101 results about "Entity relation extraction" patented technology

The invention discloses a power equipment fault diagnosis method and system based on a dynamic knowledge graph and large model collaborative reasoning, and the method comprises the steps: achieving the automatic extraction of an entity relationship through a weak supervision entity relationship extraction mechanism in combination with a power field dictionary and a remote supervision technology, and obtaining a weak supervision entity relationship; constructing a time sequence knowledge graph to capture a dynamic evolution rule of the fault propagation chain; structured knowledge graph embedded representation is fused with a large model input layer through a knowledge injection layer, a two-stage reasoning process is generated by adopting a graph retrieval enlarged model, and finally a diagnosis conclusion containing a structured evidence chain is generated. According to the method, the fusion of weak supervised learning and sequential relation modeling is realized, and the automatic extraction and dynamic updating capability of the knowledge in the electric power field is remarkably improved; through a knowledge injection layer and a two-stage joint reasoning mechanism, the structured reasoning advantage of a knowledge graph and the semantic generation capability of a large model are effectively combined, and the diagnosis accuracy, the time sequence reasoning capability and the interpretability are greatly enhanced.

The invention discloses a method for constructing a cross-modal knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the collection, structural analysis, modal recognition and classification, cleaning and standardization processing, so as to form structured multi-modal data; extracting entities and relationships of the identified modals from the structured multi-modal data, and summarizing the entities and relationships to form a multi-modal knowledge element set; mapping different modal entities in the multi-modal knowledge element set to a unified semantic space, and generating a unified entity relationship set through semantic matching, alignment and fusion; and normalizing the data into knowledge triples, and storing and organizing the knowledge triples through a graph database to form a cross-modal knowledge graph. According to the method, the problems of difficulty in multi-modal heterogeneous information alignment and difficulty in entity relationship extraction can be relieved, semantic association is enhanced, and knowledge graph integrity and multi-scene adaptability are improved.

The invention specifically discloses a triage training system and method based on a large language model and a knowledge graph, and relates to the technical field of medical training. The system comprises a multi-modal data sensing and processing module which is used for accessing and processing diversified training data sources; the dynamic time sequence knowledge graph construction and updating module comprises an entity relationship extraction unit, a multi-modal alignment unit and a knowledge fusion and conflict resolution unit; the multi-agent collaborative service module is composed of large language model agents which are special in function and work cooperatively, and comprises an extraction agent, a construction agent, a reasoning agent and a teaching agent; and the immersive interactive training and feedback module comprises a virtual case generation unit, a multi-modal interactive interface and a deep feedback system. According to the invention, the authenticity, accuracy and effectiveness of training are significantly improved, it is ensured that the learned knowledge and skills can efficiently adapt to a real emergency treatment scene, and the accuracy and efficiency of triage classification in emergency treatment are improved.

The invention discloses a medical question and answer method based on knowledge graph and retrieval enhancement generation, and belongs to the technical field of natural language processing and knowledge management. The method includes the following steps that text blocks are obtained on the basis of medical document preprocessing and partitioning, and an abstract tree is constructed; entity relationship extraction is carried out based on the text blocks, and a knowledge graph is constructed; constructing a mapping relationship between the abstract tree and the knowledge graph, and obtaining a vector database; performing entity extraction and query association degree calculation based on user query, judging a retrieval type of the user query, and obtaining a corresponding retrieval result by utilizing the vector database according to the retrieval type; and inputting a retrieval result into the large language model to generate a result. Aiming at the defect of insufficient quality of intelligent questions and answers generated in the medical field by retrieval enhancement generation, the method disclosed by the invention has the advantages that depth and breadth can be considered by one-time retrieval by constructing an abstract tree and a knowledge graph, forming an abstract tree-knowledge graph double-layer index and dynamically typing according to the query association degree, so that the accuracy and efficiency of medical questions and answers are improved.

