1472 results about "Semantics" patented technology

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health, poster design and the like, and discloses a visual sense generation method and device based on semantic association modeling, equipment and a medium. Generating a demand text containing theme and style parameters; semantic features in the demand text are extracted, semantic association weights are constructed, and element layout coordinates are optimized in combination with spatial distribution constraints; and encoding the layout information into a control matrix, fusing the control matrix with the initial noise, adjusting a noise reduction process through an encoding and decoding network, and generating target visual content highly matched with the semantic meaning of the user instruction. According to the method, the layout optimization function is constructed, the diffusion model is guided to focus the semantic salient region in space, language model output and the visual generation process are closely combined, structured response and space mapping of user semantic requirements are achieved, and the expression consistency and personalized adaptation capacity of visual content generation are improved.

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

The invention discloses an Agent-based tool combination and task processing method and device, equipment and a medium, and the method comprises the steps: carrying out the natural language processing of a task instruction inputted by a user, and generating a structured semantic vector containing task semantics; performing similarity matching on the structured semantic vector and a predefined tool node semantic tag in a knowledge base, and outputting a candidate tool node set related to the current task; performing dynamic priority ranking on the candidate tool node set based on context parameters to generate an initial thinking chain; converting the thinking chain into a tree flow chart structure, and performing dynamic addition and deletion or parameter adjustment on tool nodes in the flow chart according to a feedback or preset optimization strategy; and updating tool combinational logic according to the adjusted thinking chain, processing the task instruction, and outputting a task processing result. The tool nodes are matched through the Agent agents, and the thinking chain is generated through dynamic sorting, so that the task processing efficiency and accuracy are improved.

The invention discloses a dual-prevention intelligent interaction system based on a large language model, and belongs to the technical field of artificial intelligence. The system comprises a big language base model, a knowledge base management module, an agent module, a multi-modal big model and an evaluation module, and the big language base model understands user intentions and processes natural language tasks and provides token processing capacity for other modules; the knowledge base management module is used for classified storage and management of professional knowledge, establishing indexes according to semantics and finally performing localized storage; the intelligent agent module is used for analyzing input of a user, and different workflows are packaged in different intelligent agents so as to provide personalized services for the user; the multi-modal large model module receives voice input by a user, converts the voice into characters, transmits description of information to the Agent, generates answers by analyzing semantics, converts the characters into the voice and sends the voice to the user; the evaluation module establishes an evaluation index system from the four aspects of accuracy, answer correlation, context correlation and context recall rate.

The invention relates to the technical field of natural language processing, and provides an LLM-based complex language question SQL query generation method and system, and the method comprises the steps: responding to a target language question request; splitting a requested target language question sentence into one or more core clauses through a language model LLM, converting the core clauses into semantic vectors, calculating a matching score of each core clause and a question normal vector of each table in the schema knowledge document, performing sorting and screening according to the score size and a score threshold, and returning a recall result of the core clauses; combining and sorting the recall results of all the core clauses, and screening to generate a recall result of the target language question; and generating a Prompt through a Prompt script, and calling an LLM (Language Model) to automatically generate an SQL (Structured Query Language) query statement. The method does not depend on field fine tuning, does not need to reconstruct a wide table, and effectively solves the problem that a target table is difficult to position when a large language model processes coded value mapping, multiple tables and complex business logic by constructing a virtual table and splitting a question sentence into clauses containing complete semantics and matching the clauses.

The invention provides a document knowledge questioning and answering method, a document knowledge storage method and a document knowledge questioning and answering device, and relates to the technical field of intelligent questioning and answering. All layers of nodes of a knowledge tree are traversed according to a target retrieval mode on the basis of the query statement to obtain a query result, the knowledge tree comprises multiple layers of nodes, the multiple layers of nodes at least comprise first-layer nodes, second-layer nodes and third-layer nodes, semantics of the first-layer nodes are semantics of text contents of knowledge point levels in the document, and semantics of the third-layer nodes are semantics of text contents of knowledge point levels in the document; the semantics of the second-layer nodes are the semantics of the paragraph-level text content in the document, and the semantics of the third-layer nodes are the semantics of the full-text-level text content in the document; and outputting an answer text corresponding to the query statement based on the query result. The method provided by the invention not only improves the recall rate of knowledge and the correlation of answers, but also enhances the overall experience of the user, so that the user can obtain more comprehensive and accurate answers in complex queries.

