220 results about "Entity linking" patented technology

The invention relates to an event information enhancement and deduction prediction method combined with a knowledge graph, and belongs to the field of natural language processing and knowledge graphs. The objective of the invention is to solve the problem of insufficient accuracy and robustness of a deduction result due to the fact that multi-dimensional information cannot be effectively fused in an existing event deduction method. In order to solve the problems that a traditional event deduction method mostly depends on a large amount of entity link information and is low in efficiency and prone to being influenced by data scarcity in practical application, the method comprises the five steps of data input and preprocessing, knowledge graph construction and optimization, event semantic background enhancement, event deduction prediction and event deduction prediction. According to the method, the limitation of a traditional method in event deduction can be effectively overcome by introducing technologies such as a cross-domain knowledge graph and a graph neural network, rich event semantic backgrounds are constructed by utilizing the knowledge graph, event information is enhanced and deduced in combination with a time sequence and a causal relationship between events, and the event deduction efficiency is improved. Therefore, the accuracy and reliability of event deduction are improved.

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

The invention relates to a document processing method and system based on text content extraction. The method comprises the steps that an original document containing text, image and format information is received, the encoding format of the document is automatically detected, character set conversion is executed, and hierarchical indexes including page numbers, paragraphs and tables are established for an unstructured document; the method comprises the following steps: synchronously processing text content and visual layout through a pre-trained visual-language model, extracting word-level and sentence-level semantic features by a text stream embedding layer, analyzing spatial distribution features of document elements by a visual encoder, and fusing text and visual features through a cross-modal attention mechanism; and loading the domain knowledge graph matched with the document type, and executing entity linking to associate the text mentions to the knowledge nodes. According to the document processing method and system based on text content extraction, through the synergistic effect of vision-text joint coding and knowledge enhancement, the accuracy of financial contract key clause recognition tasks is improved, the error rate is lower than that of industry benchmark products, and the semantic understanding precision is remarkably improved.

The invention provides an entity link-based multi-source information association system and method in an agricultural scene, and belongs to the technical field of agricultural artificial intelligence, and the system comprises a data analysis module which constructs a multi-modal data analysis layer for data analysis, converts the analyzed data into a knowledge unit in a preset format through a Converter component, and stores the knowledge unit in the preset format; retaining an original semantic hierarchical structure and generating knowledge association anchor points; the graph construction module is used for constructing a cross-modal knowledge graph by utilizing knowledge association anchor points and an entity linking technology; the graph retrieval module is used for retrieving the cross-modal knowledge graph on the basis of a GraphRAG hierarchical retrieval technology; the result optimization module is used for optimizing the retrieval result to obtain an optimized retrieval result; and the result processing module constructs a low-code workflow engine based on the optimized retrieval result, further constructs an agricultural knowledge processing assembly line, and executes the agricultural knowledge processing assembly line to obtain an executable working scheme. And the processing efficiency of agricultural knowledge is improved.

Techniques for handling natural language query processing are described. In some examples, entities are recognized during an entity recognition phase and then relations between those entities are determined. Those relations are fed to an entity linker to help the linker link candidate to columns and / or a intent representation generator to help parse multiple values and column pairs of a natural language query.

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 relates to the technical field of knowledge processing, and particularly discloses a knowledge proposition error correction method and system based on knowledge graph optimization upgrading, and the method comprises the steps: extracting effective information from multi-source knowledge data through employing entity linking, relation extraction and attribute alignment methods, and constructing an initial knowledge graph. Through semantic analysis based on deep learning, grammar, semantic and logic analysis is carried out on knowledge propositions, and a proposition element triple is generated. A graph neural network is utilized to encode triples, proposition vectors and neighborhood feature vectors are obtained, structural similarity is calculated accordingly, and proposition errors are detected in combination with rules and a statistical method. An integrated learning algorithm is adopted to classify error traceability, and an error correction decision is made in combination with multi-source evidence, so that an accurate error correction result is obtained, and the accuracy and reliability of knowledge propositions are improved; the error correction process is more logical and reliable, blind error correction is avoided, and the quality and efficiency of knowledge proposition error correction are improved.

