An Inference Approach To Question Answering Over Knowledge Graphs

TL;DR

This paper introduces an inference-based approach for question answering over knowledge graphs, converting natural language queries into inference problems, which reduces domain-specific training needs and achieves high accuracy.

Contribution

The paper proposes a novel inference framework and Hierarchical Recurrent Path Encoder for knowledge graph question answering, reducing domain-specific training requirements.

Findings

Achieves over 90% accuracy on MetaQA dataset

Outperforms existing state-of-the-art methods

Requires less domain-specific training data

Abstract

Knowledge Graphs (KG) act as a great tool for holding distilled information from large natural language text corpora. The problem of natural language querying over knowledge graphs is essential for the human consumption of this information. This problem is typically addressed by converting the natural language query to a structured query and then firing the structured query on the KG. Direct answering models over knowledge graphs in literature are very few. The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph. In this work, we convert the problem of natural language querying over knowledge graphs to an inference problem over premise-hypothesis pairs. Using trained deep learning models for the converted proxy inferencing problem, we provide the solution for the original natural language querying problem. Our method achieves over 90% accuracy on MetaQA dataset, beating the existing state-of-the-art. We also propose a model for inferencing called Hierarchical Recurrent Path Encoder(HRPE). The inferencing models can be fine-tuned to be used across domains with less training data. Our approach does not require large domain-specific training data for querying on new knowledge graphs from different domains.