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Found 17 papers, 10 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.
AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents
The advances made by Large Language Models (LLMs) have led to the pursuit of LLM agents that can solve intricate, multi-step reasoning tasks.
Robust Text Classification: Analyzing Prototype-Based Networks
Downstream applications often require text classification models to be accurate, robust, and interpretable.
Linking Surface Facts to Large-Scale Knowledge Graphs
Open Information Extraction (OIE) methods extract facts from natural language text in the form of ("subject"; "relation"; "object") triples.
Large Language Models Enable Few-Shot Clustering
In this paper, we ask whether a large language model can amplify an expert's guidance to enable query-efficient, few-shot semi-supervised text clustering.
Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection
We address the problem of negative transfer in TD by coupling triggers between domains using subject-object relations obtained from a rule-based open information extraction (OIE) system.
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples.
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
Behavioral Testing of Knowledge Graph Embedding Models for Link Prediction
With an extensive set of experiments, we perform and analyze these tests for several KGE models.
Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction
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.
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.
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.
Open-Ended Question Answering
Open Information Extraction
+1
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.
