Conversational knowledge base question and answer implementation method

An implementation method and knowledge base technology, applied in the field of natural language processing, can solve problems such as loose combination of knowledge base and semantic analysis, errors, difficulty in using matching information, etc., and achieve the effect of improving user experience satisfaction and accuracy

Pending Publication Date: 2022-05-06
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

[0005] In addition, the problem of the loose combination of knowledge base and semantic parsing has always existed in existing methods
This will make it difficult to use the existing entity type and relationship information in the knowledge base when predicting the enti

Method used

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  • Conversational knowledge base question and answer implementation method
  • Conversational knowledge base question and answer implementation method
  • Conversational knowledge base question and answer implementation method

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Embodiment Construction

[0031] Before semantic analysis, various models to be used need to be trained and tested. Including: obtaining the training set and test set of questions, and the training set and test set of triples (in the knowledge base), and training and testing the model used in entity linking. Specifically, on the one hand, the parameters in the encoder-decoder model, LSTM (long short-term memory network), GAT (graph attention network) model and feed-forward neural network used in extracting question type features Task training and testing. On the other hand, the XLNet model and linear layer network for entity disambiguation are trained and tested. The input is a text composed of the current question, ambiguous entity name, and related triplet information, and the output is that the text is a positive example. and the probability of negative cases.

[0032] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings an...

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Abstract

The invention discloses a dialogue knowledge base question and answer implementation method. The method comprises the steps that 1, a fuzzy reasoning grammar system is defined in advance; 2, obtaining a current question to be answered and historical dialogue information, and obtaining vector representation through a GloVe model; step 3, obtaining a hidden state vector of the encoder; step 4, identifying the named entity and the type thereof from the feature representation; step 5, obtaining an entity ID in the knowledge base; step 6, obtaining a logic form of the problem under the current grammar framework; 7, predicting a relation ID and an entity type ID in the problem; 8, obtaining a query statement which can be executed on the knowledge base; and 9, executing the query statement in the knowledge base to obtain an answer corresponding to the current question. Compared with the prior art, the method has the advantages that the intention of the user question in the dialogue is automatically identified, the response accuracy in the dialogue is improved, the method is suitable for open domain knowledge graph question answering, and the user experience satisfaction can be improved.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a question answering method for a dialogic knowledge base. Background technique [0002] In recent years, with the development of knowledge bases such as DBPedia, Wikidata, and YAGO, their scale has become larger and larger. This makes it more and more difficult for ordinary people without professional background to obtain the knowledge they need. Knowledge Base Question Answering (KBQA) provides an effective and convenient way, which aims to use the knowledge in the knowledge base to answer the questions posed by users in natural language. Despite a large amount of research, KBQA is still a challenging task, especially in multi-turn dialogue KBQA, and it is difficult for existing methods to understand the questions in the dialogue as accurately as humans. [0003] With the development of deep learning technology, mainstream methods for KBQA usin...

Claims

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Application Information

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IPC IPC(8): G06F16/33G06F16/332G06F40/295G06F40/30G06N3/04G06N3/08
CPCG06F16/3329G06F16/3344G06F40/30G06F40/295G06N3/08G06N3/044
Inventor 熊德意李俊卓
Owner TIANJIN UNIV
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