The invention discloses a joint multi-modal entity relationship extraction method and system based on information representation and semantic alignment, and the method comprises the steps: obtaining sample data composed of an original image and a text, and obtaining the feature representation of visual information and text information based on the sample data; aligning the feature representation of the visual information and the feature representation of the text information by using a progressive modal semantic alignment strategy; by introducing a multi-layer correlation mapping mechanism guided by fine granularity, the correlation coefficient represented by the features of the aligned visual information and text information is judged, and features irrelevant to a task core are filtered; performing visual representation and text interaction by using a multi-modal interaction module to obtain multi-modal semantic features; carrying out weighted mapping on the multi-modal semantic features by utilizing a routing weighting function, and finally obtaining multi-modal feature representation; and sending the multi-modal feature representation into a word pair relation label extractor, and extracting an entity, an entity relation and an entity attribute quintuple.

An RAG auxiliary generation type search engine method based on multiple rounds of expert and self-adaptive mixed retrieval comprises the steps that a crawler is used for crawling data, the crawled data are processed, a graph knowledge base is constructed through text partitioning and entity relation extraction, the graph knowledge base is coded into semantic vectors, and a vector database is established; by calculating similarity between subject vectors and literature vectors in a vector database, retrieving related literatures, utilizing a large language model to simulate a multi-expert role to generate differentiated viewpoints, and combining multiple rounds of dialogues and user feedback, generating a multi-view fused article to complete multiple rounds of expert answers; designing a double-layer retrieval mechanism according to different nodes and relationships in the retrieval atlas based on the atlas knowledge base; meanwhile, dense retrieval of a vector knowledge base of text coding is combined and utilized, a map-vector collaborative retrieval mechanism is designed, a self-adaptive mechanism and a session memory function are introduced, and multi-round question and answer response is carried out; according to the method and the system, higher logical contents can be generated.

Methods, systems, and software are provided for determining a relationship between a subject and a health entity. An electronic health record (EHR) for the subject is split into sections by detecting delineating section headers, and sections are subdivided into text spans. Text spans are filtered by language pattern recognition into a set of text spans having an expression related to the health entity. The natural language context of the expression in each text span in the set is evaluated to obtain a corresponding scoring representation. Scoring representations are inputted into a model comprising a plurality of parameters. The model outputs, for each text span in the set, at least a prediction that the text span is associated with the health entity. Models for determining relationships between subjects and health entities and methods for training models to determine relationships between subjects and health entities are also provided.

The invention relates to the technical field of entity relationships, and discloses a multi-feature fused entity relationship extraction optimization model construction method and system, and the method comprises the steps: obtaining an input text sequence and a relationship label set, and carrying out the coding processing, and obtaining a text embedding vector and a relationship embedding vector; calculating the similarity between the text embedding vector and the relation embedding vector, screening in combination with a similarity threshold to obtain a candidate relation subset, and fusing the relation embedding vector and the text embedding vector in the candidate relation subset to generate a relation enhancement vector; processing the relation enhancement vector through a mixed attention mechanism to obtain a semantic enhancement vector; constructing a three-dimensional marking matrix based on the semantic enhancement vector, and marking the three-dimensional marking matrix by adopting a diagonal marking strategy; and carrying out decoding processing on the three-dimensional mark matrix, and generating a structured entity relationship triple by adopting a constraint decoding strategy. The interactive fusion of the text and the relation label can be accurately processed in a unified coding space.

The invention discloses an entity relationship extraction method, device and equipment for constructing a medical knowledge graph, and relates to the technical field of artificial intelligence. The method comprises the following steps: encoding an input text to obtain a first text feature corresponding to the input text; decoding the first text feature to obtain a first entity in the input text; the first text feature is decoded according to the first entity, a second entity in the input text is obtained, and the first entity and the second entity form an entity pair; decoding the input text based on an attention mechanism according to the entity pair to obtain an entity relationship between the first entity and the second entity; an entity relation triple is generated, the entity relation triple comprises the first entity, the second entity and the entity relation between the first entity and the second entity, and the entity relation triple is used for constructing the medical knowledge graph. According to the method, the accuracy of the extracted entity relationship triad is improved.

The embodiment of the invention discloses a risk perception method and device for a global supply chain enterprise and a medium, and relates to the technical field of data analysis, and the method comprises the steps: receiving a risk perception request of a target enterprise for an enterprise perception object, the enterprise perception object comprises a target product and a target shipping port, the risk perception request comprises real-time perception and simulation interruption perception; based on the risk perception request, performing entity relationship extraction from a pre-constructed dynamic knowledge graph to form an evaluation sub-graph, and calculating a multi-dimensional quantitative toughness index through the evaluation sub-graph; and fusing the multi-dimensional quantitative toughness indexes to generate a comprehensive toughness index corresponding to the enterprise perception object, and carrying out risk perception on the enterprise perception object based on the comprehensive toughness index.

