7 results about "Noun" patented technology

The application provides a search and recommendation method based on a tourism knowledge graph. The application comprises: in a label preparation stage, extracting nouns and corresponding characteristics from a tourism site introduction and comments according to rules and processing the nouns and the corresponding characteristics into labels; in a model training stage, transmitting the labeled data into a PEGASUS model pre-trained by LCSTS for training, and then transmitting unlabeled data and debugging; in a search construction stage, associating the graph with a source site and storing the graph in a database to construct a visual module and a query transmission module of a system; and in a recommendation construction stage, constructing a recommendation calculation module of the system. The application has the following advantages: 1. The labels retain the original meaning and the superior-inferior relationship, which facilitates future classification of the graph; 2. Synonyms are processed according to the principle of majority covering minority, so that the labels are generated as uniformly as possible, long-term work is reduced, and the growth of traditional inverted index vocabulary is avoided; 3. Site reputation feature retrieval is more in line with user needs than text retrieval; and 4. Compared with traditional recommendation algorithms, the application is more interpretable.

The invention discloses an intelligent inspection work order quality inspection method and system based on semantic features, and relates to the technical field of inspection work order quality inspection.The method comprises the steps that historical inspection work order data are read, and an identification noun candidate set is recognized; calculating a synonymous expression updating frequency set of the identification noun candidate set; dividing the identification noun candidate set into multiple types of identification noun candidate sets according to the size of the synonymous expression updating frequency set; and connecting an inspection work order quality inspection system, performing priority rule quality inspection on the identification noun set of the current inspection work order, and outputting an identification noun quality inspection return result. The technical problems that in the prior art, due to the fact that a single quality inspection rule is adopted for inspection work order identification nouns, expression changes of nouns of different active degrees cannot be adapted, the quality inspection efficiency is low, and the accuracy is insufficient are solved, and the technical effects that the nouns of different active degrees are accurately adapted, and the quality inspection efficiency and accuracy are improved are achieved.

A method, apparatus, and computer-readable medium for domain-specific execution of natural language queries, the method including receiving a natural language query comprising a plurality of query terms, identifying nouns in the plurality of query terms, querying an entity index based on the nouns to identify class entities corresponding to the nouns and class attributes corresponding to the class entities, generating structured query language (SQL) queries corresponding to the natural language query based on providing the class entities and the class attributes as context to a large language model (LLM), transforming the SQL queries into domain specific language (DSL) queries based on the SQL terms, additional terms semantically related to the SQL terms, and DSL mapping rules configured to map SQL to DSL, executing the DSL query on an enterprise data catalog to identify results corresponding to the natural language query, and transmitting the results in the user interface.

The application discloses a semantic feature-based intelligent inspection method and system for an inspection work order, and relates to the technical field of inspection work order inspection. The method comprises the following steps: reading historical inspection work order data and identifying a set of identified noun candidates; calculating a set of synonymous expression update frequencies of the set of identified noun candidates; dividing the set of identified noun candidates into multiple sets of identified noun candidates according to the sizes of the set of synonymous expression update frequencies; connecting an inspection work order inspection system; performing priority rule inspection on a set of identified nouns of a current inspection work order; and outputting an identified noun inspection return result. The application solves the technical problem that a single inspection rule is used for identified nouns of an inspection work order in the prior art, which cannot adapt to the expression changes of nouns with different activity levels, resulting in low inspection efficiency and insufficient accuracy. The application achieves precise adaptation to nouns with different activity levels, and improves the inspection efficiency and accuracy.

The invention relates to a large language model Auto-CoT method based on sample iterative extraction. The method comprises the following steps: firstly, extracting a problem entity from a medical problem as an initial value; then, a large language model is used for generating noun entities with similar semantics with the entities, and semantic similarity scores between the noun entities and the problem entities are calculated; thirdly, searching entity nodes similar to the noun entities in the medical knowledge graph, and calculating entity similarity scores; when the entity similarity score is lower than 0.5, the similarity degree is low; when the value is higher than 0.5, the similarity degree is high. Therefore, entity nodes with similarity scores higher than 0.5 are used as candidate samples. And finally, organizing the node with the highest score and the related content into a sample and adding the sample into Auto-CoT so as to guide the generation of a large language model, thereby enhancing the accuracy and interpretability of the answer. A specific experiment verifies that the method shows a remarkable effect in the aspect of improving the medical query processing capability and interpretability of the large language model, and the performance of an intelligent question answering system in the medical field is remarkably improved.

The invention relates to the technical field of natural language processing, and discloses a T5 and knowledge graph data enhancement-based small sample event extraction method, which comprises the following steps of: segmenting an original text according to a preset segmentation bound character, and screening a longest fragment as a covering object; performing prediction generation and backfilling on the covering object by utilizing the T5 model after fine adjustment to obtain a preliminary enhanced text; performing similar entity replacement on nouns or pronouns except the key information in the preliminarily enhanced text based on a Chinese concept knowledge graph to obtain a new sample; and combining the new sample with the original text to form an enhanced data set, and driving the event extraction model to train. According to the method, text distribution consistency is kept by adopting generative fine tuning, semantic diversity is increased by combining knowledge graph replacement, and meanwhile, tagging information is directly inherited by protecting trigger words and arguments, so that the accuracy and generalization of event extraction under a small sample condition are effectively improved.

This invention discloses a method and system for integrating search and enhanced generation capabilities. The method includes: S1 constructing a basic SQL set, where each basic SQL corresponds to a preset business intent and includes placeholder condition fields; S2 establishing a mapping relationship between fields and noun types; S3 performing intent analysis using a large language model to match the basic SQL; S4 performing query condition reasoning, extracting specific nouns, and performing extended word generation and compound word processing; S5 generating complete SQL according to rules; S6 executing the SQL and dynamically correcting the query results to meet context length requirements; and S7 combining the question and result and inputting them into the large language model to generate an answer. This invention does not require a vector database, adapts to small parameter models, is low-cost, and has strong real-time performance, and can be widely applied to various traditional business systems that require integrated AI dialogue capabilities.