  Tree matching techniques have been investigated in many fields, including web
data mining and extraction, as a key component to analyze the content of web
documents, existing tree matching approaches, like Tree-Edit Distance (TED) or
Flexible Tree Matching (FTM), fail to scale beyond a few hundreds of nodes,
which is far below the average complexity of existing web online documents and
applications. In this paper, we therefore propose a novel Similarity-based
Flexible Tree Matching algorithm (SFTM), which is the first algorithm to enable
tree matching on real-life web documents with practical computation times. In
particular, we approach tree matching as an optimisation problem and we
leverage node labels and local topology similarity in order to avoid any
combinatorial explosion. Our practical evaluation demonstrates that our
approach compares to the reference implementation of TED qualitatively, while
improving the computation times by two orders of magnitude.
