47 research outputs found
How to Train Your Agent to Read and Write
Reading and writing research papers is one of the most privileged abilities
that a qualified researcher should master. However, it is difficult for new
researchers (\eg{students}) to fully {grasp} this ability. It would be
fascinating if we could train an intelligent agent to help people read and
summarize papers, and perhaps even discover and exploit the potential knowledge
clues to write novel papers. Although there have been existing works focusing
on summarizing (\emph{i.e.}, reading) the knowledge in a given text or
generating (\emph{i.e.}, writing) a text based on the given knowledge, the
ability of simultaneously reading and writing is still under development.
Typically, this requires an agent to fully understand the knowledge from the
given text materials and generate correct and fluent novel paragraphs, which is
very challenging in practice. In this paper, we propose a Deep ReAder-Writer
(DRAW) network, which consists of a \textit{Reader} that can extract knowledge
graphs (KGs) from input paragraphs and discover potential knowledge, a
graph-to-text \textit{Writer} that generates a novel paragraph, and a
\textit{Reviewer} that reviews the generated paragraph from three different
aspects. Extensive experiments show that our DRAW network outperforms
considered baselines and several state-of-the-art methods on AGENDA and
M-AGENDA datasets. Our code and supplementary are released at
https://github.com/menggehe/DRAW
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
Candidate gene prioritization using graph embedding
International audienceCandidate genes prioritization allows to rank among a large number of genes, those that are strongly associated with a phenotype or a disease. Due to the important amount of data that needs to be integrate and analyse, gene-to-phenotype association is still a challenging task. In this paper, we evaluated a knowledge graph approach combined with embedding methods to overcome these challenges. We first introduced a dataset of rice genes created from several open-access databases. Then, we used the Translating Embedding model and Convolution Knowledge Base model, to vectorize gene information. Finally, we evaluated the results using link prediction performance and vectors representation using some unsupervised learning techniques