Intelligent Question Answering System Based on Knowledge Graph Subgraph Retrieval
A knowledge map and intelligent question answering technology, applied in the field of knowledge map, can solve problems such as complex problems and unsatisfactory results, and achieve the effects of improving the degree of relevance, improving the accuracy of recognition, and improving the accuracy of question and answer
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Embodiment 1
[0046] This embodiment provides an intelligent question answering system based on knowledge map subgraph retrieval, including: a question sentence processing module, a knowledge map embedding module, a knowledge map subgraph retrieval module, a knowledge map subgraph filtering module, and an answer output module;
[0047] The question processing module identifies the entities in the user input questions, constructs a syntactic dependency tree of the input questions according to the entities, and obtains relational predicates between entities;
[0048] The knowledge map embedding module converts the entity and the relationship predicate data between entities obtained by the question processing module into a low-dimensional dense vector;
[0049] The knowledge graph embedding module represents entities, relationships, attributes or values of the knowledge graph as low-dimensional dense vectors;
[0050] The knowledge map subgraph retrieval module maps the entities in the quest...
Embodiment 2
[0078] Such as figure 1 The intelligent question answering system based on knowledge map subgraph retrieval provided by the embodiment of the application shown includes:
[0079] Question processing module, knowledge map embedding module, knowledge map subgraph retrieval module, knowledge map subgraph filtering module and answer output module;
[0080] Assume that the length of the input question is n; the question processing module identifies the entity in the user input question, builds the syntactic dependency tree of the input question according to the entity, and obtains the relational predicate between the entities. The specific process includes:
[0081] Use the BERT model to train the user input question, and get the semantic representation vector of the question C =BERT( n ), C ={ C 1 , C 2 ,…, C n };
[0082] Then the semantic representation vector of the question C Input BiLSTM to get the hidden layer matrix;
[0083]
[0084] in and Respectively r...
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