NLPIR SEMINAR Y2019#6
In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.
This week’s seminar is organized as follows:
- The seminar time is 1.pm, Mon, at Zhongguancun Technology Park ,Building 5, 1306.
- The lecturer is Qinghong Jiang, the paper’s title is Variational Knowledge Graph Reasoning.
- The seminar will be hosted by Li Shen.
- Attachment is the paper of this seminar, please download in advance.
Everyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper.
Variational Knowledge Graph Reasoning
Wenhu Chen, Wenhan
Xiong, Xifeng Yan, William Yang Wang
Inferring missing links in knowledge
graphs (KG) has attracted a lot of attention from the research community. In
this paper, we tackle a practical query answering task involving predicting the
relation of a given entity pair. We frame this prediction problem as an
inference problem in a probabilistic graphical model and aim at resolving it
from a variational inference perspective. In order to model the relation
between the query entity pair, we assume that there exists an underlying latent
variable (paths connecting two nodes) in the KG, which carries the equivalent
semantics of their relations.
However, due to the intractability of
connections in large KGs, we propose to use variation inference to maximize the
evidence lower bound. More specifically, our framework (DIVA) is composed of
three modules, i.e. a posterior approximator, a prior (path finder), and a
likelihood (path reasoner). By using variational inference, we are able to
incorporate them closely into a unified architecture and jointly optimize them
to perform KG reasoning. With active interactions among these sub-modules, DIVA
is better at handling noise and coping with more complex reasoning scenarios.
In order to evaluate our method, we conduct the experiment of the link
prediction task on multiple datasets and achieve state-of-the-art performances
on both datasets.