166 results about "Entity linking" patented technology

The invention relates to the technical field of artificial intelligence, and discloses an education data report content interaction method and system generated based on retrieval enhancement, and the method comprises the steps: judging whether a natural language problem is an education field problem or not through a large language model, and if yes, carrying out semantic analysis to generate a structured query instruction; when the problem relates to cross-document association analysis, retrieving the structured semantic index database to generate a retrieval result set; if policy association analysis is involved, matching a policy knowledge graph by combining semantic similarity calculation and an entity linking technology, and then performing cross-modal fusion processing to obtain a retrieval result set; and inputting the retrieval result set into the retrieval enhancement generation model, and calling an education field language model to generate an analysis report. According to the method, the industrial pain points of inaccurate intention recognition, low cross-document analysis efficiency, incapability of dynamically combining with latest policies and the like in a traditional interaction mode can be solved, the efficiency and quality of data report interaction in the education field are remarkably improved, and the user interaction experience is optimized.

The invention discloses a knowledge graph generation method and system based on a large model, and the method comprises the steps: carrying out the preprocessing of data, obtaining a data set, and determining a target mode of a knowledge graph; extracting a triple conforming to the type of the target mode from the data set to obtain a candidate knowledge triple; carrying out multi-dimensional verification on the candidate knowledge triad to generate a confidence score; performing consistency verification and conflict resolution on the candidate triad set with the confidence score higher than a preset threshold value to obtain a verification candidate knowledge triad; performing entity linking and relationship standardization processing on the verification candidate knowledge triples, and mapping the verification candidate knowledge triples to a knowledge graph of a corresponding target mode to obtain standard candidate knowledge triples; and fusing the standard candidate knowledge triples into the knowledge graph database, and updating the knowledge graph database. According to the method, the powerful natural language understanding and knowledge reasoning potential of the LLM can be utilized to the maximum extent, and the inherent defects of the LLM are actively and systematically overcome by introducing an innovative mechanism.

The invention discloses a medical clinical data quality analysis method and system based on a knowledge graph, and relates to the technical field of medical data analysis. The method comprises the following steps: constructing a dynamic medical field knowledge graph fusing medical ontology and semantic relationship, accessing multi-source heterogeneous clinical data into the graph through entity linking and standardized mapping, and generating a personal data sub-graph containing time sequence information for each patient; through combination of logical reasoning based on a semantic rule base and anomaly detection based on a graph neural network, deep discovery and accurate positioning of known contradictions and unknown mode quality problems are realized, and a targeted repair scheme is generated by utilizing the traceability of a graph; quality problems are quantitatively graded and fed back to the atlas, and closed-loop management is formed; according to the method, the limitations of isolated examination and lack of semantic understanding of a traditional method are overcome, the transformation from passive verification to active prevention is realized, and the control precision and efficiency of the medical data quality are remarkably improved.

The invention relates to the technical field of natural language processing, and discloses an intelligent slot filling and mode linking method based on RAG and M-Schema, and the method comprises the steps: rapidly matching a preset FAQ knowledge base through an RAG-FAQ module, and extracting a predefined medical intention and a slot structure; if the matching fails, starting an NLU module to perform intention classification and slot position extraction; performing entity linking and normalization on the slot value by utilizing an RAG-medical field knowledge base context enhancement module, performing multi-dimensional verification in combination with M-Schema metadata of a target database, and outputting a matching column and a confidence score; aiming at uncertainty types such as medical term ambiguity and business rule conflict, a clarification problem is dynamically generated, user feedback is received, SQL query conforming to business rules is generated after slot position information is corrected, and finally the SQL query is transmitted to a medical database execution module to output an analysis result. According to the invention, the availability and accuracy of the general framework in the scene facing the hospital management field can be improved.

The invention belongs to the technical field of natural language processing and geographic information systems, and particularly relates to a region address standardization method based on knowledge graph enhanced retrieval. Cleaning and preprocessing the original address text input by the user; utilizing a fine-tuned large language model to identify a geographic entity and performing standardized expansion on variant expression to generate a query candidate set; entity linking and context retrieval are carried out based on the knowledge graph, and attributes, hierarchy and spatial topology information of associated entities are obtained; in combination with the original input and the map context, an enhanced retrieval query text is generated through large language model reconstruction; vectorized semantic retrieval is carried out through the fine-tuned embedding model, and a preliminary candidate address set is obtained; carrying out multi-dimensional refined sorting by adopting a resorting model; and generating a structured standard address by using a large language model, and outputting the structured standard address after multi-level verification. According to the method, the problems of ambiguity resolution, alias recognition and context understanding in address processing are solved, and the accuracy and robustness of address standardization are improved.