The invention relates to a large language model construction method and system fused with spatial semantic understanding, and the method comprises the steps: obtaining a multi-source heterogeneous corpus, and extracting an entity, an attribute and a business rule; extracting a semantic feature vector set based on the multi-source heterogeneous corpus, and constructing an entity relationship network and an enhanced knowledge graph; generating an enhanced training sample, and training the general large language model to obtain a primary large language model; generating a verification sample set and performing verification; identifying a specific weakness pattern, and generating a corresponding confrontation sample and a knowledge enhancement sample; training the primary large language model to obtain an optimized large language model; in conclusion, the enhanced knowledge graph fusing the spatial semantic features and the business rules is constructed, and the gradient training samples are generated based on the graph to perform multi-stage model training and optimization, so that the method has the effects of improving the internalized understanding ability of the model for the spatial semantics and the business rules and enhancing the reliability of multi-step spatial reasoning.

The invention relates to the cross technical field of business intelligence and natural language processing, in particular to an interactive AI report generation method and system based on intelligent semantic driving, and the method comprises the steps: (1) a large language model analyzes user question semantics, calculates the matching degree with a report subject, and dynamically selects a question correction or report generation process; (2) the fuzzy query is converted into a deterministic request based on the domain knowledge base, and semantic ambiguity is eliminated; (3) analyzing field association in combination with database metadata, and generating an executable SQL (Structured Query Language); (4) dynamically rewriting SQL (Structured Query Language) according to user roles, adding permission filtering conditions, executing query and desensitizing sensitive data; (5) analyzing result features, automatically matching a visual form and generating natural language interpretation; and (6) collecting user feedback, and optimizing a semantic model and a query strategy. According to the scheme, the accuracy of natural language query and the adaptive capacity of the system can be remarkably improved, and the security of data access is guaranteed.

Systems and methods are described for providing for display a user interface to facilitate creation of a three-dimensional (3D) environment. The disclosed techniques may generate the 3D environment based on one or more inputs received via the user interface, and perform visual processing of the 3D environment to obtain a natural language description of the 3D environment. The disclosed techniques may determine a context of the 3D environment based at least in part on the natural language description and may store the context in a data structure. A 3D content library may be queried, based on the natural language description of the 3D environment, to identify at least one recommended 3D object that is relevant to the stored context of the 3D environment. The disclosed techniques may providing for display, at the user interface, selectable option(s) to add the at least one recommended 3D object to the 3D environment.

The invention relates to the technical field of big language models, and discloses a big language model training method based on knowledge graph enhancement, comprising the following steps: S1, constructing a multi-source heterogeneous knowledge graph; s2, coding the mixed attention heterogeneity map; s3, bidirectionally mapping a pre-training task; and S4, position specific gating fusion. According to the big language model training method and system based on knowledge graph enhancement, a same proton graph is established for a structured triple and text entity description, nodes are connected across graph edges to form a heterogeneous graph, associated edges are established through entity linking and syntactic analysis, multi-source knowledge is modeled in a unified mode, and the problem of low fusion efficiency is solved; mixed attention coding adopts a layering mechanism, a semantic level calculates weights according to type compatibility, a node level calculates similarity aggregation features through cosine distance and path length, entity vectors are generated through pooling, map structures and semantics are explicitly learned, reasoning accuracy is improved, and the problem of knowledge understanding superficial layer is solved.

The invention belongs to the technical field of natural language processing. According to the zero resource framework-based fine-grained large model illusion processing method and system, multi-dimensional anomaly detection is carried out on a preprocessed text through a large language model, and a multi-dimensional anomaly detection result is obtained; performing anomaly correction priority ranking according to a multi-dimensional anomaly detection result; dynamically adjusting a context window range according to a syntactic analysis result and the generated syntactic dependency tree; performing priority correction on high-risk vocabularies and sentences according to an abnormal correction priority ranking result in combination with the adjusted context window range; and evaluating the corrected content, and obtaining a corrected final text when a termination condition is met. According to the method, the excessive confidence, subtle semantics and grammar deviation illusion detection of the detection content is realized, and the illusion detection processing capability of a large language model, especially the detection processing capability of a fine-grained illusion problem, is improved.

