Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
暂无分享,去创建一个
Wei Zhang | Jiaoyan Chen | Abraham Bernstein | Hai Zhu | Huajun Chen | Liang Wang | Bibek Paudel | Wen Zhang | Wei Zhang | A. Bernstein | B. Paudel | Jiaoyan Chen | Wen Zhang | Huajun Chen | Liang Wang | Hai Zhu
[1] Evgeny Kharlamov,et al. Rule Learning from Knowledge Graphs Guided by Embedding Models , 2018, SEMWEB.
[2] Yiming Yang,et al. Analogical Inference for Multi-relational Embeddings , 2017, ICML.
[3] Li Guo,et al. Jointly Embedding Knowledge Graphs and Logical Rules , 2016, EMNLP.
[4] Nicholas Jing Yuan,et al. Collaborative Knowledge Base Embedding for Recommender Systems , 2016, KDD.
[5] Heiko Paulheim,et al. RDF2Vec: RDF Graph Embeddings for Data Mining , 2016, SEMWEB.
[6] Zhen Wang,et al. Knowledge Graph and Text Jointly Embedding , 2014, EMNLP.
[7] Lorenzo Rosasco,et al. Holographic Embeddings of Knowledge Graphs , 2015, AAAI.
[8] Evgeniy Gabrilovich,et al. A Review of Relational Machine Learning for Knowledge Graphs , 2015, Proceedings of the IEEE.
[9] Jianfeng Gao,et al. Embedding Entities and Relations for Learning and Inference in Knowledge Bases , 2014, ICLR.
[10] Thomas Demeester,et al. Lifted Rule Injection for Relation Embeddings , 2016, EMNLP.
[11] Wenhan Xiong,et al. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning , 2017, EMNLP.
[12] Kewen Wang,et al. Scalable Rule Learning via Learning Representation , 2018, IJCAI.
[13] Hans-Peter Kriegel,et al. A Three-Way Model for Collective Learning on Multi-Relational Data , 2011, ICML.
[14] Fan Yang,et al. Differentiable Learning of Logical Rules for Knowledge Base Reasoning , 2017, NIPS.
[15] Praveen Paritosh,et al. Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.
[16] Steven Schockaert,et al. From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules , 2018, KR.
[17] Minlie Huang,et al. SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions , 2016, AAAI.
[18] Fabian M. Suchanek,et al. AMIE: association rule mining under incomplete evidence in ontological knowledge bases , 2013, WWW.
[19] Yelong Shen,et al. Modeling Large-Scale Structured Relationships with Shared Memory for Knowledge Base Completion , 2016, Rep4NLP@ACL.
[20] Jun Zhao,et al. Knowledge Graph Embedding via Dynamic Mapping Matrix , 2015, ACL.
[21] Alexander J. Smola,et al. Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning , 2017, ICLR.
[22] Antoine Bordes,et al. Composing Relationships with Translations , 2015, EMNLP.
[23] Juan-Zi Li,et al. Text-Enhanced Representation Learning for Knowledge Graph , 2016, IJCAI.
[24] Li Guo,et al. Semantically Smooth Knowledge Graph Embedding , 2015, ACL.
[25] Jason Weston,et al. Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.
[26] Seung-won Hwang,et al. KBQA: Learning Question Answering over QA Corpora and Knowledge Bases , 2019, Proc. VLDB Endow..
[27] Zhiyuan Liu,et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion , 2015, AAAI.
[28] Li Guo,et al. Knowledge Base Completion Using Embeddings and Rules , 2015, IJCAI.
[29] Qing Liu,et al. SWARM: An Approach for Mining Semantic Association Rules from Semantic Web Data , 2016, PRICAI.
[30] Vít Novácek,et al. Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms , 2017, ECML/PKDD.
[31] Wei Zhang,et al. Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding , 2018, EMNLP.
[32] Wen Zhang,et al. Knowledge Graph Embedding with Diversity of Structures , 2017, WWW.
[33] Li Guo,et al. Improving Knowledge Graph Embedding Using Simple Constraints , 2018, ACL.
[34] Huanbo Luan,et al. Image-embodied Knowledge Representation Learning , 2016, IJCAI.
[35] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[36] Fabian M. Suchanek,et al. Fast rule mining in ontological knowledge bases with AMIE+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{docu , 2015, The VLDB Journal.
[37] Zhiyuan Liu,et al. Representation Learning of Knowledge Graphs with Hierarchical Types , 2016, IJCAI.
[38] Pasquale Minervini,et al. Convolutional 2D Knowledge Graph Embeddings , 2017, AAAI.
[39] Li Guo,et al. Knowledge Graph Embedding with Iterative Guidance from Soft Rules , 2017, AAAI.
[40] Andrew McCallum,et al. Compositional Vector Space Models for Knowledge Base Completion , 2015, ACL.
[41] Gerhard Weikum,et al. WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .
[42] Zhiyuan Liu,et al. Representation Learning of Knowledge Graphs with Entity Descriptions , 2016, AAAI.
[43] Tom M. Mitchell,et al. Random Walk Inference and Learning in A Large Scale Knowledge Base , 2011, EMNLP.
[44] Zhen Wang,et al. Knowledge Graph Embedding by Translating on Hyperplanes , 2014, AAAI.
[45] Danqi Chen,et al. Reasoning With Neural Tensor Networks for Knowledge Base Completion , 2013, NIPS.
[46] Guillaume Bouchard,et al. Complex Embeddings for Simple Link Prediction , 2016, ICML.
[47] Huanbo Luan,et al. Modeling Relation Paths for Representation Learning of Knowledge Bases , 2015, EMNLP.
[48] Petr Hájek,et al. Metamathematics of Fuzzy Logic , 1998, Trends in Logic.
[49] Danqi Chen,et al. Observed versus latent features for knowledge base and text inference , 2015, CVSC.
[50] Juan-Zi Li,et al. RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles , 2015, ArXiv.