Web Data Commons Research Articles

Empirical Best Practices On Using Product-Specific Schema.org

Schema.org has experienced high growth in recent years. Structured descriptions of products embedded in HTML pages are now not uncommon, especially on e-commerce websites. The Web Data Commons (WDC) project has extracted schema.org data at scale from webpages in the Common Crawl and made it available as an RDF `knowledge graph' at scale. The portion of this data that specifically describes products offers a golden opportunity for researchers and small companies to leverage it for analytics and downstream applications. Yet, because of the broad and expansive scope of this data, it is not evident whether the data is usable in its raw form. In this paper, we do a detailed empirical study on the product-specific schema.org data made available by WDC. Rather than simple analysis, the goal of our study is to devise an empirically grounded set of best practices for using and consuming WDC product-specific schema.org data. Our studies reveal five best practices, each of which is justified by experimental data and analysis.

How Do Organizations Publish Semantic Markup? Three Case Studies Using Public Schema.org Crawls

Jointly launched in mid-2011 by major search engines like Google and Bing, Schema.org is designed to facilitate structured and knowledge graph–centric search applications on the Web. The Web Data Commons project has crawled increasing amounts of Schema.org data in recent years, providing a golden opportunity for socio-technological data studies that consider the semantics of content. The authors present empirical studies of organizations in three economic sectors that expose semantically linked Schema.org annotations.

Towards More Accurate Statistical Profiling of Deployed schema.org Microdata

Being promoted by major search engines such as Google, Yahoo!, Bing, and Yandex, Microdata embedded in web pages, especially using schema.org, has become one of the most important markup languages for the Web. However, deployed Microdata is very often not free from errors, which makes it difficult to estimate the data volume and create an accurate data profile. In addition, as the usage of global identifiers is not common, the real number of entities described by this format in the Web is hard to assess. In this article, we discuss how the subsequent application of data cleaning steps, such as duplicate detection and correction of common schema-based errors, leads to a more realistic view on the data, step by step. The cleaning steps applied include both heuristics for fixing errors as well as means to perform duplicate detection and duplicate elimination. Using the Web Data Commons Microdata corpus, we show that applying such quality improvement methods can essentially change the statistical profile of the dataset and lead to different estimates of both the number of entities as well as the class distribution within the data.