LIST：学习索引空间-文本数据以支持基于嵌入的空间关键词查询
LIST: Learning to Index Spatio-Textual Data for Embedding based Spatial Keyword Queries
AI总结：
提出LIST，一种基于机器学习的近似最近邻搜索索引，通过学习聚类和伪标签生成技术高效索引空间-文本数据，并集成空间与文本相关性模型实现轻量有效的对象重排序。
摘要：
随着空间-文本数据的激增，Top-k KNN空间关键词查询（TkQs）基于综合考虑空间和文本相关性的排序函数返回对象列表，已在许多现实应用中得到广泛使用。为了高效处理TkQs，人们开发了许多索引，但TkQ的有效性有限。为了提高有效性，最近提出了几种深度学习模型，但它们存在严重的效率问题，并且没有专门设计的高效索引来加速这些深度学习模型的top-k搜索过程。为了解决这些问题，我们考虑基于嵌入的空间关键词查询，该查询在两个独立的嵌入中捕获查询关键词和对象描述的语义含义，以评估文本相关性。尽管可以使用各种模型来生成这些嵌入，但目前还没有专门为这类查询设计的索引。为了填补这一空白，我们提出了LIST，一种新颖的基于机器学习的近似最近邻搜索索引，它学习索引空间-文本数据。LIST利用一种新的学习聚类技术将相关查询和对象分组在一起，同时将不相关的查询和对象分开。构建有效且高效的索引面临两个关键挑战，即缺乏高质量标签和聚类结果不平衡。我们开发了一种新颖的伪标签生成技术来解决这两个挑战。此外，我们引入了一种基于学习的空间相关性模型，该模型可以与各种文本相关性模型集成，形成一种轻量但有效的相关性，用于对LIST检索到的对象进行重排序。
英文摘要：
With the proliferation of spatio-textual data, Top-k KNN spatial keyword queries (TkQs), which return a list of objects based on a ranking function that considers both spatial and textual relevance, have found many real-life applications. To efficiently handle TkQs, many indexes have been developed, but the effectiveness of TkQ is limited. To improve effectiveness, several deep learning models have recently been proposed, but they suffer severe efficiency issues and there are no efficient indexes specifically designed to accelerate the top-k search process for these deep learning models. To tackle these issues, we consider embedding based spatial keyword queries, which capture the semantic meaning of query keywords and object descriptions in two separate embeddings to evaluate textual relevance. Although various models can be used to generate these embeddings, no indexes have been specifically designed for such queries. To fill this gap, we propose LIST, a novel machine learning based Approximate Nearest Neighbor Search index that Learns to Index the Spatio-Textual data. LIST utilizes a new learning-to-cluster technique to group relevant queries and objects together while separating irrelevant queries and objects. There are two key challenges in building an effective and efficient index, i.e., the absence of high-quality labels and the unbalanced clustering results. We develop a novel pseudo-label generation technique to address the two challenges. Additionally, we introduce a learning based spatial relevance model that can integrates with various text relevance models to form a lightweight yet effective relevance for reranking objects retrieved by LIST.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