The invention relates to the technical field of artificial intelligence and natural language processing, and particularly discloses a text content generation method based on artificial intelligence, and the method comprises the steps: obtaining natural language text input, and extracting a semantic recognition feature vector; acquiring context state data and encoding the context state data into a state recognition feature vector; generating a fusion feature vector containing a semantic and state association relationship through fusion analysis; constructing a causal discrimination model based on the fusion features, and outputting the matching confidence of semantics and states; dynamically adjusting a generation strategy according to the confidence coefficient, if the matching degree is high, generating a standard text, otherwise, triggering an error correction mechanism to output a corrected text; and finally, performing logic consistency verification on the generated text to ensure that physical constraints, technological procedures and safety standards in the industrial field are met. According to the method, by introducing multi-level feature fusion, causal reasoning, intelligent error correction and rule verification mechanisms, context perception and safety controllability in the text generation process are achieved.

The invention provides a financial fraud detection method based on a large language model. The method comprises the steps of obtaining a to-be-recognized text; performing word segmentation on the to-be-recognized text through the target word segmentation tool and the financial fraud dictionary, and determining a fraud sensitive word list; calculating the weight of each sensitive word in the sensitive word list according to a target algorithm to obtain a sensitive word weight feature vector; inputting the sensitive word weight feature vector and a to-be-recognized text into a large language model, and determining a context semantic vector of the sensitive word in combination with a word embedding technology; determining the similarity between the context semantic vector of the sensitive word and a preset financial fraud type semantic vector; and determining a financial fraud type according to the similarity. Through the implementation of the method, the generalization ability of the pre-training model is utilized to capture text deep semantics, priori knowledge is injected in combination with a sensitive word weight mechanism, the model is guided to focus high-risk vocabularies, the defect of a traditional method in semantic comprehension is overcome, the financial fraud recognition rate is increased, and the omission ratio is reduced.

The invention discloses a four-dimensional-based resume and post matching analysis method and system, and relates to the technical field of data information processing. The method comprises the steps of obtaining an original JD text and performing text preprocessing; four-dimensional information is set, and information extraction is carried out based on four dimensions; establishing a mapping relation between the JD extraction field and the resume four-dimensional degree; performing resume matching based on four dimensions; carrying out dynamic weight distribution; performing comprehensive scoring based on the dynamic weight distribution information; and result verification and output are carried out. The system comprises a preprocessing module, a four-dimensional information extraction module, a matching calculation module, an interpretability module and a verification and output module. According to the method, a four-dimensional extraction framework directly corresponding to a resume system is proposed for the first time, a four-dimensional mapping model is formed, semantic alignment of recruitment requirements and talent resumes is realized, and the analysis accuracy of complex semantics is improved in combination with a rule engine and a machine learning model.

The invention discloses a sensitive word auditing method, which belongs to the field of auditing, and comprises the following steps of: firstly, preprocessing an input text, such as removing spaces, symbols, stop words and the like, so as to generate a word segmentation result; thirdly, constructing a dynamic synonym and a deformation word library, combining user behaviors and NLP technology updating, and expanding word meanings by applying a word vector model Word2Vec; then, based on BERT, a text context is analyzed, and metaphors and biguan languages are detected. Performing sensitive word primary screening and secondary screening by using a static word bank and an SVM (Support Vector Machine), and analyzing complex semantics by LSTM (Long Short Term Memory); the model is optimized based on a feedback self-learning mechanism, difficult contents are manually checked, an analysis report is generated, and finally a word bank and an algorithm are periodically updated to adapt to hotspots. The method has the beneficial effects that sensitive word auditing is improved through a self-learning mechanism, synonyms and deformed word banks can be dynamically updated, and private expressions are identified. BERT and LSTM model multi-level auditing are adopted, secondary screening is carried out in combination with SVM, and recognition of complex contexts and biguan languages is improved.

