Tables and figures are usually used to present information in a structured and visual way in scientific documents. Understanding the tables and figures in scientific documents is significant for a series of downstream tasks, such as academic search, ...

Relational Web tables provide valuable resources for numerous downstream applications, making table understanding, especially column annotation that identifies semantic types and relations of columns, a hot topic in the field of data management. Despite ...

In a relational table, core columns represent the primary subject entities that other columns in the table depend on. While discovering core columns is crucial for understanding a table’s semantic column types, column relations, and entities, it ...

Table retrieval aims to rank candidate tables for answering natural language query, in which the most critical problem is how to learn informative representations for structured tables. Most previous methods roughly flatten the table and send it ...

Tables are widely used for data storage and presentation due to their high flexibility in layout. The importance of tables as information carriers and the complexity of tabular data understanding attract a great deal of research on large-scale pre-...

Table is a widely used data form in webpages, spreadsheets, or PDFs to organize and present structural data. Although studies on table structure recognition have been successfully used to convert image-based tables into digital structural formats, ...

Inferring meta information about tables, such as column headers or relationships between columns, is an active research topic in data management as we find many tables are missing some of this information. In this paper, we study the problem of ...

Claims over the numerical relationships among some measures are commonly expressed in tabular forms, and widely exist in the published documents on the Web. This paper introduces the problem of numerical formula recognition from tables, namely ...

We are witnessing the widespread adoption of deep learning techniques as avant-garde solutions to different computational problems in recent years. In data integration, the use of deep learning techniques has helped establish several state-of-the-art ...

Tabular data provide answers to a significant portion of search queries. However, reciting an entire result table is impractical in conversational search systems. We propose to generate natural language summaries as answers to describe the complex ...

Descriptive titles provide crucial context for interpreting tables that are extracted from web pages and are a key component of search features such as tabular featured snippets from Google and Bing. Prior approaches have attempted to produce titles by ...

This tutorial is targeted at academics and practitioners, both within and outside of the Document Engineering community, who are confronted with table processing tasks such as information extraction and conversion, or have an interest in the topic, and ...

In this paper, we propose a precise, comprehensive model of table processing which aims to remedy some of the problems in the discussion of table processing in the literature. The model targets application-independent, end-to-end table processing, and ...

Table understanding is a well studied problem in document analysis, and many academic and commercial approaches have been developed to recognize tables in several document formats, including plain text, scanned page images and born-digital, object-based ...

The analysis of table structures and the retrieval of table contents is widely agreed to be a difficult challenge in the area of document analysis systems. Instead of extracting the layout of tables, we are interested in understanding their content. In ...

Tables are very important carriers of the vast information on the Internet and are widely used in web pages. However, most designs of web tables are only for desktop PCs and just focus on how to visually and logically show large amount of data without ...

At the heart of today's information-explosion problems are issues involving semantics, mutual understanding, concept matching, and interoperability. Ontologies and the Semantic Web are offered as a potential solution, but creating ontologies for real-...

Data on the Web in HTML tables is mostly structured, but we usually do not know the structure in advance. Thus, we cannot directly query for data of interest. We propose a solution to this problem based on document-independent extraction ontologies. Our ...