5 results about "WordNet" patented technology

The application relates to a signature-based set semantic similarity connection method, and belongs to the fields of databases and information retrieval. The method comprises four parts: firstly, a classification tree construction step: a classification tree is constructed according to a WordNet knowledge base given a data set; secondly, a set signature step: each set in the data set is signed to obtain a corresponding signature data set; thirdly, a data preprocessing step: the sets in the signature data set are sorted to obtain a sorted data set; and finally, a connection processing step: self-connection is performed on the sets in the sorted data set to obtain a set of semantic similarity results. The method is based on signature prefix filtering technology and length filtering technology, and finally realizes the set semantic similarity connection method, so that the set semantic connection efficiency can be effectively improved.

The present invention relates to a method, system, storage medium and program product for automatically constructing a network threat report attack knowledge graph based on a large language model. The method generates an initial attack knowledge graph by iteratively processing the extracted entities and relationships through a clustering method based on a large language model. At the same time, the present invention aggregates multiple technical example threat reports belonging to the same attack technology through an attack technology graph template generation mechanism, establishes a standardized template library for standardized attack technologies, and adopts an attack technology alignment method based on Word2Vec and WordNet to realize automatic attack technology labeling of new threat reports, construct a complete attack technology knowledge graph, facilitate security analysts to quickly understand attack paths and key threat points, and significantly improve the automation and accuracy of threat intelligence analysis.

An electronic device comprises a hallucination detection system. The hallucination detection system can execute a hallucination detection method. The hallucination detection method comprises implementing a conversion step, an authenticity detection step, and a result output step. The conversion step comprises: generating a plurality of output wordnets according to the output text. The authenticity detection step comprises: generating multiple pieces of authenticity information by a trained graph embedding model. Each of the multiple pieces of the authenticity information indicates whether one of the output wordnets is determined to be non-hallucinated or hallucinated by the graph embedding model. The result output step comprises: generating result of hallucination detection according to the multiple pieces of the authenticity information. The graph embedding model is trained through a plurality of positive wordnets and a plurality of negative wordnets generated from domain knowledge data.

The application relates to the technical field of industrial manufacturing, and discloses a method and system for improving the completion parameters of understanding user input, which comprises the following steps: inputting an industrial standardized file, generating a semantic label, reasoning through the industrial standardized file and the semantic label, obtaining an optimized industrial standardized file, and obtaining an industrial rule set and a text parameter set input by a user. The application realizes accurate semantic label generation based on a WordNet synonym set, solves the polysemy ambiguity problem, calculates the similarity between a candidate parameter and a user conversation through an improved Levenshtein distance algorithm and a compensation mechanism, accurately captures the potential demand of the user, improves the consistency with the actual input intention of the user, multiplies the initial weight value, the click rate and the time decay factor to sort the candidate parameters, balances the instant production demand and the historical experience in the industrial scene, and significantly improves the accuracy, adaptability, efficiency and interpretability in four dimensions.

The application discloses a zero sample event detection method based on a text entailment recognition model, and comprises the following steps: S1, obtaining initial text data from a NYT corpus and performing pretreatment, and obtaining separate sentence texts after the treatment; S2, using a WordNet to expand seed keywords; S3, using the expanded keyword set to screen the text data, when a text sentence contains keywords corresponding to a certain event type, the sentence has a considerable probability to express the event type; S4, using a text entailment recognition model to mark the data screened in the step S3; S5, further using the text entailment recognition model to screen the data obtained in the step S4; S6, using the data obtained in the step S5 to train the text entailment recognition model, and using the trained model to perform event detection on an ACE data set. The application focuses on optimizing the application of the text entailment recognition model in zero sample event extraction.