Interpretable reasoning question-answering method and device
A technology of questions and answers, applied in the field of interpretable reasoning question answering methods and devices, which can solve problems that affect the answer accuracy of the question answering system, insufficient completeness of the knowledge map, and reduce user experience, etc.
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Embodiment 1
[0046] refer to figure 1 , this embodiment provides an interpretable reasoning question answering method, including:
[0047] According to the question to be answered, construct a knowledge map of the field related to the question;
[0048] Converting the knowledge map into a graph structure, and predicting the semantic relationship between entities in the graph structure;
[0049] Obtaining logical rules between entities based on the semantic relationship;
[0050] Complementing the knowledge graph based on the logic rules;
[0051] According to the question to be answered, based on the completed knowledge graph, the best answer is deduced in the knowledge graph, and the answer to the question is obtained.
[0052] Aiming at the incompleteness of the existing knowledge graph, this disclosure studies the potential semantic and structural features between knowledge entities in the knowledge graph, and then mines the logical rules between entities, and uses the logical rules ...
Embodiment 2
[0095] refer to figure 2 , this embodiment provides an interpretable reasoning question answering device, including:
[0096] The knowledge map building module is used to construct the knowledge map of the field related to the question according to the question to be answered; the knowledge map building module includes knowledge extraction, knowledge representation and knowledge fusion. Knowledge extraction is further divided into: unstructured data cleaning, named entity recognition, and relationship extraction. When building Chinese knowledge graphs, this system uses a crawler framework for data processing, and an end-to-end model based on sequences and tree structures for knowledge extraction. After knowledge fusion, a visual graph database is used to store knowledge triples.
[0097] A query reasoning module, configured to mine logical rules between entities in the knowledge graph, and use the logical rules to complete the knowledge graph;
[0098]The natural language q...
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