79,844 research outputs found
Modeling Relation Paths for Representation Learning of Knowledge Bases
Representation learning of knowledge bases (KBs) aims to embed both entities
and relations into a low-dimensional space. Most existing methods only consider
direct relations in representation learning. We argue that multiple-step
relation paths also contain rich inference patterns between entities, and
propose a path-based representation learning model. This model considers
relation paths as translations between entities for representation learning,
and addresses two key challenges: (1) Since not all relation paths are
reliable, we design a path-constraint resource allocation algorithm to measure
the reliability of relation paths. (2) We represent relation paths via semantic
composition of relation embeddings. Experimental results on real-world datasets
show that, as compared with baselines, our model achieves significant and
consistent improvements on knowledge base completion and relation extraction
from text.Comment: 10 page
A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion
Embedding-based Knowledge Base Completion models have so far mostly combined
distributed representations of individual entities or relations to compute
truth scores of missing links. Facts can however also be represented using
pairwise embeddings, i.e. embeddings for pairs of entities and relations. In
this paper we explore such bigram embeddings with a flexible Factorization
Machine model and several ablations from it. We investigate the relevance of
various bigram types on the fb15k237 dataset and find relative improvements
compared to a compositional model.Comment: accepted for AKBC 2016 workshop, 6page
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