The invention relates to an electric power large model interpretability-based fault analysis method, device and system. The method comprises the steps of performing standardization processing on multi-source historical data to obtain standardized multi-source historical data, wherein the multi-source historical data comprises power grid historical operation parameters, historical meteorological data and historical fault records; performing entity identification and entity relation extraction on the standardized multi-source historical data to obtain an electric power knowledge graph file, and inputting the electric power knowledge graph file into a graph convolutional network to obtain a knowledge embedding vector; adding a LoRA adapter behind an attention layer in the obtained large electric power model to obtain an improved large electric power model; the LoRA adapter is initialized by utilizing knowledge embedding vectors, and the improved large electric power model is finely adjusted by utilizing standardized multi-source historical data; obtaining a fault type prediction result by using the fine-tuned improved electric power large model; and querying the electric power knowledge graph file, and carrying out path search to obtain a fault reason of a prediction result. By adopting the method, the diagnosis precision and the interpretability can be improved.

The invention discloses a small sample relation extraction method and system based on multiple information interaction, and relates to the technical field of entity relation extraction, and the method comprises the specific steps: obtaining a support set and a query set; respectively coding input instances of the support set and the query set based on a coding model, extracting hidden states of initial marks of a head entity and a tail entity, splicing to form a support instance and a query instance, and generating a composite embedded relation triple instance; calculating the similarity between the support instance and the query instance, and the similarity between the relation triple instance and the query instance; performing weighted fusion on the support instance and the relation triple instance based on the similarity weight to generate a prototype vector of each relation category; and judging a relationship category based on the similarity between the query instance and each prototype vector. According to the method, the relation triple rule base is constructed, entity type embedding is injected in the encoding stage, a double-constraint mechanism is formed, and the credibility of a prediction result is remarkably improved.

The invention discloses a relation perception type two-channel entity relation extraction method. The method comprises the following steps of S1, encoding sentences through an encoder; s2, inputting the sentence vectors into a potential relation extractor, and extracting potential relation features; s3, inputting the potential relation characteristics into a dual-channel extractor, and extracting from two channels of head entity priority and tail entity priority respectively; and S4, judging the association among the subject, the relationship and the object through the bisimulation network, aggregating the entity pairs extracted by the two channels, and performing relationship classification on the entity pairs. The problems that relation recognition lacks entity semantic support and error prediction is irreversible in a relation priority type joint extraction method can be solved.

The embodiment of the invention provides an entity relationship extraction method and device, equipment, a storage medium and a program product, pseudo entities are constructed based on continuous vocabulary segments of which the length is within a preset range in a target text, feature vectors of the pseudo entities are extracted, the feature vectors corresponding to any two pseudo entities are spliced, and an entity relationship extraction result is obtained. Constructing feature vectors corresponding to the pseudo-relationships, screening out the pseudo-relationships with confidence greater than or equal to a preset threshold value as effective relationships, taking pseudo-entities corresponding to the effective relationships as effective entities, constructing an effective entity relationship graph, extracting feature vectors corresponding to the effective entities from the effective entity relationship graph by using a graph convolutional neural network, and obtaining feature vectors corresponding to the effective entities; according to the method and the device, the effective entities are extracted, the feature vectors corresponding to the effective relations are constructed, the feature vectors corresponding to the effective entities and the feature vectors corresponding to the effective relations are identified, the entity types and the relation types corresponding to the target texts are obtained, and waste of operation resources can be reduced in the entity relation joint extraction process.

