Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base
Completion
- URL: http://arxiv.org/abs/2109.09566v1
- Date: Thu, 16 Sep 2021 17:54:56 GMT
- Title: Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base
Completion
- Authors: Prithviraj Sen, Breno W. S. R. Carvalho, Ibrahim Abdelaziz, Pavan
Kapanipathi, Francois Luus, Salim Roukos, Alexander Gray
- Abstract summary: We show that not all rule-based Knowledge Base Completion models are the same.
We propose two distinct approaches that learn in one case: 1) a mixture of relations and the other 2) a mixture of paths.
When implemented on top of neuro-symbolic AI, which learns rules by extending Boolean logic to real-valued logic, the latter model leads to superior KBC accuracy outperforming state-of-the-art rule-based KBC by 2-10% in terms of mean reciprocal rank.
- Score: 59.093293389123424
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent interest in Knowledge Base Completion (KBC) has led to a plethora of
approaches based on reinforcement learning, inductive logic programming and
graph embeddings. In particular, rule-based KBC has led to interpretable rules
while being comparable in performance with graph embeddings. Even within
rule-based KBC, there exist different approaches that lead to rules of varying
quality and previous work has not always been precise in highlighting these
differences. Another issue that plagues most rule-based KBC is the
non-uniformity of relation paths: some relation sequences occur in very few
paths while others appear very frequently. In this paper, we show that not all
rule-based KBC models are the same and propose two distinct approaches that
learn in one case: 1) a mixture of relations and the other 2) a mixture of
paths. When implemented on top of neuro-symbolic AI, which learns rules by
extending Boolean logic to real-valued logic, the latter model leads to
superior KBC accuracy outperforming state-of-the-art rule-based KBC by 2-10% in
terms of mean reciprocal rank. Furthermore, to address the non-uniformity of
relation paths, we combine rule-based KBC with graph embeddings thus improving
our results even further and achieving the best of both worlds.
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