The invention discloses a lightweight knowledge graph rapid construction method and system based on an NLP technology. The method comprises the steps of preprocessing an unstructured text and segmenting the unstructured text into semantic segments; unsupervised clustering is combined with the contour coefficient to determine the optimal clustering number, and a semantic association fragment cluster is obtained; performing word segmentation, part-of-speech tagging, NER and entity linking on the fragment cluster, extracting an entity and initial relationship, and fusing semantic similarity, TF-IDF word frequency collaboration degree and co-occurrence frequency to calculate a relationship edge weight; constructing a lightweight knowledge graph; in the question and answer stage, questions are disassembled through a process engine, related sub-graphs are retrieved, and answers with reasoning links are generated. The system correspondingly comprises a text preprocessing module, a clustering module, an entity relation processing module, a graph construction module, a question and answer reasoning module and a storage module. According to the method, the construction cost is reduced, the interpretability and the module coupling degree are improved, multiple scenes such as government and enterprise public opinions and medical assistance are adapted, and the problems of weak generalization, poor real-time performance and'black box 'in the traditional technology are solved.

The invention belongs to the technical field of knowledge maps, and discloses a grape disease and insect pest automatic question-answering system based on a multi-modal knowledge map. Comprising a data collecting and processing module, a knowledge graph construction module, a main control module, a named entity recognition module, a text classification model module, a multi-modal fusion module, a knowledge graph storage module, an entity matching module and a query evaluation module. Constructing a multi-modal knowledge graph in the field of grape diseases and insect pests; the system analyzes characteristics of grape disease and insect pest data, studies a structured expression and integration method of grape disease and insect pest knowledge, constructs a grape disease and insect pest multi-mode knowledge graph ontology concept framework, and provides support for unified management and efficient utilization of related knowledge; the named entity recognition (NER) is carried out by utilizing a global normalization thought aiming at the condition that a hierarchical or nested relationship exists between entities in a knowledge graph construction process. An entity linking technology is adopted for data of different modes, and a grape pest and disease damage multi-mode knowledge graph is constructed.

The invention relates to the field of data retrieval, and particularly provides a dynamic retrieval enhancement generation method and system based on a knowledge graph, a terminal and a medium, and the method comprises the following steps: extracting a structured triple from multi-source heterogeneous data through a large language model, and constructing a global knowledge graph by means of an entity linking technology; integrating a real-time data stream interface, and dynamically updating graph nodes and attributes based on an event-driven mechanism; adopting a RotatE model to respectively encode the entity and the relationship to a complex number space, and fusing to generate a mixed vector to construct an efficient index; after user query is received, topic nodes are positioned through semantic analysis, related entities are retrieved through mixed indexes, and multi-hop reasoning is executed along a relation path to generate reasoning sub-graphs and extended contexts; and finally, generating structured text answers by using a large language model, and adaptively outputting multi-modal results such as texts, charts and the like according to user requirements. According to the method, the knowledge updating timeliness, the complex query reasoning capability and the retrieval precision are effectively improved.

The invention discloses an underground engineering on-demand ventilation knowledge graph retrieval enhancement generation method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps of data cleaning and ontology layer modeling, knowledge extraction based on prompt engineering, knowledge graph data filling and storage, storage of an extraction result in a Neo4j graph database, and retrieval enhancement generation of an inference engine. Developing an intelligent question and answer platform, and providing knowledge graph visualization and interactive question and answer functions; the system comprises a data acquisition and preprocessing layer, an ontology and knowledge extraction layer, a graph storage layer, a semantic index and entity link layer, a sub-graph arrangement and cue word generation layer, a generation and reasoning layer and an application and interaction layer. According to the invention, the acquisition and management efficiency of underground engineering ventilation knowledge is improved, the professionality and accuracy of the question-answering system are enhanced, the interface is friendly, knowledge tracing is supported, and convenient experience is provided for users.

