NLPIR SEMINAR Y2019#6
INTRO
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.
Arrangement
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
Abstract
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.
