Neural Methods for Logical Reasoning Over Knowledge Graphs

• URL: http://arxiv.org/abs/2209.14464v1
• Date: Wed, 28 Sep 2022 23:10:09 GMT
• Title: Neural Methods for Logical Reasoning Over Knowledge Graphs
• Authors: Alfonso Amayuelas, Shuai Zhang, Susie Xi Rao, Ce Zhang
• Abstract summary: We focus on answering multi-hop logical queries on Knowledge Graphs (KGs) Most previous works have been unable to create models that accept full First-Order Logical (FOL) queries. We introduce a set of models that use Neural Networks to create one-point vector embeddings to answer the queries.
• Score: 14.941769519278745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reasoning is a fundamental problem for computers and deeply studied in Artificial Intelligence. In this paper, we specifically focus on answering multi-hop logical queries on Knowledge Graphs (KGs). This is a complicated task because, in real-world scenarios, the graphs tend to be large and incomplete. Most previous works have been unable to create models that accept full First-Order Logical (FOL) queries, which include negative queries, and have only been able to process a limited set of query structures. Additionally, most methods present logic operators that can only perform the logical operation they are made for. We introduce a set of models that use Neural Networks to create one-point vector embeddings to answer the queries. The versatility of neural networks allows the framework to handle FOL queries with Conjunction ($\wedge$), Disjunction ($\vee$) and Negation ($\neg$) operators. We demonstrate experimentally the performance of our model through extensive experimentation on well-known benchmarking datasets. Besides having more versatile operators, the models achieve a 10\% relative increase over the best performing state of the art and more than 30\% over the original method based on single-point vector embeddings.

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