利用关联数据网络进行产品数据采掘的探索性研究
An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining
摘要：
在过去的十年中，关联开放数据实践导致了网络上结构化数据的显著增长。这种结构化数据以机器可读的方式描述真实世界的实体，并为自然语言处理领域的研究创造了前所未有的机会。然而，缺乏关于如何使用这些数据、用于何种任务以及这些数据在多大程度上对这些任务有用的研究。这项工作的重点是电子商务领域，探索利用这种结构化数据来创建语言资源，可用于产品分类和链接的方法。我们以RDF n-quads的形式处理数十亿个结构化数据点，以创建数百万个单词的产品相关语料库，这些语料库随后以三种不同的方式用于创建语言资源：训练词嵌入模型，继续预训练BERT类语言模型，以及训练用作代理的机器翻译模型以生成产品相关关键字。我们对一组广泛的基准测试的评估表明，词嵌入是提高这两项任务准确性的最可靠和一致的方法（在某些数据集上，宏观平均F1高达6.9个百分点）。但是，其他两种方法并不那么有用。我们的分析表明，这可能是由于一些原因，包括结构化数据中有偏见的领域表示和缺乏词汇覆盖。我们分享了我们的数据集，并讨论了如何将我们学到的经验教训用于未来的研究。
英文摘要：
The Linked Open Data practice has led to a significant growth of structured data on the Web in the last decade. Such structured data describe real-world entities in a machine-readable way, and have created an unprecedented opportunity for research in the field of Natural Language Processing. However, there is a lack of studies on how such data can be used, for what kind of tasks, and to what extent they can be useful for these tasks. This work focuses on the e-commerce domain to explore methods of utilising such structured data to create language resources that may be used for product classification and linking. We process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating of language resources: training word embedding models, continued pre-training of BERT-like language models, and training Machine Translation models that are used as a proxy to generate product-related keywords. Our evaluation on an extensive set of benchmarks shows word embeddings to be the most reliable and consistent method to improve the accuracy on both tasks (with up to 6.9 percentage points in macro-average F1 on some datasets). The other two methods however, are not as useful. Our analysis shows that this could be due to a number of reasons, including the biased domain representation in the structured data and lack of vocabulary coverage. We share our datasets and discuss how our lessons learned could be taken forward to inform future research in this direction.
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。