The invention discloses a multi-modal clothing recommendation method. The method comprises the following steps: firstly, constructing a knowledge graph by taking related data as nodes and edges, and storing the knowledge graph in a Neo4j graph database; extracting unified multi-modal vector representation of image and text pairs, performing entity linking, establishing a multi-modal knowledge base, finding a Top-m result most similar to problem embedding through an RAG strategy based on the knowledge base, positioning nodes of the result in a knowledge graph, generating a sub-graph, and according to edge weights in the knowledge graph, establishing a multi-modal knowledge base; and calculating a recommendation score of each scheme in combination with the sub-graph to obtain a final Top-n recommendation result, splicing knowledge of the Top-n scheme with a user question to generate a structured text answer, and rendering a text description part into visual content to realize mixed output of image-text combination. By combining the knowledge graph and the knowledge base driven RAG with the generation of the high-quality generation model, the semantic information and visual consistency of complex clothing recommendation can be flexibly captured, so that the clothing recommendation accuracy is remarkably improved.

The invention discloses an equipment fault diagnosis method and device based on a dynamic knowledge graph and a large model fine tuning technology. The method comprises the following steps: firstly, identifying a core entity from multi-source heterogeneous equipment fault data through a named entity identification model for fine tuning of domain data and a relation extraction model for special fine tuning of a fault diagnosis domain corpus, mining deep semantic association, and injecting the deep semantic association into a graph database after cleaning to form an initial knowledge graph; receiving user natural language fault description, realizing term and standard entity linking through editing distance fuzzy matching and Sension-BERT semantic vector similarity calculation, and combining bidirectional retrieval and attention mechanism fusion to obtain an enhanced context; and finally, generating a structured diagnosis report containing thinking chain reasoning based on an enhanced context by utilizing a specialized fine-tuning fault diagnosis large language model. According to the method, the limitation of a traditional diagnosis method is effectively solved, high-precision and interpretable equipment fault diagnosis is realized, and the diagnosis efficiency and reliability are improved.

The invention discloses a network security attack and defense strategy generation method, system and device fused with a knowledge graph and a medium, and belongs to the technical field of network security attack and defense strategies, and the method comprises the steps: dynamically constructing a network security knowledge graph containing an attack path and vulnerability association relationship based on multi-source data of a public vulnerability library, dynamic mapping of real-time asset change information and a graph entity is realized through an entity link mechanism, and consistency verification is performed on a mapping relation by using a graph neural network model so as to filter anomalies; structured and unstructured attack feature data are collected in real time from channels such as network traffic, and preprocessing is completed through regular cleaning, feature selection and data quality evaluation; real-time data and a knowledge graph are subjected to multi-dimensional matching, an attack and defense target function of a defense cost target is combined, a candidate strategy set is generated through a genetic algorithm, an incomplete information game model is introduced to calculate sub-game perfect Nash equilibrium to determine an optimal strategy, and a dynamic Bayesian network is utilized to update a strategy transition probability based on historical data.

Techniques for handling natural language query processing are described. In some examples, semantic meanings of words are determined during the natural language query processing. These semantic meanings are generated from metadata associated with the query and are to be used by an entity linker to help the linker link candidates to columns.

The invention provides a link prediction method, device and product for a power grid main equipment fault knowledge graph, the method inputs the power grid main equipment fault knowledge graph into a knowledge graph link prediction model for link prediction, and the link prediction process comprises the following steps: dividing a triple into a test set and a training set; for all triples in the test set, scoring and sorting all entity relationships in the power grid main equipment fault knowledge graph to obtain an entity relationship list; using a to-be-predicted relationship as an entity relationship of all triads in the test set to obtain a query triad; calculating the overall similarity between each triad in the training set and the query triad, and screening the triads in the training set through the overall similarity to construct an external knowledge base; and inputting the entity relationship list, the query triad and the external knowledge base into the large language model to obtain an entity link prediction result. According to the technical scheme, the accuracy of link prediction in the fault knowledge graph of the main equipment of the power grid can be improved.

