Neighborhood Mixture Model for Knowledge Base Completion

Abstract

Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mixture of its neighborhood in the knowledge base and apply this technique on TransE-a well-known embedding model for knowledge base completion. Experimental results show that the neighborhood information significantly helps to improve the results of the TransE model, leading to better performance than obtained by other state-of-the-art embedding models on three benchmark datasets for triple classification, entity prediction and relation prediction tasks.

V1: In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016. V2: Corrected citation to (Krompaß et al., 2015). V3: A revised version of our CoNLL 2016 paper to update latest related work

References in corpus (8)

• ADADELTA: An Adaptive Learning Rate Method
• A Review of Relational Machine Learning for Knowledge Graphs
• Complex Embeddings for Simple Link Prediction
• ProjE: Embedding Projection for Knowledge Graph Completion
• Traversing Knowledge Graphs in Vector Space
• Compositional Vector Space Models for Knowledge Base Completion
• Type-Constrained Representation Learning in Knowledge Graphs
• A Semantic Matching Energy Function for Learning with Multi-relational Data

Cited by in corpus (9)

• A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
• Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach
• Search Personalization with Embeddings
• Natural Language Processing for Information Extraction
• An Interpretable Knowledge Transfer Model for Knowledge Base Completion
• Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach
• Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
• SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining
• Feature Learning for Meta-Paths in Knowledge Graphs