530 results about "Entity type" patented technology

The invention relates to the technical field of artificial intelligence, in particular to a knowledge graph construction method and system based on a large model technology, and the method comprises the following steps: carrying out large model enhanced data annotation; performing entity extraction, relationship extraction, event extraction and causal relationship extraction by utilizing the large model; performing entity type extraction, relation type extraction, event type extraction and knowledge system extraction by utilizing the large model; the knowledge graph data is complemented by using a large model, so that the knowledge complementing capability is improved; performing relation reasoning and event reasoning by using the large model; the knowledge graph construction method and system based on the large model technology have the beneficial effects that the large model masters strong semantic understanding and context reasoning capabilities through deep learning and wide corpus training. Therefore, the knowledge graph can more accurately analyze the complex semantic relationship in the text and identify important entities and relationships hidden in the text, so that the information is effectively classified and integrated.

The invention provides a supply chain sales anomaly detection and root cause analysis system and method fused with a knowledge graph, and the system comprises a demand collection and preprocessing module which is used for connecting an order system, a supply chain system, a customer relationship management system and an external data source, and completing the data cleaning, entity analysis and feature extraction; the supply chain knowledge graph construction module is used for defining an entity type and a relationship type; the real-time anomaly detection module is used for accessing a sales index data stream, performing anomaly detection in combination with lightweight filtering and a graph neural network model, and calculating node and global anomaly scores; and the visual report generation module is used for automatically generating a visual report. According to the method, the dynamic supply chain knowledge graph is constructed, the graph neural network is applied, multi-source heterogeneous data is deeply fused, the complex dependency relationship between entities is effectively captured, the accuracy and timeliness of sales anomaly detection are remarkably improved, automatic positioning of abnormal root causes and evidence chain tracing are achieved, and the analysis efficiency is greatly improved.

Disclosed in the present application are a knowledge graph construction method and apparatus, and a storage medium, an electronic device and a computer program product. The method comprises: acquiring first natural language text; using an extraction model to perform entity extraction on the first natural language text, so as to obtain a first entity and a first entity relationship, and determining a corresponding first entity type and first relationship type; using a semantic encoder to determine a first semantic vector and a second semantic vector respectively corresponding to the first entity type and the first relationship type; on the basis of calculated first distances between the first semantic vector and cluster centers of a plurality of entity types and calculated second distances between the second semantic vector and cluster centers of a plurality of relationship types, determining a target entity type for the first entity type and a target relationship type for the first relationship type; and on the basis of the first entity, the first entity relationship, the target entity type and the target relationship type, constructing a target knowledge graph.

A system and method for persistent cognitive computation with temporally synchronized multimodal processing implements a geometric approach to artificial intelligence through typed latent entities within a dynamic manifold substrate. The system maintains a latent manifold incorporating heterogeneous data modalities where local curvature reflects semantic density and typed entities are stratified according to structural properties. Temporal synchronization coordinates asynchronous multimodal data streams through generation of temporal alignment fields within the manifold that preserve semantic coherence across modal boundaries. Type-aware geometric operations enforce operation legality based on entity type and local manifold geometry, enabling structured recombination, compression, and traversal while preventing semantic distortion. The system executes synchronized manifold reorganization during idle periods through coordinated optimization operations including perturbation analysis and topological surgery. This architecture enables persistent memory through geometric encoding where frequently accessed concepts develop high-curvature regions and cognitive patterns emerge from usage-based manifold evolution.

The invention discloses a large language model retrieval enhancement generation method oriented to cyberspace security emergency intelligent analysis. The retrieval and generation capability of a large language model in knowledge questions and answers in professional fields is enhanced through a knowledge graph. The method comprises a knowledge base construction module and a knowledge enhancement response generation module. The method mainly comprises the following steps: collecting multi-source data and preprocessing to construct a basic data set; defining an entity type and a relationship type, and performing entity recognition and relationship extraction based on a large language model to construct a knowledge graph; designing a graph-based hierarchical retrieval strategy to perform knowledge retrieval; and generating professional answers through a knowledge enhancement generation framework. According to the method, the accuracy of knowledge questions and answers in the professional field is remarkably improved, the accuracy of the professional field in experimental evaluation reaches 86.7%, the coverage rate of retrieval knowledge reaches 81.2%, and the interpretation quality score is 4.35 score. According to the method, the limitation of a traditional large language model in professional field application is overcome, and good expandability and adaptability are achieved.

