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Quantum Embedding of Knowledge for Reasoning
Dinesh Garg · Shajith Ikbal Mohamed · Santosh Kumar Srivastava · Harit Vishwakarma · Hima Karanam · L Venkata Subramaniam

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #186

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.

Author Information

Dinesh Garg (IBM Research AI, India)
Shajith Ikbal Mohamed (IBM Research AI, India)
Santosh Kumar Srivastava (IBM Research AI)
Harit Vishwakarma (University of Wisconsin Madison)
Hima Karanam (IBM Research AI)
L Venkata Subramaniam (IBM Research AI - India)

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