# Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks

Source: [Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks](<https://www.sota2.com/research/paper/68fa64671e209131cea046d8>)

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

Data updated: 2025-10-25T21:03:11.991Z

Authors: Salman Mohammed, Peng Shi, Jimmy Lin

Published: 2017-12-05T23:38:00.000Z

## Abstract

We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact. Adopting a straightforward decomposition of the problem into entity detection, entity linking, relation prediction, and evidence combination, we explore simple yet strong baselines. On the popular SimpleQuestions dataset, we find that basic LSTMs and GRUs plus a few heuristics yield accuracies that approach the state of the art, and techniques that do not use neural networks also perform reasonably well. These results show that gains from sophisticated deep learning techniques proposed in the literature are quite modest and that some previous models exhibit unnecessary complexity.

## 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/1712.01969>)

[PDF](<https://arxiv.org/pdf/1712.01969.pdf>)