The invention belongs to the technical field of natural language processing and multi-modal information extraction, and particularly relates to a multi-modal named entity recognition method based on semantic alignment and cross-modal graph fusion, which comprises the following steps: S1, acquiring a data sample containing a text sequence and image content; s2, encoding the text and the image into vectors respectively; s3, similarity is calculated through a trainable bilinear function, and optimization is carried out through loss comparison; s4, cross-modal attention is used to enhance association information between modals; s5, determining the proportion of reserved image information through a modal matching module; s6, introducing a gating mechanism to dynamically fuse visual and text features; s7, realizing local and global information complementation by a cross-modal graph fusion model; and S8, inputting the fused representation into the CRF layer to predict the entity type. According to the method, fine semantic alignment can be realized in a weak image-text correlation context, and balance between local entity recognition and global semantic understanding can be achieved.

The invention relates to an unmanned aerial vehicle fault traceability analysis method and device, equipment and a storage medium. The method comprises the steps of defining entity types and relationship types among entities based on a predefined fault ontology model to construct a mode layer of an unmanned aerial vehicle fault knowledge graph; based on the mode layer, extracting a fault triple from the multi-source operation data of the unmanned aerial vehicle by using a mixed extraction model, and constructing a fault knowledge graph containing instance data; endowing a dynamic weight probability representing confidence to a relation edge in the fault knowledge graph, and generating a probabilistic fault knowledge graph; and mapping to-be-analyzed fault information to the probabilistic fault knowledge graph, performing traceability analysis by using a hybrid inference engine, and outputting a fault reason and a transmission path. According to the method, structured deep fusion of domain knowledge and data value is realized, and the traceability conclusion is improved from qualitative judgment to quantitative decision support with confidence measurement.

The invention relates to a dynamic ontology construction method based on a large model, and belongs to the field of artificial intelligence, large models and knowledge maps. According to the method, expert knowledge, historical experience, a database and industrial standard data are comprehensively utilized, entity types, attributes and relationships are defined, and an initial ontology O1 is constructed; by means of rich knowledge contained in a large model, extension from a large class to a subclass is achieved, on this basis, extraction of subclass knowledge is further achieved through a large model technology, and an ontology O2 is formed; for structured, semi-structured and non-structured data updated in real time in the business data, forming an ontology set O3 based on small-sample prompt learning of a large model; the merging and updating of the domain ontology are realized through a Function Calling technology based on LLM (Logical Language Model); a function is defined through word similarity calculation based on a'known network ', mathematical calculation is carried out on a large model, merging and updating of domain ontologies are achieved, and an ontology set O4 is formed. According to the invention, the quality of ontology construction is improved.

The invention relates to a Cypher query statement generation optimization method, device and system based on a large language model. The method comprises the steps of performing intention recognition on an original question proposed by a user in combination with a large language model; according to a result after intention recognition, key entity types are extracted by using a large language model, and possible relation links are preliminarily screened out by combining the key entity types with all relation links obtained through calculation of a graph database Schema; performing semantic analysis on the preliminarily screened relationship links by utilizing a large language model, and screening out N relationship links which most accord with a result after intention recognition from a plurality of possible relationship links; and according to the N relation links and a result after intention recognition, generating a corresponding Cypher query statement by using a large language model, and optimizing the generated Cypher query statement by using the large language model. According to the method, the big language model is combined, so that the generation accuracy and the query efficiency of the Cypher query statement are improved, and the friendliness of interaction between the user and the graph database is enhanced.

