Extracting Relations from Web Tables by Leveraging Table Entity Behaviours

摘要

Relation extraction from web tables contributes to a range of key semantic web applications including semantic table interpretation, RDF triplet extraction, and knowledge base augmentation. The structure of a table inherently provides high quality semantic relations between its columns. This paper introduces RelX, a novel, non exhaustive, fast converging algorithm for extracting column relations from web tables. RelX leverages existing linked data to infer relationships between table entities by observing their behaviours in the data. On carrying out preliminary evaluations, this technique yielded high quality and high accuracy against a manually annotated data-set.