Systems and methods for entity linking using a graph neural network are disclosed. In one aspect, a method for entity linking can include extracting a first attribute set of an unknown entity from an information source and retrieving second attribute sets of known entities from a database, wherein each of the second attribute sets corresponds to one of the known entities. The method can further include generating an unknown entity graph based on the first attribute set, generating known entity graphs based on the second attribute sets, generating an unknown entity graph embedding by applying the unknown entity graph to a graph neural network, and generating known entity graph embeddings by applying the known entity graphs to the graph neural network. The method can further include assigning the information source to one of the known entities based on the unknown entity graph embedding and the known entity graph embeddings.

The invention discloses an event causal reasoning method for intelligence analysis and electronic equipment. The method comprises the following steps: acquiring a plurality of evidence fragments related to a query text from a plurality of sources, and taking the plurality of evidence fragments as an evidence fragment set; extracting entities from the evidence fragments, and linking the entities into a knowledge graph; event elements are extracted from the evidence fragments, event nodes and causal edges are generated according to the event elements, and one or more candidate causal edges are generated based on the event pairs; retrieving from the evidence fragment set to obtain one or more binding evidences of each candidate causal edge; and calling a large language model for each candidate causal edge to generate a causal assertion, performing consistency verification on the binding evidence of the causal assertion and the same candidate causal edge, combining into a traceable causal evidence chain, and performing event causal reasoning analysis. According to the method, the event-level representation is constructed, and the event-event candidate causal relationship is generated, so that a reasoning object is improved from entity co-occurrence to a reasonable and maintainable event causal structure.

The embodiment of the invention relates to the technical field of knowledge maps, and particularly discloses a knowledge map construction method and system for enterprise dynamic risks. According to the embodiment of the invention, risk original information is obtained; performing entity, attribute, relation and time information extraction on the risk original information; performing unified processing of entity linking, attribute merging and relation conflict resolution on the multiple pieces of structured information; constructing a basic knowledge graph; and based on the basic knowledge graph, constructing a dynamic risk model, performing future relationship prediction and risk trend prediction, and generating and displaying dynamic prediction information. According to the method, time span credibility evaluation and timeliness conflict interval positioning are carried out on multi-source attributes, the problems of attribute description conflicts and timeliness dislocation in multi-source data are effectively solved, meanwhile, permeation expression of risk signals on the attribute level is enhanced, and a consistent data basis is provided for accurate enterprise risk portraits.

The invention discloses a knowledge graph entity linking method and system for eliminating popularity deviation based on causal inference and a medium, an entity candidate set generation stage: processing an input text to generate a candidate entity set, and extracting semantic features, structural features and popularity features of each candidate entity; the problem that unpopular entities are neglected is effectively solved, and the robustness under complex contexts and noise data is remarkably enhanced; the effectiveness of the method in balancing popularity and semantic correlation is verified; therefore, the model can more comprehensively sense the context relationship of the entity in the knowledge graph, and the accuracy and robustness of entity linking are further improved. Ablation experiments show that introduction of the structural features contributes significantly to performance improvement of the long-tail entity.

The invention relates to a post personnel knowledge extraction method, device and equipment based on a large language model. The method comprises the following steps: defining and formally describing an entity set, a relationship set and a rule constraint set in a knowledge extraction process to construct a cue word template; on the basis of a cue word template, an obtained resume data set is input into a large model, a post personnel field triple set is output through a multi-round chain type iteration mechanism, and then a knowledge alignment strategy is carried out; performing entity-relationship filtering by using a regular expression, establishing a cross-knowledge-base entity link channel with an API (Application Program Interface) of the Wikdata database, performing knowledge disambiguation, and calculating an editing distance between entities; and calculating a similarity score between the entities according to the editing distance, and determining a knowledge alignment result by using the similarity score to complete knowledge extraction. By adopting the method, the intelligent level and accuracy of man-post matching can be improved.

The invention provides an electric power marketing service event risk hierarchical identification method and system based on knowledge graph semantic modeling, belongs to the technical field of electric power marketing service event risk identification, and solves the problems of data islanding, semantic understanding deviation, poor scene adaptation and high manual dependence in the prior art. According to the technical scheme, the method comprises the steps that multi-source data including 95598 appeals, electric vehicle charging / live broadcast electricity utilization and the like are collected, a knowledge graph with entity links is constructed, and risk identification is achieved through hierarchical semantic modeling, three-level risk identification and dynamic optimization. According to the method, the data association rate and the risk identification precision can be improved, the enterprise operation cost is reduced, and the digital transformation of enterprise power marketing is promoted.