Generating responses to queries with text extracts from unstructured data using AI models includes (i) extracting text from unstructured data sources to create machine-searchable documents, (ii) replacing PII and PHI with entity types and attributes, (iii) determining text extracts that indicate criteria, (iv) using a small-scale ML model to perform text searches and find conceptually associated text strings, (v) generating a custom context for a large language model (LLM), (vi) prompting the LLM to generate a response, (vii) combining the response with an extractive QA model to obtain relevant text extracts as response basis, and (viii) providing system-generated recommendations for next best actions based on responses that produce the most optimal outcomes in historical input documents for manually selected or automatically recommended resolution paths.

The invention discloses a multi-level construction and intelligent recall strategy implementation method, system and device for an energy policy knowledge graph and a medium, and belongs to the technical field of energy policy monitoring, and the method comprises the steps: obtaining energy policy text data, carrying out energy policy correlation analysis, and extracting entity information, relation clues and text vector representation from an energy policy text; carrying out knowledge graph construction by utilizing the extracted entity information, relation clues and vector representation, and generating a knowledge graph structure comprising an entity type, an entity attribute, a relation type and confidence; updating the knowledge graph according to the operations of newly adding, revising and revoking the policy document; and executing a multi-path recall strategy including longitudinal traceability recall and transverse comparison recall by using the knowledge graph structure, and generating a relevance recall result related to the target policy. According to the invention, efficient traceability, accurate comparison and full-link intelligent management of energy policies are realized, and the policy retrieval efficiency and accuracy are remarkably improved.

The present invention relates to the technical field of wireless network optimization operations and maintenance, and provides a two-stage optimized wireless network optimization field long entity recognition method and system. The method comprises: using a pretrained long entity recognition model to process acquired text content to be recognized to obtain a long entity recognition result; by means of a first-stage predecessor task, acquiring a pretrained model TelBert having domain knowledge; and in a second stage, introducing semantic information related to an entity to obtain a machine reading comprehension framework-based long entity recognition model, and decoding the entity by means of a dual-pointer network. According to the present invention, knowledge in a specific field is learned by adding an entity type prediction task, the text representation learning capability of a base model is enhanced, and the difficulty of model tuning in a few-shot scenario is alleviated; the entity recognition model is improved to obtain an MRC-LER model suitable for document-level long entity recognition; and a semantic similarity-based evaluation index is proposed, and the effective extraction rate of entity key information is reasonably evaluated.

Methods and systems described herein are directed to automatedly generating entity-specific notifications of a vehicle accident occurrence for entities. In response to a notification system receiving accident data, the system can determine characteristics for the accident occurrence. Using these characteristics, the system can then determine parameters for the notifications defining types of entities which ought to be recipients in accordance with the characteristics. The system can use these parameters to locate specific entities that match the parameters. Having determined the entities, the system can issue notifications to the entities, using templates specific to the entities and with accident characteristics added.

The invention provides a large model named entity recognition method based on representative sample selection and context enhancement, which comprises a representative sample selection module, an entity knowledge construction module, a dynamic context selection module, a large model calling module and an iterative feedback optimization module, according to representative sample selection, samples with representativeness and information diversity are automatically selected from unlabeled data for labeling through a sample screening strategy based on clustering, entity description integration aims at each entity type, a plurality of high-quality instances are extracted from labeled samples, and standardized entity definition or description prompts are constructed. According to the dynamic context selection, for to-be-recognized text content, a context example most relevant to a target text is dynamically selected from a historical annotation sample or a description set through a semantic similarity retrieval mechanism to serve as auxiliary prompt input, and the adaptability and generalization ability of LLM in a complex or variable scene are improved.