The invention discloses an agent reasoning method based on a knowledge graph, and belongs to the technical field of knowledge graphs, and the method comprises the steps: integrating user query, the knowledge graph and multi-modal data through an NLP model and an entity linking technology, and extracting a target entity, a relation constraint and a structured feature vector. The LLM can synthesize more information to generate a more comprehensive conclusion, the knowledge embedding model maps an entity relationship into a geometric relationship in a vector space, the LLM is assisted to verify reasonability of reasoning, the system can comprehensively generate confidence through the LLM output probability, knowledge embedding similarity and data quality, and the reliability of a result can be explained through confidence score. The LLM generation capability is combined with the vector reasoning capability of knowledge embedding, and the limitation of a single model is made up.

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 an entity linking method and system based on a large language model, and the method comprises the steps: carrying out the enhancement of the context of a given entity reference item in an entity document through a large language model, generating a candidate entity list for the entity reference item, and generating description information for each candidate entity in the candidate entity list; constructing a question and answer task of a single choice question for each entity reference item; constructing a reference graph by utilizing the entity reference items and the candidate entity list to obtain association degrees among the entity reference items; all the entity reference items are sorted, question and answer pairs of the preset number of entity reference items with the highest association degree of the current entity reference items are selected as dialogue contexts according to the sorting result, single choice questions are sequentially input into a large language model for answering, and the entity linking result of each entity reference item is obtained. According to the method, the potential relation between the entity reference items can be effectively captured, and the method is good in performance on the entity link task of the document level.

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 easily-confused entity linking method and system based on a graph neural network, and is applied to the technical field of natural language processing. The method comprises the following steps: encoding a short text and candidate entity description, generating a high-dimensional semantic vector, and providing basic features for subsequent entity disambiguation; respectively extracting features described by the short text and the candidate entities, and extracting distinguishable features in the features described by the candidate entities as candidate entity features by constructing a relation graph between the candidate entities and utilizing a graph distillation operator extracted from a graph neural network; the short text features and the candidate entity features are fused, and the most suitable entity is matched through similarity scoring. According to the method, the graph distillation operator of the graph neural network is introduced, so that the accuracy and robustness of entity linking are remarkably improved, and particularly, distinguishable features can be effectively extracted when easily confused candidate entities are processed, so that the disambiguation effect is improved.

The invention discloses an untrained biomedical entity linking method based on a large language model and retrieval enhancement generation. The untrained biomedical entity linking method comprises the following steps: inputting references to be linked into an SAPBERT-PubMedBERT model pre-trained by a biomedical corpus, and converting the references into reference embedding vectors; carrying out dimensionality distillation on the reference embedded vector by adopting a dimensionality reduction technology; performing similarity calculation on the reference embedding vector and an entity embedding vector in a pre-constructed knowledge database to obtain a similarity score, and screening out a specified number of candidate item entities to construct a candidate entity item set; designing a prompt template comprising an instruction, a sample example and actual input; and inputting the content in the prompt template into a large language model so as to select an optimal entity item corresponding to the to-be-linked reference from the candidate entity item set. According to the method, high-performance entity linking can be realized on different data sets, and retraining for different tasks is not needed.

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 embodiment of the invention provides a knowledge retrieval method and device for multi-source data fusion, electronic equipment and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: performing entity linking on a target image entity, a target audio entity and a language entity corresponding to multi-source data and a target text entity to obtain a target knowledge entity, and performing knowledge fusion on a relationship between the target knowledge entity and the target text entity and predefined ontology knowledge data to obtain a target knowledge entity; and performing knowledge retrieval on the retrieval indication information from a target knowledge graph constructed based on the fused knowledge data. According to the embodiment of the invention, the knowledge graph containing the multi-source data is constructed, and the knowledge retrieval is carried out from the knowledge graph, so that the knowledge retrieval can be carried out from the knowledge graph fusing the multi-modal data and the at least two kinds of language data, and the accuracy of the knowledge retrieval is improved.

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.