The invention relates to the technical field of natural language processing, in particular to an entity relation joint extraction method and system. Cross-span semantic clues are gathered in a channel dimension through a full-sentence semantic focusing unit so as to suppress semantic drift under long-distance dependence, and relative direction and distance information between entities is converted into learnable modulation quantity through an entity-to-geometric prior injection unit so as to reduce pairing ambiguity. And distinguishing subject and object representations through a subject-object directional interaction unit to carry out directional convergence so as to avoid role confusion, and finally realizing joint prediction of entity boundaries and relation types in a unified framework. According to the system, the stability and the accuracy of an extraction result can be remarkably improved in a multi-entity coexistence and relation overlapping scene, entity boundary intersection and subject-object inversion errors are reduced, the structured triad can be directly generated without relying on a post-processing rule, and a more reliable solution is provided for application such as knowledge graph construction and text analysis.

The invention discloses a fault positioning method, system and device and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: collecting alarm data, carrying out the entity relation extraction and link traceability tracking of the alarm data, and obtaining a corresponding text entity relation triple and a potential fault propagation path; performing multi-dimensional alarm association aggregation on the alarm data to generate a corresponding alarm clustering set; and in combination with variable information and multi-dimensional feature values in the text entity relationship triple, performing deep aggregation and association degree evaluation on the alarm clustering set to obtain a target fault set and a corresponding association degree score, and in combination with the association degree score and a potential fault propagation path, positioning a target root cause fault. According to the method, identification and convergence of alarm storm are realized through multi-dimensional alarm association aggregation, and root cause faults are quickly positioned in combination with a fault propagation path and a text entity relationship triple.

The application discloses a geological entity relation extraction method based on a field multi-prompt template and relates to the technical field of geological text analysis, which comprises the following steps: collecting geological survey reports and preprocessing the same to obtain original texts; performing data enhancement on the original texts to obtain enhanced texts; constructing prompt templates and target templates; optimizing the prompt templates by using the enhanced texts and the target templates to obtain updated prompt templates; splicing the enhanced texts and the updated prompt templates to obtain new prompt templates; transmitting the enhanced texts and the new prompt templates to an input sequence encoder for coding to output hidden layer word vectors; jointly decoding entity positions and relations in the hidden layer word vectors by using three global pointer networks to obtain relation triplets; and performing mapping processing on original relation types according to the differences in the meanings of different relation types. The application can perform data enhancement for small sample fields, improve semantic understanding capability and realize high-precision and high-confidence geological entity relation extraction.

The invention relates to an electric power entity joint relation extraction method and system based on a multi-modal large model in the technical field of electric power system automation control, and the method is used for inputting collected electric power texts and electric power equipment images into a trained electric power entity joint relation extraction model. The entity-relation triple and a visual knowledge graph are output; the power entity joint relation extraction model comprises a data acquisition module; a text preprocessing module; a text encoder (BERT-Base); a visual encoder (Swin Transform) is arranged; a fusion layer; a cascading type binary annotation framework CasRel is adopted; according to the method, through multi-modal fusion and a joint extraction framework, the technical problems that an existing electric power entity relation extraction method is difficult to identify an overlapping relation in an electric power text, the multi-modal data fusion efficiency is low, the field adaptability is poor and the like are effectively solved.

The application discloses an entity relation extraction method and device in the network security field, and relates to the network security field. According to the features of the target of attention in the network security field, the application generates a semantic matrix of each segment by enumerating segments of a certain length in sentences of multi-source heterogeneous network security data, thereby improving the accuracy of an entity recognition model. On this basis, the entity pair vector is re-encoded, and the entity subject-object boundary, entity type and attribute features are supplemented into the input of the relation extraction model to obtain a more accurate relation extraction model and reduce the error propagation method. Further, the application filters and judges the segments with a higher frequency and an unrecognized entity type, supplements them into the entity type set and the entity relation set, continuously optimizes and feeds back, and improves the recognition breadth and accuracy of the model.

The invention belongs to the technical field of natural language processing, and particularly relates to an entity relation joint extraction method oriented to industrial knowledge graph construction and causal reasoning driving. The method comprises the steps of obtaining a to-be-processed industrial field unstructured text and inputting the to-be-processed industrial field unstructured text into a pre-training language model for shared coding to obtain a shared feature sequence; processing the shared feature sequence by adopting a dual-channel gating module to obtain an entity feature sequence and a relation feature sequence; fusing the entity feature sequence and the relation feature sequence by adopting a causal inference module to generate a causal enhanced feature sequence; inputting the causal enhancement feature sequence into a joint decoder for entity boundary identification and relationship classification to obtain an entity relationship triple; according to the method, the task features are separated, confusion is eliminated through causal reasoning, the accuracy and robustness of entity relation extraction in a complex industrial text are remarkably improved, and particularly the effect in the aspects of processing nested entities and multi-task feature interaction is prominent.

