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Greatest papers with code

Knowledge Representation Learning: A Quantitative Review

28 Dec 2018thunlp/OpenKE

Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks.

CLASSIFICATION INFORMATION RETRIEVAL KNOWLEDGE GRAPH COMPLETION LANGUAGE MODELLING QUESTION ANSWERING RECOMMENDATION SYSTEMS RELATION EXTRACTION REPRESENTATION LEARNING TRIPLE CLASSIFICATION

KG-BERT: BERT for Knowledge Graph Completion

7 Sep 2019yao8839836/kg-bert

Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.

Ranked #3 on Link Prediction on FB15k-237 (MR metric)

KNOWLEDGE GRAPH COMPLETION LANGUAGE MODELLING LINK PREDICTION TRIPLE CLASSIFICATION

Differentiating Concepts and Instances for Knowledge Graph Embedding

EMNLP 2018 davidlvxin/TransC

Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances.

KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPHS LINK PREDICTION TRIPLE CLASSIFICATION

CoDEx: A Comprehensive Knowledge Graph Completion Benchmark

EMNLP 2020 tsafavi/codex

We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty.

KNOWLEDGE GRAPH COMPLETION LINK PREDICTION TRIPLE CLASSIFICATION

A Relational Memory-based Embedding Model for Triple Classification and Search Personalization

ACL 2020 daiquocnguyen/R-MeN

Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems.

CLASSIFICATION KNOWLEDGE GRAPH EMBEDDING TRIPLE CLASSIFICATION

Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence

9 May 2017thunlp/CKRL

Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of CKRL modeling confidence with structural information in both KG noise detection and knowledge representation learning.

KNOWLEDGE GRAPH COMPLETION REPRESENTATION LEARNING TRIPLE CLASSIFICATION

Image-embodied Knowledge Representation Learning

22 Sep 2016thunlp/IKRL

More specifically, we first construct representations for all images of an entity with a neural image encoder.

CLASSIFICATION KNOWLEDGE GRAPH COMPLETION REPRESENTATION LEARNING TRIPLE CLASSIFICATION

On the Role of Conceptualization in Commonsense Knowledge Graph Construction

6 Mar 2020mutiann/ccc

Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially different from conventional KGs as they consist of much larger number of nodes formed by loosely-structured text, which, though, enables them to handle highly diverse queries in natural language related to commonsense, leads to unique challenges for automatic KG construction methods.

GRAPH CONSTRUCTION KNOWLEDGE GRAPHS TRIPLE CLASSIFICATION

Reasoning on Knowledge Graphs with Debate Dynamics

2 Jan 2020m-hildebrandt/R2D2

The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively.

CLASSIFICATION KNOWLEDGE GRAPHS LINK PREDICTION TRIPLE CLASSIFICATION

TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces

1 Jul 2020SoyeonTiffanyMin/TransINT

We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space.

KNOWLEDGE GRAPHS LINK PREDICTION TRIPLE CLASSIFICATION