Noname manuscript No. (will be inserted by the editor) Harvesting Relational Tables from Lists on the Web

Citations in the corpus

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, methodology): “extracting relational data in the formof vertical tables and lists and leveraging knowledge bases [27], [28].”
• TEGRA: Table Extraction by Global Record Alignment (2015, methodology): “We note that previous studies [16] as well as our own show that the majority (over 95%) of Web tables have less than 10 columns, in which case the unoptimized TEGRA can already finish within 4 seconds on average as shown in Figures 9(a).”
• Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “types of data and these types of data also need to be linked with the knowledge base, such as web tables [140], [141], web lists [142], [143], and tweets [144], [145].”
• Navigating the Data Lake with DATAMARAN: Automatically Extracting Structure from Log Datasets (2017, background): “, extraction from lists on the web is another area that has seen some work [15, 18, 28, 39, 63].”
• Understandable Big Data: A survey (2015, unknown): “[67–69].”
• Data Integration for the Relational Web (2009, methodology): “We extract data from HTML tables using techniques described in [3], and process HTML lists using techniques from [9].”
• Ten Years of WebTables (2018, background): “However, relational data are also encoded as attribute-value pairs in the form of vertical tables (e.g., Wikipedia infoboxes), or as entries in lists [16, 13].”
• Big data integration (2013, methodology): “More recent efforts include extracting and integrating structured data from Web tables [4], [40] and Web lists [21], [15].”
• Automatic web-scale information extraction (2012, background): “E.g. WebTables [4, 8] extracts all simple tables and lists from the Web and store them as relational data.”