The invention relates to the technical field of network data security management, and discloses an internet data security multi-dimensional evaluation and analysis system, which comprises a data acquisition and preprocessing module used for acquiring security data from a plurality of heterogeneous internet data sources and performing standardization and entity relationship extraction on the security data; the core asset and risk map construction module is used for constructing a multi-dimensional attribute graph model based on the extracted entities and relationships, nodes in the multi-dimensional attribute graph model represent the entities, edges in the multi-dimensional attribute graph model represent the interaction relationships between the entities, and the nodes and the edges carry attribute information; and the dynamic risk assessment engine is used for distributing dynamic weights for edges in the multi-dimensional attribute graph model. According to the invention, by constructing the core asset and risk map fusing multi-source data, the system breaks the information island of a traditional assessment tool, and associates isolated vulnerabilities, abnormal behaviors and attacked assets into an organic whole.

This invention relates to the field of knowledge graph technology, and discloses an automated entity relation extraction method and system for knowledge graph construction. The method includes: deep cleaning of heterogeneous data to obtain target text data; contextual structure parsing of the target text data to identify core entities and inter-entity event descriptions; latent constraint analysis of the core entities to obtain a set of deep relations; logical conflict determination between the explicit relation statement set and the deep relation set of the target text data to obtain a set of non-contradictory relations; semantic connection completion of the set of non-contradictory relations to construct a fused relation network; and association reinforcement reconstruction of the fused relation network to confirm the final entity relation set of the target text data. This invention can improve the efficiency of automated entity relation extraction in knowledge graph construction.

The invention discloses a threat alarm knowledge extraction method, relates to the technical field of the power industry, solves the problem of poor semantic understanding and entity relation extraction relation of threat alarm texts, and aims to identify to-be-verified texts existing in determined to-be-determined texts by performing feature verification on the determined to-be-determined texts so as to obtain the threat alarm knowledge. According to feature identifiers associated with different text data in a to-be-verified text, feature identifiers with records are locked, a to-be-verified identifier group is generated, feature verification is carried out on the to-be-verified identifier group, whether the to-be-verified text is an extracted text or not is recognized, and the to-be-verified text is extracted or not by adopting the step-by-step progressive recognition processing mode; according to the method, the interval texts existing in the corresponding texts are confirmed in sequence, so that more accurate confirmation of the extracted texts is completed, the optimal alarm knowledge extraction effect is achieved, and the knowledge extraction accuracy is improved.

The application discloses an entity relation extraction method, an entity relation learning model obtaining method and equipment. The method comprises the following steps: obtaining a target text and a target entity relation learning model, the target entity relation learning model being obtained based on a prototype feature set corresponding to a target entity relation set; calling the target entity relation learning model to obtain a text feature of the target text and a target prototype feature corresponding to each target entity relation; determining a matching degree of the target text and any target entity relation based on the text feature and the target prototype feature corresponding to the target entity relation; and determining an entity relation corresponding to the target text based on the matching degrees of the target text and the target entity relations. In this way, the prototype features in the prototype feature set can more comprehensively represent the entity relations, the target entity relation learning model obtained based on the prototype feature set has a good entity relation learning effect, and the accuracy of entity relation extraction by using the target entity relation learning model is relatively high.

Embodiments of the present application disclose an entity relation extraction method, device, electronic equipment and storage medium. The method comprises: obtaining at least one text information in a preset text library; determining an embedding vector corresponding to the text information; determining a vector pair corresponding to any two embedding vectors, and saving each vector pair to a label combination result; determining an entity relation corresponding to the text information according to the label combination result and a pre-trained classifier. In the embodiments of the present application, the pre-trained classifier is used to analyze the embedding vector of the text information, and the entity relation of the text information is extracted, so that the problems of entity nesting and entity pair overlap can be solved, the error can be reduced, the problem of poor generalization can be solved, and the accuracy of entity relation extraction is improved on the basis of ensuring the close correlation of each element in the triple extraction process.

