Knowledge Repre… (2018-12-28T00:00:00.000000Z)

TL;DR

This article extensively conduct and quantitative comparison and analysis of several typical KRL methods on three evaluation tasks of knowledge acquisition including knowledge graph completion, triple classification, and relation extraction.

Abstract

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. In this article, we introduce the reader to the motivations for KRL, and overview existing approaches for KRL. Afterwards, we extensively conduct and quantitative comparison and analysis of several typical KRL methods on three evaluation tasks of knowledge acquisition including knowledge graph completion, triple classification, and relation extraction. We also review the real-world applications of KRL, such as language modeling, question answering, information retrieval, and recommender systems. Finally, we discuss the remaining challenges and outlook the future directions for KRL. The codes and datasets used in the experiments can be found in this https URL.

Authors

Xu Han

16 papers

Zhiyuan Liu

34 papers

Yankai Lin

9 papers

References130 items

Improving Visual Relationship Detection Using Semantic Modeling of Scene Descriptions

Flexible End-to-End Dialogue System for Knowledge Grounded Conversation

Iterative Entity Alignment via Joint Knowledge Embeddings

TransNet: Translation-Based Network Representation Learning for Social Relation Extraction

Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning

TL;DR

Abstract

Authors

Xu Han

16 papers

Zhiyuan Liu

34 papers

Yankai Lin

9 papers

TL;DR

Abstract

Authors

References130 items

Improving Visual Relationship Detection Using Semantic Modeling of Scene Descriptions

Flexible End-to-End Dialogue System for Knowledge Grounded Conversation

Iterative Entity Alignment via Joint Knowledge Embeddings

TransNet: Translation-Based Network Representation Learning for Social Relation Extraction

Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning

TL;DR

Abstract

Authors

References130 items

Improving Visual Relationship Detection Using Semantic Modeling of Scene Descriptions

Flexible End-to-End Dialogue System for Knowledge Grounded Conversation

Iterative Entity Alignment via Joint Knowledge Embeddings

TransNet: Translation-Based Network Representation Learning for Social Relation Extraction

Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning

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