The application discloses an industrial knowledge generative decision method based on multi-granularity semantics and large model assistance, comprising the following steps: step one: encoding a business problem into high-dimensional problem semantic features; step two: processing a multi-modal industrial knowledge graph by using entity linking; based on TOP-K path expansion, different subgraphs are obtained; step three: adopting a graph neural network GNN to capture different granularity features in the subgraphs, and obtaining industrial knowledge multi-granularity semantic features; step four: uniformly fusing the industrial knowledge multi-granularity semantic features and the problem semantic features by using a graph convolution network GCN; step five: constructing a triple sampling process for SPARQL-based query, and extracting relevant triple data; step six: rewriting the structured triple data into free format text, and constructing a problem-decision pair data set; and step seven: fine-tuning a large language model based on the problem-decision pair data set, and obtaining an industrial knowledge generative decision model enhanced by knowledge text.

The application discloses a BERT model optimization method combined with knowledge graph entity correlation. The method firstly obtains an entity set through entity linking according to a knowledge graph and a text data set, and calculates the correlation between entities according to the shortest path length between the entities on the knowledge graph in the entity set, then inputs the text data set into the BERT model as a training sample, trains the BERT model by using the loss function in the BERT model, and optimizes the attention distribution of the corresponding entity part in the text data under the multi-head attention of the BERT model according to the correlation between the entities during the training process, obtains the optimized BERT model, and applies the BERT model to processing a downstream task. The application guides the training of the BERT model through the knowledge graph entity correlation, and effectively improves the effect of the BERT model in processing the downstream task.

The application relates to a kind of port entry and exit special goods risk identification methods based on knowledge graph construction and entity linking, comprising: according to the risk identification demand of port entry and exit special goods in advance, constructing hierarchical risk factor knowledge graph;Obtain the declaration material of risk to be identified, carry out entity extraction from the declaration material, retrieve historical similar cases according to the extracted entity, build enhanced context containing declaration material and its historical similar cases, and carry out overall vectorization processing;Adopt three-granularity matching strategy: first, through Top-K screening, to match special goods subclass with coarse granularity;Then through threshold filtering, to match risk factor with medium granularity, finally, fine-grained matching risk feature node, to obtain the risk identification result of port entry and exit special goods.Compared with the prior art, the present application has the advantages of more precise risk feature expression, explicit risk knowledge audit, and realizes accurate matching from declaration material to knowledge graph node.

Embodiments of the present disclosure disclose a method, an electronic device and a computer readable medium for multi-entity joint linking. A specific implementation of the method comprises: obtaining a target entity mention and original text; generating a candidate entity information set based on a predetermined text library and the target entity mention; inputting the candidate entity information set into a pre-trained encoding model to generate a candidate entity feature set; for each candidate entity information in the candidate entity information set, concatenating the candidate entity, the original text and the candidate entity feature set of the candidate entity information to generate a text information feature, so as to obtain a text information feature set; inputting the text information feature set into a pre-trained classification model to generate a discrimination result set; and determining an entity linking result set based on the discrimination result set. This method uses the text information feature set to supplement information in the candidate entity through multi-entity joint linking, thereby improving the entity linking accuracy in short text and information deficiency scenarios.

The application provides an entity linking method and system based on dynamic time steps, and relates to the technical field of natural language processing. The method comprises the following steps: obtaining a plurality of entity mentions of a related topic in a document to be analyzed; obtaining candidate entities of each entity mention from a knowledge base; constructing a mapping entity association graph by using a dynamic time step method, and extracting global topic consistency features from the mapping entity association graph; obtaining target entities of each entity mention from the candidate entities according to the global topic consistency features, and associating each entity mention with the corresponding target entity. In this way, by using the dynamic time step method to construct the mapping entity association graph, in each time step, the candidate entities related to the topic are selected as the mapping entities of the mapping entity association graph, so that the correct global topic consistency features can be extracted from the mapping entity association graph, thereby improving the accuracy of entity linking.