The invention discloses an aircraft maintenance knowledge management and intelligent recommendation method based on a knowledge graph, and the method comprises the following steps: carrying out the multi-source collection of aircraft maintenance data, and carrying out the classification and coding of the maintenance data, extracting an entity and a type thereof by using a domain self-adaptive entity recognition model according to a splicing vector spliced by the classified coding data; constructing an entity co-occurrence graph according to the entities, performing relation extraction, attribute extraction and logic generation according to the entity co-occurrence graph, and constructing a knowledge graph according to the entities, the entity types, the entity relations, the entity attributes and the entity logic; aircraft fault problems, environmental conditions and maintenance preferences are manually input, and an aircraft fault maintenance scheme is intelligently recommended according to manual input and the knowledge graph; and carrying out resource conflict detection according to the recommended aircraft fault maintenance scheme, and regularly updating the knowledge graph. According to the intelligent maintenance recommendation method provided by the invention, the knowledge graph composed of multi-source maintenance data and multi-factor training are provided, and the accuracy of intelligent recommendation is improved.

A system includes a set of crawlers that find and retrieve documents from an information network, an information extraction system, a knowledge graph storing nodes and edges that connect them, wherein each node represents a respective entity of a corresponding entity type of a plurality of entity types, and wherein the knowledge graph further stores event data relating to events detected by the information extraction system, a machine learning system that trains models that are used in connection with at least one of entity extraction, event extraction, recipient identification, and content generation, a lead scoring system that scores the relevance of information to an individual and references information in the knowledge graph, and a content generation system that generates content of a personalized message to a recipient who is an individual for which the lead scoring system has determined a threshold level of relevance.

The invention discloses a conflict resolution method for an open avionics architecture. The method comprises the following steps: constructing an avionics field standardized ontology model, an entity type, a semantic relationship and a constraint rule; the entity types comprise a standard type, a resource type, an interface type and a security type; the semantic relationship comprises inclusion, inheritance, compatibility, dependence, conflict, constraint and version evolution; the constraint rule comprises version consistency, function interoperability and performance index constraint; building a multi-standard library based on the knowledge graph; and through a conflict detection and resolution mechanism, standard conflicts possibly occurring in the open avionics architecture design process are identified and eliminated. According to the method, unified modeling, multi-standard library construction and semantic level conflict detection and resolution of the multi-source heterogeneous avionics standard are realized.

The invention discloses a knowledge graph-based search engine relation chain recommendation method, which comprises the following steps of: receiving a relation chain recommendation request submitted by a user through a search engine to obtain initial query information; performing semantic analysis and intention recognition on the initial query information, and determining a corresponding entity type and a relationship type; calling a dynamic knowledge selection and evaluation mechanism according to the entity type and the relationship type, and dynamically screening the original knowledge graph to obtain effective knowledge sub-graphs; performing diffusion type reasoning based on a knowledge graph diffusion model to generate a candidate relation chain path set; evaluating the quality score of each candidate path in real time; and performing dynamic screening according to the path quality score, and outputting a relation chain path recommendation result. According to the method, accurate mining and dynamic optimization of the knowledge graph relation chain path are realized, and the accuracy of a recommendation result and the flexibility of user interaction are improved.

Disclosed are machine learning techniques directed to training a machine learning model for the combined learning of multiple natural language processing (NLP) tasks. The NLP tasks may be named entity recognition (NER), relation extraction (RE), and assertion detection (AD) tasks. The machine learning model may be a multi-layer transformer model. Training the machine learning model may involve first training the NER module on the NER task, and thereafter training the RE module on the RE task while the AD module is simultaneously trained on the AD task. Training the machine learning model may alternatively involve training the NER module on the NER task concurrently with training the RE module on the RE task and training the AD module on the AD task. The trained machine learning model can predict entities and entity types in newly provided text, along with relations between the entities and assertions associated with the entities.

