5 results about "Seed entity" patented technology

The invention discloses a psychological health assessment field knowledge graph construction method and a storage medium. The method comprises the following steps: S1, data preprocessing and structuring: processing an original document of a mental health assessment scale, and converting the original document into scale descriptive text and structured data; s2, entity extraction and seed entity set construction: extracting measurement factors and dimension entities in a single scale range, and constructing a standardized seed entity set; s3, relation triad extraction and deduplication: extracting structural and semantic triads under the constraint of a single scale, and performing semantic discrimination and deduplication by using LLM; and S4, multi-level map fusion and dynamic mapping are carried out, a global three-level semantic skeleton is constructed, entities are normalized and mapped to a standard concept, and problem access and map completion are realized. According to the method, the problems of cross-scale semantic confusion, inconsistent entity granularity and lack of hierarchical logic in the prior art are solved, and the specialty, accuracy and traceability of the knowledge graph are improved.

The present invention proposes a knowledge graph fact-checking method enhanced with hint subgraphs, comprising: S1, locating entities of the factual statements to be checked and identifying seed entities; S2, using the seed entities as seed nodes, performing a depth- and width-restricted heuristic bundle search on the knowledge graph to construct a local hint subgraph for the factual statements to be checked; S3, inputting the hint subgraph as a structured context into a large language model to generate a query program composed of three types of restricted functions: SEARCH, MATCH, and VERIFY; S4, executing the query program on the knowledge graph through an interpreter and outputting whether the factual statements to be checked are supported by the current knowledge graph. This invention, by introducing a hint subgraph constraint program generation process, solves the problems of easily fabricating relation names, omitting intermediate entities, and query path mismatches in existing methods, thereby improving the matching degree between the generated program and the real structure of the knowledge graph.

This application relates to the field of artificial intelligence technology, and in particular to a knowledge graph question answering method, system, and storage medium based on structural induction and differentiable connectivity constraints. It decomposes natural language questions to obtain seed entities, employs personalized random walks around these seed entities to obtain their structural importance distribution, and integrates this distribution with semantic similarity as a retrieval criterion to filter candidate entity subgraphs. A gated graph neural network with geometric features such as hop count and node degree is introduced into the candidate subgraphs for structural inductive reasoning. Furthermore, differentiable connectivity constraints are constructed to softly model entity rewards and edge costs, ensuring the compact connectivity of the reasoning subgraph. End-to-end optimization is performed by combining question answering main loss, ranking loss, and connectivity regularization, thereby achieving accurate reasoning and prediction of answer entities in the knowledge graph.

The invention belongs to the technical field of artificial intelligence, and relates to a multi-agent-based knowledge graph automatic construction and query method and system. The method comprises the following steps: performing entity identification on an input document to obtain an entity set; determining a theme of the input document, and obtaining a seed entity according to the association strength between each entity in the entity set and the theme; extracting a relationship between seed entities based on a layered strategy label system; the seed entities and the extracted relations form candidate knowledge triples, and verification of different dimensions is carried out on the candidate knowledge triples through multi-agent cooperation; storing the knowledge triples passing the verification into a knowledge base to form a knowledge graph; and querying by using the knowledge graph to obtain answers to the questions. According to the method, the knowledge can be organized into a traceable logic chain, the interpretability and reasoning capability of the knowledge graph are improved, the consistency and reliability of the stored knowledge can be improved, and the timeliness maintenance capability and query accuracy of the knowledge base can be improved.

This invention belongs to the field of privacy computing technology and provides a method for constructing a multi-departmental government knowledge graph and making collaborative decisions with intelligent agents based on asynchronous federated learning. The method includes: CLIP joint embedding feature extraction, cross-departmental collaborative updating, relational reasoning and cross-departmental entity matching, comprehensive judgment and decision-making execution path matching, construction of a domain-level large graph and exception condition library, construction of an RPA rule base, and generation of automated processes. This invention achieves semantic-level fusion of heterogeneous data through seedless entity alignment and multimodal embedding technology, breaking down data silos; improves the rationality of human-machine collaboration through a two-factor threshold judgment mechanism; and enhances the automation level of processes by automatically mapping stable business rules to RPA action sequences.