Work

DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

Year: 2017

Type: conference-paper

Abstract: We study the problem of learning to reason in large scale knowledge graphs (KGs).More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path.In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration.Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.1

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d17-1060

Authors: Wenhan Xiong, Thien Hoang, William Yang Wang

Institution: University of California, Santa Barbara

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Nvidia

OpenAlex ID: W2962886429