How graph structure help symbolic logic for reasoning,?
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Graph structures play a crucial role in enhancing symbolic logic for reasoning by providing a framework to encode relational information and facilitate complex reasoning processes. These structures enable the representation of logical relations between entities, aiding in multi-hop reasoning and query execution over knowledge graphs. Neural-symbolic models, such as Hybrid Graph Network (HGN) and Graph Neural Network Query Executor (GNN-QE), leverage graph structures to generate feature representations, filter irrelevant information, and execute logical operations, thereby improving interpretability and performance in tasks like commonsense reasoning and answering complex first-order logic queries. By incorporating graph neural networks and adaptive logic graph networks, these models achieve a synergy between neural and symbolic reasoning, leading to more effective and interpretable reasoning processes.
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16 Mar 2022 | The graph structure in AdaLoGN enables adaptive logical reasoning by passing messages over logical relations between text units, facilitating mutual reinforcement between neural and symbolic reasoning for improved comprehension. |
16 May 2022 | Graph structure aids symbolic logic in reasoning by decomposing queries into relation projections and logical operations over fuzzy sets, enhancing interpretability and reasoning on incomplete knowledge graphs. |
04 May 2021 | Graph structures aid symbolic logic for reasoning by encoding relational information from knowledge graphs, enhancing commonsense reasoning tasks through contextualized knowledge representations and filtering unhelpful edges. |
16 May 2022 23 Citations | Graph Neural Network Query Executor (GNN-QE) combines symbolic logic with graph structure to decompose queries into relation projections and logical operations, enhancing interpretability and reasoning in knowledge graphs. |
16 Mar 2022 10 Citations | The graph structure in AdaLoGN enables adaptive logical relations inference, facilitating neural-symbolic reasoning by passing messages over the graph to enhance logical reasoning in machine reading comprehension. |
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