC
reduces register usage by 28.5% against an industrial-strength open-source HLS
tool.

该论文提出了一种名为 ISDC 的新型反馈引导的迭代系统差别约束（SDC）调度算法，用于高层综合（HLS）。ISDC 利用下游工具如逻辑综合器提供的基于子图提取的低层反馈，迭代改进 HLS 调度。技术创新包括：（1）增强的 SDC 形式化，有效将低层反馈整合到线性规划（LP）问题中；（2）基于扇出和窗口的子图提取机制驱动反馈循环；（3）无需人为干预的 ISDC 流程，与广泛范围的下游工具和流程设计工具包（PDKs）兼容。评估结果显示，ISDC 相比工业级开源 HLS 工具可以减少 28.5% 的寄存器使用。