A 2-phase frame-based knowledge extraction framework

We present an approach for extracting knowledge from natural language English texts where processing is decoupled in two phases. The first phase comprises several standard NLP tasks whose results are integrated in a single RDF graph of mentions. The second phase processes the mention graph with SPARQL-like mapping rules to produce a knowledge graph organized around semantic frames (i.e., prototypical descriptions of events and situations). The decoupling allows: (i) choosing different tools for the NLP tasks without affecting the remaining computation; (ii) combining the outputs of different NLP tasks in non-trivial ways, leveraging their integrated and coherent representation in a mention graph; and (iii) relating each piece of extracted knowledge to the mention(s) it comes from, leveraging the single RDF representation. We evaluate precision and recall of our approach on a gold standard, showing its competitiveness w.r.t. the state of the art. We also evaluate execution times and (sampled) accuracy on a corpus of 110K Wikipedia pages, showing the applicability of the approach on large corpora.