The Web has evolved into a huge mine of knowledge carved in different forms, the predominant one still being the free-text document.
This motivates the need for intelligent Web-reading agents: hypothetically, they would skim through disparate Web sources corpora and generate meaningful structured assertions to fuel knowledge bases (KBs).
Ultimately, comprehensive KBs, like Wikidata and DBpedia, play a fundamental role to cope with the issue of information overload.
On account of such vision, this paper depicts the Fact Extractor, a complete natural language processing (NLP) pipeline which reads an input textual corpus and produces machine-readable statements.
Each statement is supplied with a confidence score and undergoes a disambiguation step via entity linking, thus allowing the assignment of KB-compliant URIs.
The system implements four research contributions: it (1) executes n-ary relation extraction by applying the frame semantics linguistic theory, as opposed to binary techniques; it (2) simultaneously populates both the T-Box and the A-Box of the target KB; it (3) relies on a single NLP layer, namely part-of-speech tagging; it (4) enables a completely supervised yet reasonably priced machine learning environment through a crowdsourcing strategy.
We assess our approach by setting the target KB to DBpedia and by considering a use case of 52,000 Italian Wikipedia soccer player articles.
Out of those, we yield a dataset of more than 213,000 triples with an estimated 81.27% F1.
We corroborate the evaluation via (i) a performance comparison with a baseline system, as well as (ii) an analysis of the T-Box and A-Box augmentation capabilities.
The outcomes are incorporated into the Italian DBpedia chapter, can be queried through its SPARQL endpoint, and/or downloaded as standalone data dumps.
The codebase is released as free software and is publicly available in the DBpedia association repository.