54 results about "Entity relation extraction" patented technology

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.

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 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.

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.

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.

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.

This disclosure relates to a training method, apparatus, and readable storage medium for an entity relation extraction model. The method includes: acquiring multiple sample sentences; performing prediction triplet extraction processing on each sample sentence to obtain prediction triplets for the multiple sample sentences; the prediction triplet extraction processing includes: filling in labels on the table obtained after tabulating the sample sentences; extracting label features of the sample sentences based on the labeled table; obtaining prediction triplets for the sample sentences based on the label features; the prediction triplets contain the predicted entities of the sample sentences and the relationships between the entities; obtaining a loss function for a pre-trained model based on the prediction triplets and actual triplets of the multiple sample sentences; and training the pre-trained model based on the loss function to obtain an entity relation extraction model. The entity relation extraction model based on table-filled labels has higher accuracy and recall compared to traditional methods when extracting entity relations.

The application provides an editable layered medical SVG generation method, system, device and medium, and belongs to the technical field of medical image processing; the method comprises the following steps: receiving natural language input and performing entity relationship extraction to obtain structured medical entities and relationships; based on the extraction result, searching in a preset medical knowledge graph to obtain relevant knowledge triples and visual attributes; accordingly, constructing an enhanced prompt and inputting a large language model to generate SVG code containing a layered structure and each main graphical element being bound with a unique entity identifier; finally, parsing the SVG code and establishing a mapping index between medical entities and graphical elements. Through the introduction of a medical knowledge graph for professional knowledge injection, and in combination with an entity-element mapping index mechanism, the application realizes end-to-end generation of structured and editable medical SVG from natural language, and significantly improves the accuracy, interactivity and editing flexibility of medical visualization.

The application 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 comprises the following steps: obtaining text blocks based on medical document preprocessing and block division, and constructing an abstract tree; performing entity relation extraction based on the text blocks, and constructing a knowledge graph; constructing a mapping relationship between the abstract tree and the knowledge graph, and obtaining a vector database; performing entity extraction and calculating query correlation based on user query, judging the retrieval type of the user query, and obtaining corresponding retrieval results from the vector database according to the retrieval type; and inputting the retrieval results into a large language model to generate results. In view of the deficiency of retrieval enhancement generation in the medical field in generating intelligent question and answer quality, the abstract tree and the knowledge graph are constructed, a double-layer index of the abstract tree-knowledge graph is formed, dynamic typing is performed according to the query correlation, the depth and the breadth can be considered at one time, and therefore the accuracy and the efficiency of the medical question and answer are improved.

This invention discloses a method for constructing a knowledge graph in the field of carbon reduction in building materials, involving knowledge graph construction technology and the field of carbon emission management in building materials. The method includes: Step 1, entity concept design; Step 2, corpus collection and preprocessing…Step 6, knowledge fusion; and Step 7, knowledge storage. This invention is the first to construct a knowledge graph for the field of carbon reduction in building materials, filling a gap in this field and providing systematic knowledge support for carbon reduction decisions in the building materials industry. A domain ontology covering the core concepts of carbon reduction in building materials is designed, including 8 entity types and 12 relation types, which can comprehensively express the knowledge structure and semantic relationships in the field of carbon reduction in building materials. A deep learning method based on a BERT pre-trained model and a CasRel joint extraction architecture is used for entity relation extraction, which can effectively solve the problems of entity overlap and multiple relation extraction, improving the accuracy and completeness of knowledge extraction.

This invention discloses a joint entity relation extraction method based on a dual-query network. Unlike previous works that treat entity relation extraction as a multi-turn question-answering task, this invention uses two sets of queries for extraction, where each entity query extracts one entity and each relation query retrieves one relation. Since these learnable queries are type-independent, this invention avoids manually constructing natural queries for each relation category, which is inefficient and labor-intensive. To obtain the final relation triples, this invention designs relation pointers to associate the results of the two sets of queries. Furthermore, through an attention mechanism, this invention can naturally model the interdependencies between the two tasks, which is difficult to achieve in span-based classification methods.

The invention discloses a knowledge graph increment construction method based on large-scale language model entity enhancement, which comprises the following steps of: performing entity relationship extraction from a newly added unstructured document by using a first large-scale language model to obtain a plurality of candidate triples; for candidate entities in each candidate triple, extracting context information of the candidate entities in the original document, and performing semantic enhancement by using a second large language model to generate semantic portraits of the candidate entities; retrieving a plurality of candidate matching entities in the basic knowledge graph based on context semantic vectors in the semantic portraits; for each candidate matching entity, calculating a fusion score with the candidate entity; determining an optimal matching entity from the candidate matching entities, and if the fusion score exceeds a preset threshold value, determining that the candidate entities and the optimal matching entity are the same entity; replacing a candidate entity in the candidate triad with an optimal matching entity, and adding the updated triad into the basic knowledge graph; according to the invention, data redundancy and logic conflicts are avoided.

This invention discloses an entity relation extraction method based on multi-head attention. The steps are as follows: 1) Obtain the contextual representation of the input sentence using Bi-LSTM; 2) Obtain the global feature vector and features of different subspaces of the sentence using a multi-head attention mechanism; 3) Generate corresponding hidden forests for the different subspaces using the matrix tree theorem; 4) Encode the global feature vector and hidden forests separately using GCN; 5) Obtain the final representations of entity and sentence vectors through pooling; 6) Fuse the global feature vector and the global feature vector calculated by convolution of the hidden forest; 7) Finally, output the results through a fully connected layer in a classifier to identify the relationship types between entities. This invention was tested on three datasets: Semeval2010task8, CPR, and PGR. The results show that this method performs excellently in entity relation extraction tasks.

