Representing Text for Joint Embedding of Text and Knowledge Bases

Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al., 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.

[1]  Ramesh Nallapati,et al.  Multi-instance Multi-label Learning for Relation Extraction , 2012, EMNLP.

[2]  Kai-Wei Chang,et al.  Typed Tensor Decomposition of Knowledge Bases for Relation Extraction , 2014, EMNLP.

[3]  Wei Zhang,et al.  Knowledge vault: a web-scale approach to probabilistic knowledge fusion , 2014, KDD.

[4]  Ni Lao,et al.  Reading The Web with Learned Syntactic-Semantic Inference Rules , 2012, EMNLP.

[5]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[6]  Andrew McCallum,et al.  Compositional Vector Space Models for Knowledge Base Completion , 2015, ACL.

[7]  Jianfeng Gao,et al.  Embedding Entities and Relations for Learning and Inference in Knowledge Bases , 2014, ICLR.

[8]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[9]  Tom M. Mitchell,et al.  Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases , 2014, EMNLP.

[10]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

[11]  Andrew McCallum,et al.  Relation Extraction with Matrix Factorization and Universal Schemas , 2013, NAACL.

[12]  Yoshua Bengio,et al.  Word Representations: A Simple and General Method for Semi-Supervised Learning , 2010, ACL.

[13]  Andrew McCallum,et al.  Modeling Relations and Their Mentions without Labeled Text , 2010, ECML/PKDD.

[14]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[15]  Evgeniy Gabrilovich,et al.  A Review of Relational Machine Learning for Knowledge Graphs , 2015, Proceedings of the IEEE.

[16]  Jun Zhao,et al.  Relation Classification via Convolutional Deep Neural Network , 2014, COLING.

[17]  Jason Weston,et al.  Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction , 2013, EMNLP.

[18]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[19]  Tom M. Mitchell,et al.  Random Walk Inference and Learning in A Large Scale Knowledge Base , 2011, EMNLP.

[20]  Zhen Wang,et al.  Knowledge Graph Embedding by Translating on Hyperplanes , 2014, AAAI.

[21]  Christopher D. Manning,et al.  Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks , 2015, ACL.

[22]  Oren Etzioni,et al.  Modeling Missing Data in Distant Supervision for Information Extraction , 2013, TACL.

[23]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.

[24]  Danqi Chen,et al.  Observed versus latent features for knowledge base and text inference , 2015, CVSC.

[25]  Mark Dredze,et al.  Improved Relation Extraction with Feature-Rich Compositional Embedding Models , 2015, EMNLP.

[26]  Zhen Wang,et al.  Knowledge Graph and Text Jointly Embedding , 2014, EMNLP.

[27]  Luke S. Zettlemoyer,et al.  Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations , 2011, ACL.

[28]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[29]  Hans-Peter Kriegel,et al.  A Three-Way Model for Collective Learning on Multi-Relational Data , 2011, ICML.

[30]  Daniel Jurafsky,et al.  Distant supervision for relation extraction without labeled data , 2009, ACL.

[31]  Tom M. Mitchell,et al.  Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues , 2013, EMNLP.