mars-wei / knowledge_graph_reasoning_papers

Must-read papers on knowledge graph reasoning

Knowledge Graph Reasoning Papers

(h, r, ?)

Predict the missing tail entity and corresponding supporting paths in one triple.

1. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning. Wenhan Xiong, Thien Hoang, William Yang Wang. EMNLP 2017. paper code

   They 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.

2. Differentiable Learning of Logical Rules for Knowledge Base Reasoning. Fan Yang, Zhilin Yang, William W. Cohen. NIPS 2017. paper code

   They propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. They design a neural controller system that learns to compose differentiable reasoning operations.

3. Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum. ICLR 2018. paper code

   MINERVA addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity.

4. M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search. Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao. NIPS 2018. paper code

   M-Walk learns to walk over a graph towards a desired target node for given input query and source nodes. Specifically, this paper proposes a novel neural architecture that encodes the state into a vector representation, and maps it to Q-values and a policy.

5. Multi-Hop Knowledge Graph Reasoning with Reward Shaping. Xi Victoria Lin, Richard Socher, Caiming Xiong. EMNLP 2018. paper code

   This paper proposes two modeling advances for end-to-end RL-based knowledge graph query answering: (1) reward shaping via graph completion and (2) action dropout.

(h, ?, t)

Given head and tail entity and paths between them, predict the missing relation.

1. Random walk inference and learning in a large scale knowledge base. Ni Lao, Tom Mitchell, William W. Cohen. EMNLP 2011. paper

   This paper shows that the system can learn to infer different target relations by tuning the weights associated with random walks that follow different paths through the graph, using a version of the Path Ranking Algorithm.

2. Compositional vector space models for knowledge base inference. Arvind Neelakantan, Benjamin Roth, Andrew McCallum. ACL 2015. paper

   This paper presents an approach that reasons about conjunctions of multi-hop relations non-atomically, composing the implications of a path using a recurrent neural network (RNN) that takes as inputs vector embeddings of the binary relation in the path.

3. Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks. Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum. EACL 2017. paper code

   This paper proposes three significant modeling advances: (1) they learn to jointly reason about relations, entities, and entity-types; (2) they use neural attention modeling to incorporate multiple paths; (3) they learn to share strength in a single RNN that represents logical composition across all relations.

4. Variational Knowledge Graph Reasoning. Wenhu Chen, Wenhan Xiong, Xifeng Yan, William Yang Wang. NAACL 2018. paper

   This paper tackles apractical query answering task involving predicting the relation of a given entity pair. They frame this prediction problem as an inference problem in a probabilistic graphical model andaim at resolving it from a variational inference perspective.

Rules Learning

1. Differentiable Learning of Logical Rules for Knowledge Base Reasoning. Fan Yang, Zhilin Yang, William W. Cohen. NIPS 2017. paper code

   They propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. They design a neural controller system that learns to compose differentiable reasoning operations.

2. Scalable Rule Learning via Learning Representation. Pouya Ghiasnezhad Omran, Kewen Wang, Zhe Wang. IJCAI 2018. paper

   This paper presents a new approach RLvLR to learning rules from KGs by using the technique of embedding in representation learning together with a new sampling method. For massive KGs with hundreds of predicates and over 10M facts, RLvLR is much faster and can learn much more quality rules than major systems.

3. Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning. Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, Huajun Chen. WWW 2019. paper

   This paper explores how embedding and rule learning can be combined together and complement each other’s difficulties with their advantages.

4. RUGE: Knowledge Graph Embedding with Iterative Guidance from Soft Rules. Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo. AAAI 2018. paper code

   RUGE is the first work that models interactions between embedding learning and logical inference in a principled framework. It enables an embedding model to learn simultaneously from labeled triples, unlabeled triples and soft rules in an iterative manner.

5. Rule Learning from Knowledge Graphs Guided by Embedding Models. V. Thinh Ho, D. Stepanova, M. Gad-Elrab, E. Kharlamov, G. Weikum. ISWC 2018. paper

   They propose a rule learning method that utilizes probabilistic representations of missing facts. In particular, they iteratively extend rules induced from a KG by relying on feedback from a precomputed embedding model over the KG and external information sources including text corpora.

Complex Natural Language Query

1. (Dataset: WikiTableQuestions) Compositional Semantic Parsing on Semi-Structured Tables. Panupong Pasupat, Percy Liang. ACL 2015. paper code

   This paper creates a dataset of 22,033 complex questions on Wikipedia tables. They propose a logical-form driven parsing algorithm guided by strong typing constraints.

2. (Dataset: MetaQA) Variational Reasoning for Question Answering with Knowledge Graph. Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song. AAAI 2018. paper data

   This paper proposes an end-to-end variational learning algorithm which can handle noise in questions, and learn multi-hop reasoning simultaneously. Besides, they derive a series of new benchmark datasets named MetaQA, including questions for multi-hop reasoning, questions paraphrased by neural translation model, and questions in human voice.

Others

1. Embedding Logical Queries on Knowledge Graphs. William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec. NIPS 2018. paper

   This paper aims to develop techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. They introduce a framework to efficiently make predictions about conjunctive logical queries --— a flexible but tractable subset of first-order logic —-- on incomplete knowledge graphs.