The embodiment of the invention provides an abnormal working condition data analysis and early warning method and system for a drying machine, and the method comprises the steps: firstly constructing a diagnosis knowledge graph of the abnormal working condition of the drying machine, which comprises entity types of drying machine parts, operation parameters, abnormal working condition types, environmental factors and the like; then, working condition parameter data collected by a drying machine in real time is subjected to correlation mapping with the working condition parameter data, knowledge graph instantiation data are generated, multi-hop correlation reasoning is carried out according to a reasoning rule set in the diagnosis knowledge graph, and the diagnosis knowledge graph instantiation data is obtained; and constructing an abnormal association path set containing entity nodes, relation edges and attribute values, screening out a target abnormal association path of which the comprehensive confidence exceeds a preset threshold value from the abnormal association path set, generating dryer abnormal working condition early warning information according to information contained in the target abnormal association path, and sending the dryer abnormal working condition early warning information to a dryer monitoring terminal. Therefore, accurate analysis and timely early warning of the abnormal working condition of the drying machine are realized.

The invention relates to the technical field of agriculture, in particular to a large-model-driven agricultural knowledge graph analysis method and system, and aims to realize standardized processing of multi-source heterogeneous data through a three-stage preprocessing process and combine with a field adaptive large-model training technology so as to realize the large-model-driven agricultural knowledge graph analysis method and the large-model-driven agricultural knowledge graph analysis method and the large-model-driven agricultural knowledge graph analysis system. A special system containing 2000 + entity types of crops / diseases / farming operation and the like is constructed. In the entity extraction link, the large model zero sample learning ability is utilized, novel agricultural entities can be automatically recognized, the entity recognition accuracy is improved by 35% compared with a traditional method, and particularly in cross-modal alignment of pest and disease damage images and text description, feature vector Euclidean distance minimization is achieved through a ResNet50-BERT fusion model, and the alignment precision reaches 92% or above. The dynamic updating mechanism captures three core periodicals and policy documents in real time on the basis of web crawlers, the monthly updating frequency of the knowledge graph is improved to four times in combination with an incremental updating algorithm, the timeliness and integrity of agricultural knowledge are ensured, and technical guarantee is provided for precise agricultural data management.

The invention provides an electric power document keyword extraction method based on Prompt and a knowledge graph, relates to the technical field of electric power document processing, and constructs a lightweight multi-level index knowledge graph in the electric power field by combining entity type and relation type division based on an electric power industry standard document and an electric power field corpus. The method comprises the following steps: performing vector modeling on a power document, constructing a multi-level index from an entity to a vector, realizing standardized semantic modeling and efficient hybrid retrieval of a power document field background, and obtaining a topic vector and a core paragraph of the power document in combination with power key information; according to the method, entity types are indexed in a knowledge graph by using subject vectors, similar entities are obtained to form knowledge sub-graphs, so that multilayer Prompt is obtained to guide a large language model to extract keywords, then knowledge graph similarity constraints are introduced to decode the output of the large language model, the keyword recognition capability in the power field is improved, and the keyword recognition efficiency is improved. And the accuracy of keyword type identification and the normalization of term naming are both considered.

The invention discloses a CAD automatic multi-label generation method and system. The method comprises the following steps: constructing an extension protocol library, and registering extension protocols for various entities; the extension protocol comprises at least one key feature needing to be labeled by an entity and a rule for labeling the key feature; the rule comprises a labeling style and a labeling position; the entity type is obtained based on user selection, and a corresponding extension protocol is called from an extension protocol library so as to obtain key features, needing to be labeled, of the entity; and obtaining annotation data corresponding to the key features, needing to be annotated, of the entity, and generating annotations for the entity according to the annotation data and the annotation rules. According to the method, the entity type can be automatically obtained, the annotation is generated according to the rule, manual annotation setting on each key feature one by one is not needed, the annotation time is greatly saved, and the drawing efficiency is improved.

The invention relates to the field of natural language processing, and provides an entity relation joint extraction model based on a heterogeneous graph neural network and a construction method. A conditional attention mechanism is adopted to introduce predefined relationship type information in an original text sequence as priori knowledge into a dynamic feature word vector of the original text sequence fused with context semantic information on word granularity to obtain a feature word vector fused with the context semantic information and a relationship type; when the heterogeneous graph is constructed based on the entity types, virtual relation edges are added between the entity pairs existing in the heterogeneous graph, the relation between the two connected entities is expressed through the virtual relation edges, and combined modeling of the entities and the relation is achieved; context dependency between entities and relationships and relationship interaction among multiple entities can be better captured through joint modeling, the complexity of actual relationships can be better restored through joint modeling of the relationships, and the consistency and accuracy of extracting the overlapping relationships among the entities are more efficiently improved.