This invention relates to the field of natural language processing technology, and more particularly to a method, apparatus, terminal, and storage medium for question answering related to power finance knowledge. The method first performs word segmentation and entity linking on the question text, identifying multiple candidate word sets corresponding to multiple keywords in the power finance knowledge graph. The power finance knowledge graph includes multiple entities and multiple relationships. Then, relationship path matching is performed based on the multiple candidate word sets to obtain multiple candidate relationship paths. Finally, the final relationship path is selected from the multiple candidate relationship paths, and the final answer is determined based on the final relationship path. This invention utilizes a link reasoning model based on the intersection of candidate word sets for power finance knowledge graph question answering tasks, which can reduce the scope of relationship matching, improve the accuracy of relationship path matching, and thus enhance the accuracy of the question answering task.

The application provides an entity linking method and system, an electronic device and a readable storage medium, wherein the method comprises the following steps: determining a representation of a denotation and a context entity; determining an entity vector of a candidate entity, wherein the entity vector of the candidate entity is obtained by using an average method of word vectors to initially represent entity words of the candidate entity; inputting the entity type feature vector, the entity distance feature vector, the entity relationship feature vector and the entity vector of the candidate entity into an entity disambiguation model to obtain a similarity between the denotation and the candidate entity. The application models entities with complex interaction relationships, maps the entities into low-dimensional vectors, carries more semantic information, improves the accuracy of the model, can fully utilize the context of the text where the entity is located and the background information of the text, disambiguates the entities in the unstructured text, and serves subsequent graph expansion and graph merging and the like.

A multimodal entity linking method based on dual encoders and a hybrid expert mechanism is proposed. This invention relates to multimodal entity linking technology at the intersection of natural language processing and computer vision. Addressing the problems of low inference efficiency, insufficient cross-modal interaction, and shallow modal fusion in existing methods, this invention proposes a multimodal entity linking method based on dual encoders and a hybrid expert mechanism. A dual-tower architecture is used to independently encode mentions and entities. Entity embeddings can be pre-computed offline and indexed, and linking is completed during inference through fast vector retrieval. A hybrid expert mechanism is introduced to achieve adaptive feature transformation of samples, and a gating network dynamically selects expert combinations. Bidirectional cross-modal attention is used to establish fine-grained alignment at the word-image block granularity. A channel attention mechanism dynamically balances the contributions of textual and visual modalities. The model is jointly optimized by multiple constraints, including load balancing loss. This invention achieves efficient retrieval while maintaining deep inference capabilities, simplifies inference time complexity, and is suitable for scenarios such as knowledge graph construction and intelligent question answering systems.

The invention provides a network content-oriented hierarchical semantic processing label automatic generation method and system, and relates to the technical field of semantic processing, and the method comprises the following steps: receiving a semantic analysis request of a target user, analyzing to obtain a first work and a second work, respectively carrying out core entity extraction and entity derivative analysis, and establishing a retrieval vector, the method comprises the following steps: collecting a first network content set and a second network content set from a network data source, respectively extracting key semantic concepts and associated emotional polarities, performing entity linking with a pre-constructed domain knowledge graph, constructing a first hierarchical semantic tag set and a second hierarchical semantic tag set, and comparing the first hierarchical semantic tag set and the second hierarchical semantic tag set; and performing label alignment classification and semantic aggregation about the knowledge field and the emotional polarity to generate a hierarchical comparison report. The technical problems that in the prior art, network content hierarchical semantic tags are low in generation efficiency and limited in analysis precision are solved, and the technical effects of efficiently and accurately generating precise semantic tags and improving semantic processing efficiency and analysis precision are achieved.

The application relates to an entity linking method, device, equipment and medium, wherein the method comprises the following steps: extracting element data from target data, wherein the target data comprises a field to be linked to corresponding entity data; matching the element data with entity elements in a preset element library to obtain a matching result, wherein the preset element library is a database of entity elements including a plurality of entity data which is pre-arranged; and determining a target entity to be linked by the target data according to the matching result. Through the element matching method of the target data and the entity of the preset element library, the linking entity corresponding to the target data is determined, and the problems of low artificial matching efficiency and large amount of labeled data required by model matching are solved.