This invention relates to a knowledge graph-driven intelligent molecular design method for oil displacement agents. It involves integrating multi-source heterogeneous data from experiments, literature, and oilfield sites. Through cleaning, standardization, and entity relationship extraction, a knowledge graph for oil displacement is constructed, deeply integrating molecular structure, functional group attributes, performance indicators, and reservoir environmental parameters. Using graph computing and graph neural network technologies, hidden association rules are mined from the graph, establishing an interpretable mapping model from molecular microstructure to macroscopic performance and environmental adaptability. Finally, based on the established mapping relationships, and with target reservoir conditions and performance requirements as multiple constraints, candidate molecular structural features are generated through reverse reasoning, achieving knowledge-guided precise molecular design. This invention constructs a knowledge graph for oil displacement, revealing the complex intrinsic relationships between molecular functional groups, reservoir environment, and oil displacement performance. Through the combination of knowledge graphs and intelligent algorithms, it achieves intelligent and automated molecular design of oil displacement agents.

The present application provides an entity relationship extraction method, device, equipment, medium and program product, the method comprises: obtaining a to-be-recognized sentence; inputting the to-be-recognized sentence into an entity fragment identification module in an entity relationship extraction model to obtain an entity fragment arrangement of the to-be-recognized sentence; inputting a fragment arrangement vector corresponding to the entity fragment arrangement into a relationship matrix generation module in the entity relationship extraction model to obtain a fragment arrangement matrix representing the relationship between all entity fragments; inputting the fragment arrangement matrix into a relationship prediction module in the entity relationship extraction model to obtain a relationship prediction result corresponding to each entity fragment. The present application is used to solve the defect of unstable entity relationship extraction result in the prior art, and realizes higher stability of the entity relationship extraction model.

This invention presents an AI-generated text detection method based on graph structure features, belonging to the fields of artificial intelligence and natural language processing. The method includes: dataset construction, entity relation extraction and graph structure construction, graph structure feature extraction, graph structure feature model training, and text detection. It further incorporates traditional text feature extraction and model training, adaptively fusing the traditional text feature model and the graph feature model based on confidence-weighted entropy, and then performing text detection based on the fused model. This invention is the first to perform AI text detection from the perspective of graph structure features, breaking through the limitations of existing research that focuses on surface features such as vocabulary, syntax, and perplexity. The fusion strategy dynamically adjusts the fusion weights by quantifying the uncertainty of model predictions, maintaining a high level of performance on both original data and adversarial examples, achieving a balance between detection accuracy and adversarial robustness. It can be widely applied to the detection of AI-generated content such as news content and academic papers.

This invention discloses a method, system, device, and storage medium for joint extraction of Chinese entity relations. The method includes acquiring and preprocessing data from tourism websites to obtain a dataset; performing text embedding and entity span representation on each text in the dataset; performing entity recognition and establishing an entity type matrix; extracting grammatical information; fusing the entity span representation and the entity span representation with grammatical information to obtain a new entity span representation; performing pooling and other operations on the non-entity parts of the entity span representation to obtain local text information representation; and concatenating the new entity span representation, entity type matrix, entity width representation, and local text information representation into a relation classifier to obtain relation classification results. By supplementing entity information with entity type information and the grammatical information of the corresponding input text, an entity relation extraction method for Chinese tourism text data is established, improving the accuracy of entity relation extraction.

The application discloses a power industry entity relation extraction method, device, equipment and medium, and the method comprises the following steps: acquiring a power industry text dataset and preprocessing, based on a power industry entity query template, extracting power industry entities by using a generative pre-training language model, and constructing a labeled entity dataset; the context features of the power industry text dataset are extracted and sequence labeling is performed by using an ELMO model and a Transformer-CRF model, and a global dependency relationship is obtained by modeling; based on the global dependency relationship, the context features of the text data to be labeled are extracted and sequence labeling is performed, an entity label sequence is obtained, the continuous and same entity labels in the entity label sequence are merged, and a plurality of entities are determined; based on the entity relation query template, the relation information is extracted by using the generative pre-training language model, and the relation instances are constructed according to the preset matching rule. The application can improve the accuracy and coverage of power industry entity relation extraction.