The embodiment of the invention provides a community gas system dynamic risk assessment method and device based on a graph neural network. The method comprises the following steps: constructing a community gas system knowledge graph based on a community gas system risk assessment index system; inputting the community gas system knowledge graph containing the entities, the entity characteristics and the relationship between the entities into a community gas system dynamic risk assessment model so as to obtain the risk level of the target entity and carry out reason reasoning and accident consequence dynamic prediction on the community gas accident, wherein the community gas system dynamic risk assessment model comprises a first entity-level attention layer, a first semantic-level attentionlayer, a second entity-level attention layer, a second semantic-level attention layer, an entity classification layer and a relationship prediction layer. According to the embodiment of the invention,by obtaining the risk level of the target entity and carrying out reason reasoning and accident result dynamic prediction on the community gas accident, dynamic risk assessment of the community gas system is realized, and the accuracy of risk assessment is improved.

Community gas system dynamic risk assessment method and device based on graph neural network

The invention provides a dynamic reasoning network and method for multi-hop questions and answers, and the network comprises: a paragraph selector which receives paragraphs and questions, and selectssub-paragraphs related to answers of the questions from the paragraphs; the encoding module that is used for enhancing interaction between the problem and the sub-paragraphs by using collaborative attention, and calculating to obtain final vector representation of the sub-paragraphs and vector representation of the problem; the entity graph construction module that is used for constructing an entity graph; the dynamic reasoning module that is used for reasoning the entity graph, repeatedly reading texts to simulate the process of analyzing information by people and constructing a problem remodeling mechanism so as to repeatedly read problems and related important parts; and the answer prediction module that is connected with the encoding module, is connected with the dynamic reasoning module and is used for receiving the final vector representation of the sub-paragraphs and outputting to obtain four types of prediction. The network establishes a question remodeling mechanism, and the mechanism can repeatedly read questions to simulate the reading habits of people so as to improve the understanding and reasoning ability of the multi-hop reasoning question and answer model.

Dynamic reasoning network and method for multi-hop questions and answers

The embodiment of the invention provides a food safety event risk research and judgment method and device based on knowledge graph reasoning. The method comprises the following steps: inputting a foodsafety knowledge graph containing entities, relationships among the entities and entity characteristics into a food safety event risk research and judgment model, obtaining a risk level of a target entity, and carrying out food safety event reason reasoning and event consequence prediction, wherein the food safety event risk research and judgment model comprises a triad internal attention mechanism layer, a triad external attention mechanism layer, an entity classification layer and a relationship prediction layer. The embodiment of the invention provides a food safety event risk research andjudgment method and device based on knowledge graph reasoning, a food safety knowledge graph is input into a food safety event risk research and judgment model, the risk level of the target entity isobtained, food safety event reason reasoning and event consequence dynamic prediction are carried out, risk dynamic research and judgment and dynamic prediction of important activity food safety events are achieved, and the accuracy of risk research and judgment and dynamic prediction is improved.

Food safety event risk research and judgment method and device based on knowledge graph reasoning

The embodiment of the invention provides a patent blank prediction method and system, and the method comprises the steps: obtaining historical patents in a preset time period, and generating a historical text file; extracting entities in the historical text file and relationships among the entities; constructing a patent knowledge graph by taking the entities as nodes and taking the relationshipsas edges; predicting a potential edge existing between two nodes of which the edges are not connected in the patent knowledge graph to construct a new patent knowledge graph; and obtaining a target prediction patent according to the new patent knowledge graph. According to the patent blank prediction method and system provided by the embodiment of the invention, the patent knowledge graph is constructed by utilizing the entities and relationships in the historical patent document data, and the potential triples in the patent knowledge graph are predicted through the graph neural network algorithm, so that the prediction of blank patents is realized, and a convenient way is provided for defining the scientific and technological research direction and the declaration direction of new patents.

Patent blank prediction method and system

The invention relates to a method and a device for detecting authenticity of triples in a knowledge graph. The method comprises the following steps of: forming a substrate; extracting a first vector for describing triad structure information in the knowledge graph and a second vector for describing triad description information in the knowledge graph from the text; fusing the first vector and thesecond vector; obtaining fusion vectors, according to a preset network structure of a capsule network, constructing a scoring function used for defining a triple score, scoring function, constructinga loss function for training a capsule network; constructing a sample set according to the fusion vector, training the capsule network model according to the sample set and the loss function to obtaina trained capsule network, inputting the fusion vector corresponding to the a to-be-predicted triad into the trained capsule network, and determining whether the relationship of the triad is accurateor not according to the output value of the scoring function. By adopting the method, tThe accuracy of triple authenticity detection can be improved.

Method and device for detecting authenticity of triples in knowledge graph

The invention relates to a knowledge graph reasoning method and device for concerning neighbor entities. The knowledge graph reasoning method comprises the following steps: obtaining an initial embedded representation of an entity and a relationship in the knowledge graph; obtaining a to-be-inferred problem according to the to-be-inferred problem in the knowledge graph; constructing neighbor subgraphs, aiming at each neighbor node in the neighbor subgraph; and setting the attention probability, obtaining feature representation of each pair of triples in the neighbor subgraph, according to theattention probability and the feature represention, obtaining a head entity embedding representation fused with neighbor node information, splicing the head entity embedding representation and an initial embedding representation corresponding to a head entity to obtain an embedding representation of a final head entity, carrying out evaluation sorting on each formed triad according to the embedding representation, and carrying out reasoning according to an evaluation sorting result to obtain a tail entity in the triads. By adopting the knowledge graph reasoning method, invisible nodes can be generalized, and the knowledge graph completion efficiency is improved.

Knowledge graph reasoning method and device for concerning neighbor entities

The invention relates to a multi-hop entity relationship prediction method and device in a knowledge graph. The method comprises the following steps of: obtaining entity pairs from the training set; constructing a multi-hop reasoning relation chain according to the entity pairs; encoding the relationship sequence by using a preset first gating cycle unit to obtain an implicit state sequence corresponding to the relationship sequence; encoding the hidden state sequence by using a preset second gating cycle unit to obtain a prediction entity sequence, determining a first loss function accordingto the prediction entity sequence, training a preset neural network model according to the first loss function and the entity pairs in the training set to obtain an entity relationship prediction model, and inputting the entity pairs in the knowledge graph into the entity relationship prediction model to obtain a relationship prediction result of the entity pairs. By adopting the method, the relationship of the multi-hop entity pairs in the knowledge graph can be accurately predicted.

Multi-hop entity relationship prediction method and device in knowledge graph

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Knowledge graph reasoning method and device for concerning neighbor entities

Multi-hop entity relationship prediction method and device in knowledge graph