The invention provides a natural language text data intelligent classification method and system based on deep learning, and relates to the technical field of natural language processing, and the method comprises the steps: 1, employing a context awareness mechanism to analyze the real semantics of a target vocabulary according to an antagonistic variant existing in a text, and obtaining a target vocabulary; in combination with a word meaning library and a pre-training process of a dynamic learning rate adjustment strategy, generating a candidate replacement vocabulary set with consistent semantics; and step 2, based on the candidate replacement vocabulary set, performing multi-dimensional semantic similarity calculation and emotional tendency discrimination, determining applicable vocabularies conforming to an original culture background through a context adaptation strategy, and generating a standardized text sequence. According to the method, through multi-dimensional semantic analysis, cultural context fusion, cross-granularity feature construction and dynamic parameter correction, the accuracy and adaptability of natural language text classification are realized.

The invention relates to the technical field of artificial intelligence, can be applied to the field of medical health, and discloses a timbre conversion method, device, equipment and medium based on cultural semanteme, the method comprises the following steps: constructing a cultural semantic timbre library comprising a semantic label and timbre characteristic parameter mapping relationship, the semantic label characterizing emotional semanteme of a target timbre, and the timbre characteristic parameter mapping relationship between the semantic label and the timbre characteristic parameter; the timbre characteristic parameters comprise a pitch range, rhythm rhythm and a harmonic structure; performing feature extraction based on text, image and audio multi-mode information to obtain semantic keywords, visual emotion features and audio acoustic features; performing attention weight fusion on the features through a multi-modal fusion deep learning model, and dynamically adjusting model parameters in combination with a semantic timbre library to generate a target timbre; and finally, intelligent conversion from the multi-mode information to the adaptive tone is realized. Through semantic-driven multi-modal feature collaborative optimization, the defect that timbre conversion machinery is stiff and lacks emotional expression is overcome, and the integrating degree of timbre expression and semantic scenes is improved.

The embodiments of the present application relate to the technical field of artificial intelligence. Provided are a prompt anomaly detection method and apparatus, and a device and a storage medium. The method comprises: acquiring a prompt to be subjected to detection and the perplexity of said prompt; if the perplexity exceeds a second threshold value and does not exceed a first threshold value, using an adversarial perturbation mode to process said prompt, in order to acquire a perturbed prompt; on the basis of prediction by a large language model, obtaining response semantics of said prompt and perturbed response semantics corresponding to the perturbed prompt; and if the response semantics and the perturbed response semantics are inconsistent, said prompt being anomalous. Two threshold values are set for perplexity detection, and after perturbation processing is performed on a prompt to be subjected to detection, whether the prompt is anomalous can be determined again by means of the comparison between response semantics and perturbed response semantics, so that the accuracy of prompt anomaly detection can be effectively improved, thereby enhancing the security of large language models.

The invention discloses an ICL large language model data query generation method and system based on diversity SQL enhancement, and the method comprises the steps: S1, determining a target query database, and obtaining a database Schema; s2, vectorizing a database Schema, and storing the vectorized database Schema into a database meta-information vector library; s3, vectorizing SQL statements in the SQL sample set, and storing the vectorized SQL statements in an SQL sample vector library; s4, performing clustering diversity sample recall on the information stored in the SQL sample vector library; s5, according to the database summary and the natural language question of the user, combining the recalled sample question and the corresponding SQL query statement as model reference information, constructing comprehensive prompt information, and guiding the large language model to generate SQL query conforming to semantics and grammar; and S6, inputting the large language model, and generating SQL statements by the large language model. According to the method, the Prompt is formed by recalling the reasonable related information, the model is guided to correctly generate the SQL, and the accuracy of the SQL statement generated by the large language model can be effectively improved.

