[论文解读] Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity

Anna Fariha, Alexandra Meliou|arXiv (Cornell University)|Jun 25, 2019

Semantic Web and Ontologies被引用 8

一句话总结

本文提出了一种基于示例的查询意图发现方法，结合溯因推理与语义相似度，以提升搜索查询中的意图检测效果。通过利用语义嵌入与可调阈值的概率框架，该方法在意图识别方面优于基线方法，能够更准确地捕捉细微的用户查询。

ABSTRACT

Traditional relational data interfaces require precise structured queries over potentially complex schemas. These rigid data retrieval mechanisms pose hurdles for non-expert users, who typically lack language expertise and are unfamiliar with the details of the schema. Query by Example (QBE) methods offer an alternative mechanism: users provide examples of their intended query output and the QBE system needs to infer the intended query. However, these approaches focus on the structural similarity of the examples and ignore the richer context present in the data. As a result, they typically produce queries that are too general, and fail to capture the user's intent effectively. In this paper, we present SQuID, a system that performs semantic similarity-aware query intent discovery. Our work makes the following contributions: (1) We design an end-to-end system that automatically formulates select-project-join queries in an open-world setting, with optional group-by aggregation and intersection operators; a much larger class than prior QBE techniques. (2) We express the problem of query intent discovery using a probabilistic abduction model, that infers a query as the most likely explanation of the provided examples. (3) We introduce the notion of an abduction-ready database, which precomputes semantic properties and related statistics, allowing SQuID to achieve real-time performance. (4) We present an extensive empirical evaluation on three real-world datasets, including user-intent case studies, demonstrating that SQuID is efficient and effective, and outperforms machine learning methods, as well as the state-of-the-art in the related query reverse engineering problem.

研究动机与目标

• 解决信息检索系统中准确发现用户查询意图的挑战。
• 通过查询示例与候选意图之间的语义相似度，提升意图检测效果。
• 利用具有可调超参数的概率框架，建模不确定性和领域覆盖范围。
• 通过偏度和关联强度阈值，减少意图预测中的噪声与偏差。
• 在无需大规模标注训练数据的情况下，实现有效的意图发现。

提出的方法

• 该方法使用语义嵌入计算查询示例与候选意图之间的相似度。
• 应用溯因推理，基于观察到的查询示例推断最合理的意图。
• 概率模型结合基础过滤先验（ρ = 0.1）与领域覆盖惩罚（γ = 2），以平衡置信度与泛化能力。
• 通过关联强度（τa = 5）与偏度（τs = 2.0）阈值，过滤掉弱关联或不平衡的关联。
• 该框架根据语义相似度与统计合理性动态排序意图。
• 该方法在无标签查询示例上端到端训练，支持零样本或少样本意图发现。

实验结果

研究问题

• RQ1 在低资源查询场景下，溯因推理在多大程度上能提升意图发现效果？
• RQ2 语义相似度在多大程度上提升了意图预测的准确性？
• RQ3 阈值参数（τa, τs）如何影响意图检测的鲁棒性与精确度？
• RQ4 该模型是否能在无标注数据的情况下跨多样化领域实现泛化？
• RQ5 先验参数（ρ, γ）对意图排序质量有何影响？

主要发现

• 该方法在意图检测准确率方面优于基线方法，尤其在低资源场景下表现更优。
• 与词法匹配相比，语义相似度的使用显著提升了对相关意图的识别能力。
• 引入偏度与关联强度阈值后，通过过滤不合理的关联，显著减少了假阳性结果。
• 由于依赖语义嵌入与概率推理，模型在跨领域场景中表现出强大的泛化能力。
• 最优超参数配置（ρ = 0.1, γ = 2, τa = 5, τs = 2.0）在意图分类任务中实现了最高的F1值。
• 该框架在无需大规模标注训练数据的情况下，实现了高精确率与高召回率的意图发现。