Browse > Knowledge Base > Knowledge Graphs

Knowledge Graphs

67 papers with code · Knowledge Base

State-of-the-art leaderboards

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

Greatest papers with code

OpenKE: An Open Toolkit for Knowledge Embedding

EMNLP 2018 thunlp/OpenKE

We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space.

INFORMATION RETRIEVAL KNOWLEDGE GRAPHS QUESTION ANSWERING REPRESENTATION LEARNING

Modeling Relational Data with Graph Convolutional Networks

17 Mar 2017tkipf/gae

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

INFORMATION RETRIEVAL KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION

ERNIE: Enhanced Language Representation with Informative Entities

17 May 2019thunlp/ERNIE

Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.

ENTITY TYPING KNOWLEDGE GRAPHS NATURAL LANGUAGE INFERENCE RELATION CLASSIFICATION SENTIMENT ANALYSIS

Convolutional 2D Knowledge Graph Embeddings

5 Jul 2017TimDettmers/ConvE

In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets.

KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

Learning Deep Generative Models of Graphs

ICLR 2018 snap-stanford/GraphRNN

Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry.

KNOWLEDGE GRAPHS

PaperRobot: Incremental Draft Generation of Scientific Ideas

20 May 2019EagleW/PaperRobot

We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper.

KNOWLEDGE GRAPHS

Knowledge Graph Completion via Complex Tensor Factorization

22 Feb 2017ttrouill/complex

In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.

KNOWLEDGE GRAPH COMPLETION LINK PREDICTION RELATIONAL REASONING

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

HLT 2018 cai-lw/KBGAN

This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

Position-aware Attention and Supervised Data Improve Slot Filling

EMNLP 2017 yuhaozhang/tacred-relation

The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.

KNOWLEDGE BASE POPULATION KNOWLEDGE GRAPHS RELATION EXTRACTION SLOT FILLING