Open Information Extraction
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Graphene: Semantically-Linked Propositions in Open Information Extraction
COLING 2018 • Lambda-3/Graphene
We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification.
CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information
Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large Open Knowledge Bases (Open KBs).
FEATURE ENGINEERING OPEN INFORMATION EXTRACTION OPEN KNOWLEDGE GRAPH CANONICALIZATION
A Consolidated Open Knowledge Representation for Multiple Texts
We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner.
Integrating Local Context and Global Cohesiveness for Open Information Extraction
However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions.
OPIEC: An Open Information Extraction Corpus
In this paper, we release, describe, and analyze an OIE corpus called OPIEC, which was extracted from the text of English Wikipedia.
MinIE: Minimizing Facts in Open Information Extraction
The goal of Open Information Extraction (OIE) is to extract surface relations and their arguments from natural-language text in an unsupervised, domain-independent manner.
OPEN INFORMATION EXTRACTION QUESTION ANSWERING RELATION EXTRACTION
Answering Complex Questions Using Open Information Extraction
ACL 2017 • allenai/semanticilp
While there has been substantial progress in factoid question-answering (QA), answering complex questions remains challenging, typically requiring both a large body of knowledge and inference techniques.
Quantifying Similarity between Relations with Fact Distribution
ACL 2019
• thunlp/relation-similarity
• 
We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases.