4,944 research outputs found
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
Knowledge graph embedding has been an active research topic for knowledge
base completion, with progressive improvement from the initial TransE, TransH,
DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution
over embeddings and multiple layers of nonlinear features to model knowledge
graphs. The model can be efficiently trained and scalable to large knowledge
graphs. However, there is no structure enforcement in the embedding space of
ConvE. The recent graph convolutional network (GCN) provides another way of
learning graph node embedding by successfully utilizing graph connectivity
structure. In this work, we propose a novel end-to-end Structure-Aware
Convolutional Network (SACN) that takes the benefit of GCN and ConvE together.
SACN consists of an encoder of a weighted graph convolutional network (WGCN),
and a decoder of a convolutional network called Conv-TransE. WGCN utilizes
knowledge graph node structure, node attributes and edge relation types. It has
learnable weights that adapt the amount of information from neighbors used in
local aggregation, leading to more accurate embeddings of graph nodes. Node
attributes in the graph are represented as additional nodes in the WGCN. The
decoder Conv-TransE enables the state-of-the-art ConvE to be translational
between entities and relations while keeps the same link prediction performance
as ConvE. We demonstrate the effectiveness of the proposed SACN on standard
FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over
the state-of-the-art ConvE in terms of HITS@1, HITS@3 and [email protected]: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI
2019
Broadcasting Convolutional Network for Visual Relational Reasoning
In this paper, we propose the Broadcasting Convolutional Network (BCN) that
extracts key object features from the global field of an entire input image and
recognizes their relationship with local features. BCN is a simple network
module that collects effective spatial features, embeds location information
and broadcasts them to the entire feature maps. We further introduce the
Multi-Relational Network (multiRN) that improves the existing Relation Network
(RN) by utilizing the BCN module. In pixel-based relation reasoning problems,
with the help of BCN, multiRN extends the concept of `pairwise relations' in
conventional RNs to `multiwise relations' by relating each object with multiple
objects at once. This yields in O(n) complexity for n objects, which is a vast
computational gain from RNs that take O(n^2). Through experiments, multiRN has
achieved a state-of-the-art performance on CLEVR dataset, which proves the
usability of BCN on relation reasoning problems.Comment: Accepted paper at ECCV 2018. 24 page
Holographic Embeddings of Knowledge Graphs
Learning embeddings of entities and relations is an efficient and versatile
method to perform machine learning on relational data such as knowledge graphs.
In this work, we propose holographic embeddings (HolE) to learn compositional
vector space representations of entire knowledge graphs. The proposed method is
related to holographic models of associative memory in that it employs circular
correlation to create compositional representations. By using correlation as
the compositional operator HolE can capture rich interactions but
simultaneously remains efficient to compute, easy to train, and scalable to
very large datasets. In extensive experiments we show that holographic
embeddings are able to outperform state-of-the-art methods for link prediction
in knowledge graphs and relational learning benchmark datasets.Comment: To appear in AAAI-1
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