Open Information Extraction
60 papers with code • 13 benchmarks • 13 datasets
In natural language processing, open information extraction is the task of generating a structured, machine-readable representation of the information in text, usually in the form of triples or n-ary propositions (Source: Wikipedia).
Benchmarks
These leaderboards are used to track progress in Open Information Extraction
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Datasets
Penn Treebank
New York Times Annotated Corpus
QA-SRL
CaRB
GenericsKB
hasPart KB
Most implemented papers
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).
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference
In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB).
Improving Open Information Extraction via Iterative Rank-Aware Learning
We found that the extraction likelihood, a confidence measure used by current supervised open IE systems, is not well calibrated when comparing the quality of assertions extracted from different sentences.
MinScIE: Citation-centered Open Information Extraction
Acknowledging the importance of citations in scientific literature, in this work we present MinScIE, an Open Information Extraction system which provides structured knowledge enriched with semantic information about citations.
Quantifying Similarity between Relations with Fact Distribution
thunlp/relation-similarity
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ACL 2019
We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases.
On the Possibility of Rewarding Structure Learning Agents: Mutual Information on Linguistic Random Sets
We present a first attempt to elucidate a theoretical and empirical approach to design the reward provided by a natural language environment to some structure learning agent.
CaRB: A Crowdsourced Benchmark for Open IE
We release the CaRB framework along with its crowdsourced dataset.
IMoJIE: Iterative Memory-Based Joint Open Information Extraction
While traditional systems for Open Information Extraction were statistical and rule-based, recently neural models have been introduced for the task.
Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction
samuelbroscheit/open_knowledge_graph_embeddings
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ACL 2020
An evaluation in such a setup raises the question if a correct prediction is actually a new fact that was induced by reasoning over the open knowledge graph or if it can be trivially explained.
Multi$^2$OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT
In this paper, we propose Multi$^2$OIE, which performs open information extraction (open IE) by combining BERT with multi-head attention.