This invention relates to the field of natural language processing, disclosing a method, apparatus, electronic device, and storage medium for relation extraction based on model training. The method includes: inserting preset characters before and after entity pairs in training text and text to be extracted, obtaining target training text and target text to be extracted; encoding entity pairs in the target training text using a preset model, and concatenating the encoded vectors to obtain entity pair concatenation vectors; calculating the loss value of the entity pair concatenation vectors; adjusting the parameters of the preset model according to the loss value until the loss value meets a preset loss threshold, obtaining a trained preset model; using the trained preset model to extract target entity concatenation vectors from the target text to be extracted; calculating the similarity between the target entity concatenation vectors and preset sample representation vectors, and using the entity relation of the preset sample representation vector with the highest similarity as the entity relation of the text to be extracted. This invention can improve the efficiency of triple entity relation extraction.

The application discloses an entity relation extraction method, device and equipment and a storage medium thereof. The method comprises the following steps: performing entity processing on a to-be-processed text to obtain an entity sequence, wherein the entity sequence comprises a plurality of candidate entity pairs; performing feature extraction on the to-be-processed text according to a feature item contained in the to-be-processed text to obtain a text feature vector; performing feature extraction on the candidate entity pairs and candidate relations between the candidate entity pairs to obtain a knowledge feature vector; performing fusion processing on the text feature vector and the knowledge feature vector to obtain a text knowledge fusion feature; and performing classification processing on the text knowledge fusion feature to obtain a corresponding relation of each candidate entity pair. The technical scheme provided by the embodiment of the application can obtain multiple dimensions of features of the to-be-processed text to improve the accuracy of entity relation extraction.

The application discloses a radar-oriented entity relationship joint extraction method, first, Chinese word segmentation and vectorization processing are performed on radar text corpus, then the number of entities in the radar text corpus and the start / stop position of a single entity are acquired based on the word vector, and the entity vector of each entity is calculated; then, the relationship vector between any two entities and the relationship vector based on the attention weight are calculated, the two relationship vectors are spliced and input into a full connection neural network, and finally, the entity relationship is extracted through the full connection neural network, so that the entity relationship about the radar feature is quickly extracted from the radar field sample corpus.

This invention discloses a method, apparatus, electronic device, and storage medium for constructing a knowledge graph for intelligent decision-making, belonging to the field of industrial intelligent technology. The knowledge graph construction method of this invention includes: inputting text data; using a BERT-BiLSTM-CRF entity recognition model that integrates word segmentation features to obtain sequence labels for the input text; using an R-BertTransformer entity relation extraction model to extract domain text event relations; using a BERT-BiLSTM-CRF sequence labeling model to extract event trigger words, followed by a rule-based method to extract event core words, and finally a rule-based method to extract text numerical knowledge; and using a deep learning-based event relation extraction model to extract the logical relationships between events. Using the knowledge graph construction method of this invention, the event extraction accuracy can reach over 80%, and the logical relationship recognition accuracy F1 value can reach up to 0.75, meeting the needs of practical applications.

This invention relates to an entity relation extraction method based on syntactic information and attention mechanisms. It employs a vector transformation unit to convert each word in a sentence, and the relative position and syntactic relation of each word with two specified entities, into vectors. A gated encoding unit performs feature extraction and nonlinear processing on the relative position vector and syntactic relation vector to obtain entity association feature gate vectors. A sentence encoding unit performs gated feature extraction and pooling processing on the entity association feature gate vectors and the word vectors to obtain the final sentence vector. A relation extraction unit calculates the distance between the final sentence vector and the classification prototype center in the prototype network to obtain the relation between two specific entities in the sentence. The entity relation extraction method of this invention solves the problem in existing research where insufficient utilization of sentence entity position and syntactic information leads to ineffective filtering of confusing information. This method can accurately distinguish the relations between specified entities using only a small number of annotated instances.

The application provides a document-level entity relation extraction method based on multi-scale and non-bridging reasoning. The method realizes fine-grained and adaptive document-level relation extraction by constructing a multi-scale semantic space and a cross-axis attention mechanism. First, a pre-trained language model is used to encode the document to generate semantic representations of entity mentions and entities. Then, a multi-scale reasoning module is used to construct multi-granularity relation semantic representations through mention-to-mention, entity-to-mention and entity-to-entity. Next, the representations are enriched through context enhancement and axis attention mechanism. Then, the dependency relationship between bridging and non-bridging elements is fused through cross-axis multi-head attention. Finally, the model is optimized through a classification module and loss calculation.

The application provides an entity relation extraction method and device based on mass data, comprising: performing named entity recognition based on the target sentence to obtain a named entity sentence; inputting the named entity sentence into a trained relation extraction model to obtain an entity category relation corresponding to each named entity sentence; wherein the trained relation extraction model is obtained by inputting a named entity sentence sample carrying an entity category label into a generated discriminator combination model for training, and the generated discriminator combination model comprises a preset relation extraction model and a preset discriminator. Through the discriminator combination model comprising the preset relation extraction model and the preset discriminator, the idea of reward and punishment and strategy is applied to the training of the remote supervision relation extraction. Thus, the relation extraction model capable of effectively realizing the entity relation extraction is trained, and the entity relation extraction can be effectively realized through the model.