The invention discloses a large model text auditing optimization method based on clustering preprocessing, which relates to the technical field of natural language processing, and comprises the following steps: S1, preprocessing input original text data, including text cleaning, word segmentation, stop word and punctuation mark removal and standardized text vector generation, during preprocessing, a TF-IDF algorithm is adopted to calculate a text feature weight, and a high-dimensional feature vector matrix is generated. According to the large model text auditing optimization method provided by the invention, the text is divided into clusters with similar semantics or theme contents through clustering preprocessing, and a special auditing model is constructed for each cluster, so that the models can more accurately understand the text contents, the auditing accuracy is improved, and meanwhile, the auditing efficiency is improved. Due to the fact that the data size needing to be processed is reduced through clustering, the calculation efficiency of the model is remarkably improved, the clustering center vector and the cluster division rule are continuously adjusted, and closed-loop optimization of the model is achieved.

The invention belongs to the technical field of industrial equipment life prediction, and discloses an equipment life prediction method and system of a large language model based on semantic alignment, and the method comprises the steps: obtaining original multi-dimensional sensor time sequence data, and obtaining an embedded matrix after preprocessing; constructing a prompt text with domain semantics to obtain a natural language embedded representation; constructing a semantic text prototype, and realizing alignment of the embedding matrix and the semantic text prototype to obtain a patch embedding sequence; and splicing the patch embedding sequence and the natural language embedding representation, inputting the spliced patch embedding sequence and the natural language embedding representation into a pre-trained large language model for forward propagation, extracting hidden vectors output corresponding to the patch embedding sequence, splicing and flattening the hidden vectors into a single vector, and outputting to obtain an equipment life prediction result. According to the method, cross-modal knowledge learned by the LLM in large-scale pre-training and the powerful reasoning ability are fully utilized, and accurate prediction of the residual life of the equipment is achieved.

The invention discloses a header column recognition method based on depth semantics and context self-adaption, which comprises the following steps of: performing structured analysis on a target table file by utilizing a table analysis tool to obtain a target header text, a target table main body text and a target table structure corresponding to the target table file; performing feature coding on the target header text to obtain target header features; performing feature extraction on the target table main body text and the target table structure to obtain context features of a target scene; fusing the target header feature and the target scene context feature to generate a target comprehensive feature; and inputting the target comprehensive features into a header semantic classification model, outputting the probability of each category corresponding to the target form file, and taking the category with the maximum probability as a final header semantic classification result. According to the method, the deep semantics of the header can be automatically extracted and understood, and the actual meaning of the header under different tasks can be accurately judged by fully utilizing the table content and the context information of the business scene.

The invention particularly relates to a document abstracting method based on domain knowledge and a multi-granularity graph network, which comprises the following steps: S1, constructing a knowledge coding module, carrying out domain knowledge graph transfer learning, extracting triple structure features in combination with a capsule network, and generating a text entity coding module; s2, constructing a knowledge fusion module, converting a document text into a semantic vector, and adaptively fusing the semantic vector with external knowledge; s3, constructing a word-level semantic graph, and aggregating neighborhood information through a semantic graph attention mechanism to obtain word embedding representation containing context; s4, constructing a sentence-level semantic graph, performing self-attention calculation on the word-level semantic vector in the S3 to obtain semantic node representation of the sentence-level semantic vector, and performing feature aggregation on sentence-level nodes to obtain global semantic information of the document; and S5, constructing an output layer, and predicting according to the document feature vector in the S4 to obtain a final extraction type abstract result. The method has the advantages that redundant information in the document is removed, and ambiguity of text semantics is eliminated.

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention discloses a large model knowledge distillation method based on anti-fact data enhancement and a multi-view thinking chain. According to the method, firstly, key features of text semantics are obtained through the subject terms and the grammar analysis tree, high-quality anti-fact data are automatically generated through the large language model, then the diversity of the anti-fact data is enhanced through the multi-view thinking chain, and the reasoning ability of the small language model is improved. Compared with the prior art, the large-model anti-fact distillation framework provided by the invention can enhance the robustness and generalization ability of small-model reasoning, and has excellent performance on small language models with different architectures and data sets with different distributions.

