Knowledge graphs(KGs)express relationships between entity pairs,and many real-life problems can be formulated as knowledge graph reasoning(KGR).Conventional approaches to KGR have achieved promising performance but st...Knowledge graphs(KGs)express relationships between entity pairs,and many real-life problems can be formulated as knowledge graph reasoning(KGR).Conventional approaches to KGR have achieved promising performance but still have some drawbacks.On the one hand,most KGR methods focus only on one phase of the KG lifecycle,such as KG completion or refinement,while ignoring reasoning over other stages,such as KG extraction.On the other hand,traditional KGR methods,broadly categorized as symbolic and neural,are unable to balance both scalability and interpretability.To resolve these two problems,we take a more comprehensive perspective of KGR with regard to the whole KG lifecycle,including KG extraction,completion,and refinement,which correspond to three subtasks:knowledge extraction,relational reasoning,and inconsistency checking.In addition,we propose the implementation of KGR using a novel neural symbolic framework,with regard to both scalability and interpretability.Experimental results demonstrate that our proposed methods outperform traditional neural symbolic models.展开更多
A quantum circuit is a computational unit that transforms an input quantum state to an output state.A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by it.However,when...A quantum circuit is a computational unit that transforms an input quantum state to an output state.A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by it.However,when the number of qubits increases,the matrix dimension grows exponentially and the computation becomes intractable.In this paper,we propose a symbolic approach to reasoning about quantum circuits.It is based on a small set of laws involving some basic manipulations on vectors and matrices.This symbolic reasoning scales better than the explicit one and is well suited to be automated in Coq,as demonstrated with some typical examples.展开更多
基金funded by National Natural Science Foundation of China(Grant no.91846204 and U19B2027)National Key Research and Development Program of China(Grant no.2018YFB1402800).
文摘Knowledge graphs(KGs)express relationships between entity pairs,and many real-life problems can be formulated as knowledge graph reasoning(KGR).Conventional approaches to KGR have achieved promising performance but still have some drawbacks.On the one hand,most KGR methods focus only on one phase of the KG lifecycle,such as KG completion or refinement,while ignoring reasoning over other stages,such as KG extraction.On the other hand,traditional KGR methods,broadly categorized as symbolic and neural,are unable to balance both scalability and interpretability.To resolve these two problems,we take a more comprehensive perspective of KGR with regard to the whole KG lifecycle,including KG extraction,completion,and refinement,which correspond to three subtasks:knowledge extraction,relational reasoning,and inconsistency checking.In addition,we propose the implementation of KGR using a novel neural symbolic framework,with regard to both scalability and interpretability.Experimental results demonstrate that our proposed methods outperform traditional neural symbolic models.
基金supported by the National Natural Science Foundation of China under Grant Nos.61832015 and 62072176the Research Funds of Happiness Flower East China Normal University under Grant No.2020ECNU-XFZH005+1 种基金the Inria-CAS Joint Project Quasar.Yuan Feng was partially supported by the National Key Research and Development Program of China under Grant No.2018YFA0306704the Australian Research Council under Grant No.DP180100691.
文摘A quantum circuit is a computational unit that transforms an input quantum state to an output state.A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by it.However,when the number of qubits increases,the matrix dimension grows exponentially and the computation becomes intractable.In this paper,we propose a symbolic approach to reasoning about quantum circuits.It is based on a small set of laws involving some basic manipulations on vectors and matrices.This symbolic reasoning scales better than the explicit one and is well suited to be automated in Coq,as demonstrated with some typical examples.
