10 results about "Knowledge acquisition method" patented technology

The embodiment of the invention provides an auxiliary knowledge acquisition method and device in program code development and a program product. On the basis of obtaining the development assistance request, analyzing the development assistance request through a pre-trained request analysis model to obtain an MCP command; the method comprises the following steps: receiving an MCP instruction, analyzing the MCP instruction through an MCP service to generate a knowledge base query instruction, and sending the knowledge base query instruction to a corresponding target knowledge query service, so that the target knowledge query service performs knowledge query in a target knowledge base according to the knowledge base query instruction to generate development auxiliary knowledge; middle adaptation is carried out through the MCP service, so that the target knowledge base can generate development assistance knowledge without accessing the target knowledge base by the pre-trained request analysis model, development assistance demand information proposed by a user when program code development is carried out is accurately solved, and the user experience is improved. The problem that auxiliary knowledge replied based on a scheme in the prior art is poor in accuracy is solved.

The invention relates to the technical field of computers, and discloses a knowledge acquisition method and device, a medium, a program product and a system. In the method, a first terminal device generates answer content based on knowledge information related to question information determined by a cloud side device and an end side user portrait. Compared with the knowledge obtained directly based on the question information, the correlation between the answer content and the content in which the user is interested is stronger. The user does not need to perform other operations such as re-acquisition and page turning to acquire interested contents, so that the use experience of the user can be improved.

The application discloses a cyclic field self-adaptive knowledge acquisition method for high-speed rail intelligent operation and maintenance, and is used for field knowledge self-adaptive acquisition, comprising the following steps: constructing a field self-adaptive learning framework; converting entity recognition and relationship extraction processes in the field self-adaptive learning framework into classification processes; inputting training text into the field self-adaptive learning framework for training to obtain an optimal field self-adaptive learning framework; wherein, in order to complete the training process of the optimal field self-adaptive learning framework, a loss function of components in the field self-adaptive learning framework is defined; inputting actual data text into the optimal field self-adaptive learning framework to obtain target knowledge; wherein, the field self-adaptive learning framework process is: encoding of input text, shared decoding, first classification and second classification. In the case that the number of labeled data is insufficient, massive high-speed train fault text data can be automatically subjected to knowledge extraction, and three-tuples with knowledge, such as fault sources, fault categories and fault features, are constructed.

The application discloses a knowledge acquisition method based on neural symbol hybrid retrieval enhancement, comprising the following steps: acquiring an event corpus, a semantic graph, and a symbolized knowledge base related to an event extraction task; mapping each corpus text in the event corpus and an event type in the symbolized knowledge base according to the semantic graph by a weak supervision mode to construct a retrieval knowledge base; when event extraction is performed on a target text, retrieving a plurality of candidate corpus texts related to the target text from the retrieval knowledge base, retrieving an optimal event type for the target text according to a mapping relationship between the candidate corpus texts and the event type, and extracting an event subgraph corresponding to the optimal event type and an optimal corpus text as prompt knowledge; combining the prompt knowledge converted into a prompt text with the target text as an input text, performing knowledge acquisition on the input text by using a language model, and obtaining an event extraction result based on the target text. The method enhances the accuracy and efficiency of knowledge acquisition.

A method for acquiring knowledge based on an interview is provided. The method includes obtaining, through an interface displayed on a screen, an objective of an interview from a user; deriving one or more topics from the objective of the interview using a large language model (LLM) and displaying, on the interface, the one or more topics; generating a plurality of questions using the LLM based on the objective of the interview and the derived one or more topics; providing the plurality of questions to a target user; obtaining answers to the plurality of questions from the target user; gathering the plurality of questions and the answers as knowledge units; and providing an answer to a question input by another user based on the knowledge units.

The application provides a kind of class incremental knowledge acquisition method based on prototype network gate mechanism, including obtaining text to be predicted;Text is input into the encoding module trained by the class incremental learning method in the knowledge acquisition field, the feature vector of each word element is obtained, and the feature vector of each word element is used to construct the unit representation of each knowledge unit;Determine the category to which each knowledge unit belongs using the preset prototype network gate mechanism, including: obtaining the similarity between the unit representation of each knowledge unit and the prototype representation of each category, predicting the knowledge unit according to the similarity of the screened expert network, obtaining the prediction value of the screened expert network to predict whether the knowledge unit belongs to the category corresponding to the expert network;For overlapping knowledge units, only the prediction value of the knowledge unit with the highest prediction value is retained;If the prediction value of a knowledge unit in all categories is less than a preset threshold, the knowledge unit does not belong to any category corresponding to the expert network.

The application discloses a kind of knowledge acquisition method and system based on causal inference, belong to natural language processing field.The application first constructs causal graph by identifying the causal dependency between events in text.Then input process text containing missing steps and candidate steps, extract events and judge whether there is causal relationship by causal graph, and take relevant causal information as additional features.Calculate the probability of containing and not containing leading entity in candidate steps respectively, encode by two kinds of models, and calculate the probability of candidate steps as missing steps by combining multilayer perceptron.Finally, compare the probability of all candidate steps with negative class threshold, determine the candidate step that meets the conditions or judge that there is no correct step.The application additionally introduces the causal relationship between events, so as to realize more accurate knowledge acquisition capability.

To provide a method for knowledge acquisition based on data relative steady-state analysis configured to improve the precision and other performances of digital decision jointly driven by limited multi-source data and knowledge.SOLUTION: A method to solve the problem of knowledge acquisition of limited multi-source data under the condition of no domain expert knowledge comprises: based on relative steady-state analysis of data elements, defining reference data elements and accompanying data elements, and calculating trend degrees and trend directions of the reference data elements and the accompanying data elements and a relative trend value of the accompanying data element; and obtaining quantitative knowledge behind the data element, a data unit or an entity node based on the relative trend value of the accompanying data element.SELECTED DRAWING: Figure 1

The application discloses a semi-quantitative fault diagnosis system based on the combination of bond graph and expert system, which comprises the following steps: a data module is used for importing fault data into the system; a field graph module is used for drawing a nuclear power plant equipment and system graph; a reasoning machine module is used for designing a reasoning logic architecture, adaptively changing a quantitative bond graph model and designing the reasoning machine module through a qualitative bond graph method; and a threshold setting module is used for setting a parameter alarm threshold; if an input parameter deviates from the range, a corresponding reasoning event is triggered in the reasoning machine module, and a change trend and a diagnosis result of other parameters are obtained according to reasoning of the reasoning machine module. The application can also diagnose unknown faults through coefficient abnormalities, and can judge fault sizes through parameter abnormal ranges to realize semi-quantification. Compared with other methods, the application has higher adaptability and wider diagnosis range in the fault diagnosis of a nuclear power plant complex system, and a new expert system knowledge acquisition method is added.