• Corpus ID: 2768038

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

@article{Yang2014EmbeddingEA,
  title={Embedding Entities and Relations for Learning and Inference in Knowledge Bases},
  author={Bishan Yang and Wen-tau Yih and Xiaodong He and Jianfeng Gao and Li Deng},
  journal={CoRR},
  year={2014},
  volume={abs/1412.6575}
}
Abstract: We consider learning representations of entities and relations in KBs using the neural-embedding approach. [] Key Method Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2% vs. 54.7% by TransE on Freebase).

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References

SHOWING 1-10 OF 39 REFERENCES

Translating Embeddings for Modeling Multi-relational Data

TransE is proposed, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities, which proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases.

Learning Structured Embeddings of Knowledge Bases

A learning process based on an innovative neural network architecture designed to embed any of these symbolic representations into a more flexible continuous vector space in which the original knowledge is kept and enhanced would allow data from any KB to be easily used in recent machine learning meth- ods for prediction and information retrieval.

Reasoning With Neural Tensor Networks for Knowledge Base Completion

An expressive neural tensor network suitable for reasoning over relationships between two entities given a subset of the knowledge base is introduced and performance can be improved when entities are represented as an average of their constituting word vectors.

A latent factor model for highly multi-relational data

This paper proposes a method for modeling large multi-relational datasets, with possibly thousands of relations, based on a bilinear structure, which captures various orders of interaction of the data and also shares sparse latent factors across different relations.

A semantic matching energy function for learning with multi-relational data

A new neural network architecture designed to embed multi-relational graphs into a flexible continuous vector space in which the original data is kept and enhanced, demonstrating that it can scale up to tens of thousands of nodes and thousands of types of relation.

Typed Tensor Decomposition of Knowledge Bases for Relation Extraction

A tensor decomposition approach for knowledge base embedding that is highly scalable, and is especially suitable for relation extraction by leveraging relational domain knowledge about entity type information, which is significantly faster than previous approaches and better able to discover new relations missing from the database.

A Three-Way Model for Collective Learning on Multi-Relational Data

This work presents a novel approach to relational learning based on the factorization of a three-way tensor that is able to perform collective learning via the latent components of the model and provide an efficient algorithm to compute the factorizations.

Factorizing YAGO: scalable machine learning for linked data

This work presents an efficient approach to relational learning on LOD data, based on the factorization of a sparse tensor that scales to data consisting of millions of entities, hundreds of relations and billions of known facts, and shows how ontological knowledge can be incorporated in the factorizations to improve learning results and how computation can be distributed across multiple nodes.

Learning deep structured semantic models for web search using clickthrough data

A series of new latent semantic models with a deep structure that project queries and documents into a common low-dimensional space where the relevance of a document given a query is readily computed as the distance between them are developed.

Knowledge vault: a web-scale approach to probabilistic knowledge fusion

The Knowledge Vault is a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories that computes calibrated probabilities of fact correctness.