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Putting Web Tables into Context

Web tables are a valuable source of information used in many application areas. However, to exploit Web 
 
tables it is necessary to understand their content and intention which is impeded by their ambiguous semantics 
 
and inconsistencies. Therefore, additional context information, e.g. text in which the tables are embedded, 
 
is needed to support the table understanding process. In this paper, we propose a novel contextualization 
 
approach that 1) splits the table context in topically coherent paragraphs, 2) provides a similarity measure 
 
that is able to match each paragraph to the table in question and 3) ranks these paragraphs according to their 
 
relevance. Each step is accompanied by an experimental evaluation on real-world data showing that our 
 
approach is feasible and effectively identifies the most relevant context for a given Web table.