# Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering

Source: [Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering](<https://www.sota2.com/research/paper/68fa63031e209131ce9fc581>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:31:19.733Z

Authors: Liwen Zhang, John Winn, Ryota Tomioka

Published: 2016-11-07T20:57:24.000Z

## Abstract

We propose the Gaussian attention model for content-based neural memory access. With the proposed attention model, a neural network has the additional degree of freedom to control the focus of its attention from a laser sharp attention to a broad attention. It is applicable whenever we can assume that the distance in the latent space reflects some notion of semantics. We use the proposed attention model as a scoring function for the embedding of a knowledge base into a continuous vector space and then train a model that performs question answering about the entities in the knowledge base. The proposed attention model can handle both the propagation of uncertainty when following a series of relations and also the conjunction of conditions in a natural way. On a dataset of soccer players who participated in the FIFA World Cup 2014, we demonstrate that our model can handle both path queries and conjunctive queries well.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1611.02266>)

[PDF](<https://arxiv.org/pdf/1611.02266.pdf>)