N-ary relation extraction for simultaneous T-Box and A-Box knowledge base augmentation

@article{Fossati2018NaryRE,
  title={N-ary relation extraction for simultaneous T-Box and A-Box knowledge base augmentation},
  author={Marco Fossati and Emilio Dorigatti and Claudio Giuliano},
  journal={Semantic Web},
  year={2018},
  volume={9},
  pages={413-439}
}
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… 
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References

SHOWING 1-10 OF 75 REFERENCES
Leveraging Linguistic Structure For Open Domain Information Extraction
TLDR
This work replaces this large pattern set with a few patterns for canonically structured sentences, and shifts the focus to a classifier which learns to extract self-contained clauses from longer sentences to determine the maximally specific arguments for each candidate triple.
Open Language Learning for Information Extraction
Open Information Extraction (IE) systems extract relational tuples from text, without requiring a pre-specified vocabulary, by identifying relation phrases and associated arguments in arbitrary
FrameBase: Representing N-Ary Relations Using Semantic Frames
TLDR
FrameBase is presented, a wide-coverage knowledge-base schema that uses linguistic frames to seamlessly represent and query n-ary relations from other knowledge bases, at different levels of granularity connected by logical entailment.
FrameBase: Enabling integration of heterogeneous knowledge
Large-scale knowledge graphs such as those in the Linked Data cloud are typically represented as subjectpredicate-object triples. However, many facts about the world involve more than two entities.
Open Information Extraction Using Wikipedia
TLDR
WOE is presented, an open IE system which improves dramatically on TextRunner's precision and recall and is a novel form of self-supervised learning for open extractors -- using heuristic matches between Wikipedia infobox attribute values and corresponding sentences to construct training data.
Identifying Relations for Open Information Extraction
TLDR
Two simple syntactic and lexical constraints on binary relations expressed by verbs are introduced in the ReVerb Open IE system, which more than doubles the area under the precision-recall curve relative to previous extractors such as TextRunner and woepos.
Enriching Structured Knowledge with Open Information
TLDR
This work presents a complete, ontology independent, generalized workflow which is evaluated on facts extracted by Nell and Reverb and indicates that the clustering of relational phrases pays of in terms of an improved instance and property mapping.
Integrating Heterogeneous Knowledge with FrameBase
TLDR
FrameBase is presented, a wide-coverage knowledge-base schema that uses linguistic frames to represent and query n-ary relations from other knowledge bases, providing also different levels of granularity connected by logical entailment, providing for flexible and expressive seamless semantic integration from heterogeneous sources.
Semantic Parsing via Paraphrasing
TLDR
This paper presents two simple paraphrase models, an association model and a vector space model, and trains them jointly from question-answer pairs, improving state-of-the-art accuracies on two recently released question-answering datasets.
From hyperlinks to Semantic Web properties using Open Knowledge Extraction
TLDR
This work proposes a novel paradigm, named Open Knowledge Extraction, and its implementation that performs unsuper- vised, open domain, and abstractive knowledge extraction from text for producing machine readable information.
...
...