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Found 14 papers, 4 papers with code
On Aligning OpenIE Extractions with Knowledge Bases: A Case Study
First, we investigate OPIEC triples and DBpedia facts having the same arguments by comparing the information on the OIE surface relation with the KB rela- tion.
What Makes a Good Paraphrase: Do Automated Evaluations Work?
Paraphrasing is the task of expressing an essential idea or meaning in different words.
Human-Centric Research for NLP: Towards a Definition and Guiding Questions
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users.
A Human-Centric Assessment Framework for AI
Inspired by the Turing test, we introduce a human-centric assessment framework where a leading domain expert accepts or rejects the solutions of an AI system and another domain expert.
milIE: Modular & Iterative Multilingual Open Information Extraction
In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction.
AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark
Open Information Extraction (OIE) is the task of extracting facts from sentences in the form of relations and their corresponding arguments in schema-free manner.
BenchIE: A Framework for Multi-Faceted Fact-Based Open Information Extraction Evaluation
In this work, we introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese, and German.
Ranked #1 on
Open Information Extraction
on BenchIE
Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders
Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries.
Attending to Future Tokens For Bidirectional Sequence Generation
Treated as a node in a fully connected graph, a placeholder token can take past and future tokens into consideration when generating the actual output token.
Abstract Graphs and Abstract Paths for Knowledge Graph Completion
Knowledge graphs, which provide numerous facts in a machine-friendly format, are incomplete.
Learning Numerical Attributes in Knowledge Bases
It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction.
Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs
We note a marked difference in the impact of these sampling methods on the two datasets, with the "traditional" corrupting positives method leading to best results on WN18, while embedding based methods benefiting the task on FB15k.
Learning Knowledge Graph Embeddings with Type Regularizer
Learning relations based on evidence from knowledge bases relies on processing the available relation instances.