The invention discloses a scientific paper academic value measurement method based on semantic fusion and reference behavior analysis. The method comprises the following steps: determining a literature database according to a target research field, constructing a field keyword list, and retrieving and collecting an initial literature data set; the internal innovation value is quantified; quantizing an external approved value; the step of constructing the academic value comprehensive measurement model comprises the sub-steps of applying an entropy weight method to distribute index weights; calculating an academic value comprehensive score through linear weighting; checking and evaluating through a Spearman correlation coefficient; according to the method, a comprehensive evaluation system based on multi-dimensional semantics and behavior characteristics is constructed, internal knowledge contribution and external recognition value of the papers are systematically fused, objectivity and precision of scientific research paper academic value evaluation can be effectively improved, and therefore the problems of evaluation time lag, reference behavior deviation and subjective weighting existing in a traditional method are solved, and the scientific research paper academic value evaluation efficiency is improved. And the method has good universality and popularization value.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a cue word compiling and generating method and system for a large language model.The method comprises the steps that in response to a compiling calling request, a PDL protocol is received to serve as a compiling input text; performing lexical and grammatical analysis on the compiled input text, and constructing a corresponding abstract syntax tree AST as an intermediate representation according to a context-independent method; semantic verification and structure correction are carried out on the AST, and when semantics are effective, structure optimization is carried out to generate an optimized AST; and traversing the optimized AST, matching a cue word template in a cue word generation rule base, mapping semantic nodes into structured text fragments, splicing the structured text fragments to form a final cue word text, and outputting a compilation result consistent with PDL protocol semantics. By introducing a PDL-oriented compiling mechanism, automatic analysis, structure optimization and high-quality cue word generation of an input protocol are realized before cue words of a large language model are generated, and the development efficiency and the output quality of the cue words are improved.

The invention discloses a knowledge base technical method and system based on RAG retrieval enhancement, and relates to the technical field of knowledge bases. The method comprises the steps that a source document is analyzed into structured text fragments, and semantic vectors are generated; splitting the text segments into minimum knowledge units, extracting concepts and behavior trigger words to construct a concept association graph, and storing session abstracts in a fixed-length annular structure; the method comprises the following steps: receiving user query, generating a query vector, retrieving a most relevant text fragment, constructing and de-duplicating candidate knowledge units by combining hierarchical diffusion of a concept association graph and an annular abstract matching result, deeply splicing original text paragraphs according to a graph path, generating a dynamic prompt box, calling a generative model to generate a preliminary answer, and executing verification. And the verification state is marked on the final answer. By constructing a concept association map, the activeness weight and the time sequence fingerprint of a map edge are updated in real time, and accurate capture of deep semantics and logical relationships of query intentions is realized.

The invention provides an AI auxiliary reading method and system based on dynamic partitioning knowledge classification self-verification, and relates to the technical field of artificial intelligence, the method comprises the following steps: carrying out dynamic semantic partitioning on a super-long text to obtain a plurality of initial text units with consistent internal semantics; performing topic model construction according to the initial text unit and a layered Dirichlet process to obtain a topic distribution tag; receiving a user query request, and extracting a retrieval text set according to the hierarchical retrieval strategy and a retrieval text corresponding to the user query request; inputting the constraint cue word extracted by the topic distribution tag corresponding to the retrieval text set, the retrieval text set and the retrieval text into a text generation model to output a to-be-calibrated text; if the error scoring function of the to-be-calibrated text and any initial text unit in the retrieval text set is smaller than a preset error threshold value, the to-be-calibrated text serves as a credible text to be output to a user. According to the invention, the credibility and accuracy of the answer can be enhanced.

The invention discloses a knowledge graph reasoning method based on dynamic rule perception memory. The method aims at solving the problems that an existing neural combination rule learning method is insufficient in expression ability and prone to splitting global semantics and local relation modes. The method comprises the following steps: introducing a lightweight dynamic relationship memory module, and executing self-attention on all relationships in a knowledge graph to capture global semantics; meanwhile, local relation interaction features are extracted through convolution, semantic fusion is carried out through a Transform encoder with multi-head attention and relative position coding, and unified and parallel-computing high-quality combination representation is constructed for each pair of relations in parallel. And meanwhile, a closed high-quality reasoning path is generated in combination with bidirectional breadth-first search. Through global semantic and local interactive collaborative modeling, the extendibility is ensured, the understanding of global semantics is enhanced, and the reasoning accuracy is improved. Global and local relation dependence is fused, an interpretable reasoning path is generated, and reasoning accuracy, expandability and robustness are improved.