Learning To Rank Resources with GNN
Learning To Rank Resources with GNN
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DOI：
10.1145/3543507.3583360
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Ulugbek Ergashev;Eduard Constantin Dragut;W. Meng
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文献类型：
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作者：
Ulugbek Ergashev;Eduard Constantin Dragut;W. Meng
随着Internet上内容的不断增长，许多新的动态变化的异构数据源不断出现。传统的搜索引擎无法跟上互联网扩张的步伐。此外，互联网上的大部分数据是传统搜索引擎无法访问的。分布式信息检索（DIR）是一种可行的解决方案，因为它集成了多个碎片（资源）并提供对它们的统一访问。资源选择是DIR系统的关键组成部分。关于DIR的资源选择方法有大量的文献。现有方法的一个关键限制是，它们主要使用基于术语的统计特征，通常不为资源查询和资源-资源关系建模。本文提出了一种基于图神经网络（GNN）的排序学习方法，该方法能够对资源查询和资源关系进行建模。具体来说，我们利用预训练语言模型（PTLM）从查询和资源中获取语义信息。然后，我们显式构建异构图来保存查询-资源关系的结构信息，并利用GNN提取结构信息。此外，在异构图中丰富了资源-资源类型的边，进一步提高了排序精度。在基准数据集上的大量实验表明，我们提出的方法在资源选择方面是非常有效的。在各种性能指标上，我们的方法比最先进的方法高出6.4%到42%。
As the content on the Internet continues to grow, many new dynamically changing and heterogeneous sources of data constantly emerge. A conventional search engine cannot crawl and index at the same pace as the expansion of the Internet. Moreover, a large portion of the data on the Internet is not accessible to traditional search engines. Distributed Information Retrieval (DIR) is a viable solution to this as it integrates multiple shards (resources) and provides a unified access to them. Resource selection is a key component of DIR systems. There is a rich body of literature on resource selection approaches for DIR. A key limitation of the existing approaches is that they primarily use term-based statistical features and do not generally model resource-query and resource-resource relationships. In this paper, we propose a graph neural network (GNN) based approach to learning-to-rank that is capable of modeling resource-query and resource-resource relationships. Specifically, we utilize a pre-trained language model (PTLM) to obtain semantic information from queries and resources. Then, we explicitly build a heterogeneous graph to preserve structural information of query-resource relationships and employ GNN to extract structural information. In addition, the heterogeneous graph is enriched with resource-resource type of edges to further enhance the ranking accuracy. Extensive experiments on benchmark datasets show that our proposed approach is highly effective in resource selection. Our method outperforms the state-of-the-art by 6.4% to 42% on various performance metrics.