Techniques are provided for computing with private healthcare data. The techniques include a de-identification method including receiving a text sequence; providing the text sequence to a plurality of entity tagging models, each of the plurality of entity tagging models being trained to tag one or more portions of the text sequence having a corresponding entity type; tagging one or more entities in the text sequence using the plurality of entity tagging models; and obfuscating each entity among the one or more tagged entities by replacing the entity with a surrogate, the surrogate being selected based on one or more attributes of the entity and maintaining characteristics similar to the entity being replaced.

Techniques are disclosed for prompt-based few-shot entity extraction. The techniques include obtaining an annotated natural language document set for an arbitrary new entity type. A prompt sequence set is generated based on the annotated document set. A pre-trained entity extraction model is trained based on the prompt sequence set to yield a few-shot trained entity extraction model trained to extract at least the arbitrary new entity type. In response to obtaining a test document set, one or more entities of the arbitrary new entity type are extracted from the test document set using the few-shot trained entity extraction model.

A machine learning service implements programmatic interfaces for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. A first request to perform an operation on an instance of a particular entity type is received, and a first job corresponding to the requested operation is inserted in a job queue. Prior to the completion of the first job, a second request to perform another operation is received, where the second operation depends on a result of the operation represented by the first job. A second job, indicating a dependency on the first job, is stored in the job queue. The second job is initiated when the first job completes.

The invention relates to a domain-oriented knowledge graph construction method and device, and the method comprises the steps: obtaining the related text data of a bid invitation file, carrying out the filtering of stop words, and constructing a text data sample set for training; constructing an entity type and a type guide vector by utilizing priori knowledge and a text data sample set, and generating a prompt engineering structure; performing domain self-learning training on the first preset language model to grasp bid invitation file auditing domain knowledge; performing comparative learning training on the second preset language model to identify a related entity; through entity identification and a thinking chain strategy, entity relationship prediction training is performed on the first language model to identify the relationship between entities, through the above mode, the accuracy of entity identification and information extraction is improved, the compliance and the high efficiency of bid invitation auditing work are ensured, and thus a more perfect knowledge graph is constructed.

The invention discloses a structured data storage method based on natural language transformation, which comprises the following steps of: a system initialization configuration stage: deploying a protocol adapter in a local memory of a PC (Personal Computer) client, and loading natural language processing pipeline configuration parameters; a heterogeneous data acquisition stage: capturing a multi-source text data stream through the protocol adapter, uniformly converting the multi-source text data stream into a standardized data packet, and sending the standardized data packet to a message queue theme; a text cleaning stage: a named entity recognition stage: inputting the pure text data into an NER module deployed with a language model loader; in the conditional feature extraction stage, feature vectors are generated for texts meeting preset conditions on the basis of entity type tags in the entity recognition result; and a consistent storage stage: inserting the entity identification results into a relational database in batches, and updating the entity mapping relationship in the cache. According to the method, the intelligent level of cache management is remarkably improved, and the access fluency of the key data of the user is guaranteed.

Systems, devices, and techniques are disclosed for efficient knowledge graph indexing and retrieval. Document chunks may be generated from documents. Summarizations may be generated from document chunks. Entity types, entity properties, relations, and relation properties may be generated from a subset of the summarizations. A schema including entity types, entity properties, relations, and relation properties may be generated. Entity property triplets and entity relation triplets may be generated from the summarizations based on the schema and linked to the document chunks. A knowledge graph including nodes representing entities from the entity property triplets and entity relation triplets and edges representing the entity property triplets and the entity relation triplets may be generated. A search query may be received. Nodes and edges of the knowledge graph that include the entities, the entity property triplets and the entity relation triplets most similar to keywords of the search query may be determined.