4 results about "Semantic technology" patented technology

A subject-event method and a workflow engine developed based on it are designed for no-code creation and execution of semantic models of business processes and construction of domain ontologies. The operation of the workflow engine is based on original event semantics using the semantic implementation of a dataflow approach to algorithm execution. The use of a unified event data format made it possible to implement: (1) a specification for describing actions and entities, (2) a semantic workflow engine for executing business processes, (3) a semantic temporal data store. The invention provides a unified platform for enterprise content management, process automation, and knowledge base maintenance, addressing the limitations of traditional workflow engines and semantic technologies.

The application provides a medical search recommendation method, system, product and terminal based on a knowledge graph and a semantic vector. The medical search request input by a user is subjected to medical entity recognition and standardized processing to obtain standard medical entity data. On the one hand, based on the standard medical entity data, a pre-constructed medical knowledge graph is used to perform retrieval reasoning to obtain a first retrieval result set. On the other hand, based on the standard medical entity data, a pre-trained semantic vector model and a vector database are used to obtain a second retrieval result set. According to a preset fusion rule, the first retrieval result set and the second retrieval result set are subjected to fusion processing to obtain a recommendation result. The application effectively breaks the limitations of the existing medical retrieval technology, and through double-path collaborative fusion, the defects of the vector semantic technology in lacking explicit medical logic support and the semantic generalization of the knowledge graph being insufficient are compensated for, so that precise, interpretable and safe medical search recommendation is finally achieved.

Embodiments of the present disclosure disclose a method and device for querying building data and synthetic data. The specific implementation of the method comprises: extracting a graph embedding vector from input building-related graph data, and storing the graph embedding vector into a vector database and storing the graph data into a graph database; converting a question related to the graph data into a question embedding vector, and searching for a target vector similar to the question embedding vector in the vector database; generating a query instruction in a predetermined language based on the target vector through a first large language model; executing the query instruction in the graph database to determine a query result; and outputting a natural language answer to the question based on the query result through a second large language model. The implementation realizes efficient querying of complex building data and reduces the user's dependence on professional knowledge of semantic technology.

The application discloses a kind of multimodal depth semantic fusion integrated machine system and method, system uses integrated design, including hardware base, fusion engine, enable platform and scene application relevant module, hardware base provides heterogeneous computing, high-speed storage and interconnection resources, fusion engine realizes multi-channel semantic understanding, depth fusion decision and resource elasticity scheduling, enable platform provides visual configuration and model management tool, scene application module provides industry application template;Method generates intermediate semantic result by parallel processing multiple source heterogeneous input, generates structured instruction by multidimensional confidence evaluation and tree fusion decision, realizes system closed-loop optimization in combination with interactive feedback;The application realizes depth collaboration of software and hardware and private deployment, solves the problems of complex deployment, insufficient adaptation, multi-source information fusion difficulty and data security risk etc. of traditional semantic technology.