A Survey of Relation Extraction of Knowledge Graphs

2019 ◽  
pp. 52-66
Author(s):  
Aoran Li ◽  
Xinmeng Wang ◽  
Wenhuan Wang ◽  
Anman Zhang ◽  
Bohan Li
Keyword(s):  
Relation Extraction ◽  
Knowledge Graphs

scholarly journals Populating Web-Scale Knowledge Graphs Using Distantly Supervised Relation Extraction and Validation

Information ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 316
Author(s):  
Sarthak Dash ◽  
Michael R. Glass ◽  
Alfio Gliozzo ◽  
Mustafa Canim ◽  
Gaetano Rossiello
Keyword(s):  
Deep Learning ◽  
Relation Extraction ◽  
Automated System ◽  
External Information ◽  
Learning Approach ◽  
Wide Margin ◽  
Structure Information ◽  
Adaptation Cost ◽  
Knowledge Graphs ◽  
Scale Experiment

In this paper, we propose a fully automated system to extend knowledge graphs using external information from web-scale corpora. The designed system leverages a deep-learning-based technology for relation extraction that can be trained by a distantly supervised approach. In addition, the system uses a deep learning approach for knowledge base completion by utilizing the global structure information of the induced KG to further refine the confidence of the newly discovered relations. The designed system does not require any effort for adaptation to new languages and domains as it does not use any hand-labeled data, NLP analytics, and inference rules. Our experiments, performed on a popular academic benchmark, demonstrate that the suggested system boosts the performance of relation extraction by a wide margin, reporting error reductions of 50%, resulting in relative improvement of up to 100%. Furthermore, a web-scale experiment conducted to extend DBPedia with knowledge from Common Crawl shows that our system is not only scalable but also does not require any adaptation cost, while yielding a substantial accuracy gain.

Large-scale relation extraction from web documents and knowledge graphs with human-in-the-loop

2020 ◽  
Vol 60 ◽  
pp. 100546
Author(s):  
Petar Ristoski ◽  
Anna Lisa Gentile ◽  
Alfredo Alba ◽  
Daniel Gruhl ◽  
Steven Welch
Keyword(s):  
Large Scale ◽  
Relation Extraction ◽  
Web Documents ◽  
Human In The Loop ◽  
Knowledge Graphs

scholarly journals A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs

2022 ◽  
Vol 12 (2) ◽  
pp. 715
Author(s):  
Luodi Xie ◽  
Huimin Huang ◽  
Qing Du
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Space ◽  
Knowledge Graph ◽  
High Quality ◽  
Gaussian Distributions ◽  
Benchmark Datasets ◽  
Semantic Spaces ◽  
Knowledge Graphs ◽  
Low Dimensional

Knowledge graph (KG) embedding has been widely studied to obtain low-dimensional representations for entities and relations. It serves as the basis for downstream tasks, such as KG completion and relation extraction. Traditional KG embedding techniques usually represent entities/relations as vectors or tensors, mapping them in different semantic spaces and ignoring the uncertainties. The affinities between entities and relations are ambiguous when they are not embedded in the same latent spaces. In this paper, we incorporate a co-embedding model for KG embedding, which learns low-dimensional representations of both entities and relations in the same semantic space. To address the issue of neglecting uncertainty for KG components, we propose a variational auto-encoder that represents KG components as Gaussian distributions. In addition, compared with previous methods, our method has the advantages of high quality and interpretability. Our experimental results on several benchmark datasets demonstrate our model’s superiority over the state-of-the-art baselines.

Merging Web Tables for Relation Extraction with Knowledge Graphs

2021 ◽  
pp. 1-1
Author(s):  
Jhomara Luzuriaga ◽  
Emir Munoz ◽  
Henry Rosales-Mendez ◽  
Aidan Hogan
Keyword(s):  
Relation Extraction ◽  
Knowledge Graphs

scholarly journals Neural Snowball for Few-Shot Relation Learning

2020 ◽  
Vol 34 (05) ◽  
pp. 7772-7779 ◽  
Author(s):  
Tianyu Gao ◽  
Xu Han ◽  
Ruobing Xie ◽  
Zhiyuan Liu ◽  
Fen Lin ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Semantic Knowledge ◽  
Training Data ◽  
High Quality ◽  
Knowledge Graphs ◽  
Relation Learning ◽  
Siamese Networks

Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient training data. To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations. More specifically, we use Relational Siamese Networks (RSN) to learn the metric of relational similarities between instances based on existing relations and their labeled data. Afterwards, given a new relation and its few-shot instances, we use RSN to accumulate reliable instances from unlabeled corpora; these instances are used to train a relation classifier, which can further identify new facts of the new relation. The process is conducted iteratively like a snowball. Experiments show that our model can gather high-quality instances for better few-shot relation learning and achieves significant improvement compared to baselines. Codes and datasets are released on https://github.com/thunlp/Neural-Snowball.

scholarly journals Expanding a Database-derived Biomedical Knowledge Graph via Multi-relation Extraction from Biomedical Abstracts

2019 ◽  
Author(s):  
David N. Nicholson ◽  
Daniel S. Himmelstein ◽  
Casey S. Greene
Keyword(s):  
Contextual Information ◽  
Relation Extraction ◽  
Publication Rate ◽  
Knowledge Graph ◽  
Biomedical Knowledge ◽  
Text Annotation ◽  
Label Function ◽  
Manual Curation ◽  
Function Combination ◽  
Knowledge Graphs

AbstractKnowledge graphs support multiple research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via some form of manual curation, which is difficult to scale in the context of an increasing publication rate. Data programming is a paradigm that circumvents this arduous manual process by combining databases with simple rules and heuristics written as label functions, which are programs designed to automatically annotate textual data. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This makes populating a knowledge graph with multiple nodes and edge types practically infeasible. We sought to accelerate the label function creation process by evaluating the extent to which label functions could be re-used across multiple edge types. We used a subset of an existing knowledge graph centered on disease, compound, and gene entities to evaluate label function re-use. We determined the best label function combination by comparing a baseline database-only model with the same model but added edge-specific or edge-mismatch label functions. We confirmed that adding additional edge-specific rather than edge-mismatch label functions often improves text annotation and shows that this approach can incorporate novel edges into our source knowledge graph. We expect that continued development of this strategy has the potential to swiftly populate knowledge graphs with new discoveries, ensuring that these resources include cutting-edge results.

scholarly journals A Brief Review of Relation Extraction Based on Pre-Trained Language Models

2020 ◽  
Author(s):  
Tiange Xu ◽  
Fu Zhang
Keyword(s):  
Recurrent Neural Networks ◽  
Rapid Development ◽  
Relation Extraction ◽  
Language Models ◽  
Research Progress ◽  
Future Research ◽  
Distant Supervision ◽  
Future Research Directions ◽  
Knowledge Graphs ◽  
Key Techniques

Relation extraction is to extract the semantic relation between entity pairs in text, and it is a key point in building Knowledge Graphs and information extraction. The rapid development of deep learning in recent years has resulted in rich research results in relation extraction tasks. At present, the accuracy of relation extraction tasks based on pre-trained language models such as BERT exceeds the methods based on Convolutional or Recurrent Neural Networks. This review mainly summarizes the research progress of pre-trained language models such as BERT in supervised learning and distant supervision relation extraction. In addition, the directions for future research and some comparisons and analyses are discussed in our whole survey. The survey may help readers understand and catch some key techniques about the issue, and identify some future research directions.

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