Quantum Embedding of Knowledge for Reasoning
Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam, L Venkata Subramaniam
Abstract
Statistical Relational Learning (SRL) methods are the most widely used techniques to generate distributional representations of the symbolic Knowledge Bases (KBs). These methods embed any given KB into a vector space by exploiting statistical similarities among its entities and predicates but without any guarantee of preserving the underlying logical structure of the KB. This, in turn, results in poor performance of logical reasoning tasks that are solved using such distributional representations. We present a novel approach called Embed2Reason (E2R) that embeds a symbolic KB into a vector space in a logical structure preserving manner. This approach is inspired by the theory of Quantum Logic. Such an embedding allows answering membership based complex logical reasoning queries with impressive accuracy improvements over popular SRL baselines.
BibTeX
@inproceedings{NEURIPS2019_cb12d7f9,
author = {Garg, Dinesh and Ikbal, Shajith and Srivastava, Santosh K. and Vishwakarma, Harit and Karanam, Hima and Subramaniam, L Venkata},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Quantum Embedding of Knowledge for Reasoning},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/cb12d7f933e7d102c52231bf62b8a678-Paper.pdf},
volume = {32},
year = {2019}
}