13 results about "Vector space model" patented technology

The invention belongs to the technical field of artificial intelligence, and relates to an LSH-based retrieval enhancement generation method and device, equipment and a storage medium thereof. Carrying out serialization processing; sampling processing is carried out through the vector space model, and high-dimensional embedded vector representation is generated; inputting the high-dimensional embedded vector representation into a vector database, and carrying out matching calculation; carrying out binary code valuation conversion on a matching calculation result by adopting an LSH algorithm to obtain binary code values corresponding to all the retrieved texts after conversion; according to the binary code value, an expected retrieved text is screened out; combining and sorting the retrieval input text and the expected retrieved text to generate a to-be-enhanced text; and inputting the text to be enhanced into the text enhancement generator to generate an enhanced retrieval feedback text. The method is applied to webpage search, question and answer systems and recommendation system scenes in the field of financial services or medical services, and a faster and more accurate retrieval feedback result is provided for retrieval personnel.

A computer-implemented method for detecting attribute value pairs from corpus data using a computer comprising a processor and a computer readable medium comprising instructions executable by the processor to at least: receive the corpus data comprising at least one pair; detect a layout template of the at least one pair; measure the merit of the layout template by determining at least one of (a) relative magnitudes of content probabilities based on a probability of the contents of an attribute cell and a probability of a corresponding value cell, (b) the validity of a name-value pair, or (c) the pointwise mutual information of a frequency matrix M corresponding to a sparse vector capturing context information of a word; and output detected attribute value pairs.

The application provides a public policy participation evaluation method and system based on an LDA and a vector space model, and the method comprises the following steps: obtaining target public policy documents and target dynamic data; adopting a TF-IDF algorithm to perform word segmentation processing on the target public policy documents and the target dynamic data respectively, so as to obtain a document feature word segmentation set and a data feature word segmentation set; extracting semantic information hidden in the target public policy documents and the target dynamic data respectively through an LDA model, so as to obtain a document-data matrix; constructing a vector space model based on the document feature word segmentation set and the data feature word segmentation set, calculating the similarity between the target public policy documents and the target dynamic data, so as to obtain a similarity matrix; and performing linear weighting processing on the document-data matrix and the similarity matrix. The application can effectively evaluate the citizen participation in public policy, and also solves the problems of data sparseness and semantic loss, and improves the accuracy and effectiveness of the evaluation.

The invention discloses a medical knowledge graph-driven diagnosis and treatment decision support system, and relates to the technical field of medical knowledge graph data processing, and the method comprises the steps: obtaining a clinical data flow of a patient, mapping the clinical data flow into an updating instruction for a pre-constructed medical knowledge graph, and dynamically updating the attribute data of entity nodes and relation edges; generating a vectorization representation of the node by taking the target entity node as an original point according to the attribute data of the relation edge between the target entity node and the associated node; screening nodes to be compounded according to a preset rule, and calculating a composite vector between the target core node and the vectorized representation of the nodes to be compounded; and finally, generating a clinical state evaluation result according to the mathematical characteristics of the composite vector. According to the method, the complex medical relationship is quantified into the computable vector space model, dynamic, objective and quantitative evaluation and risk early warning of the clinical state of the patient are achieved, and the accuracy and foresight of diagnosis and treatment decisions are remarkably improved.

The application provides a kind of complex product design knowledge service method and system based on knowledge graph, comprising: through text tree structure and improved TFIDF algorithm combination N-gram strategy, reference document is semantically annotated;Knowledge carding template and rule extraction method are used to extract design process knowledge, generate knowledge triple and build knowledge graph in Neo4j graph database;On this basis, product design knowledge super network is built, and the semantic matching degree between tasks and knowledge is calculated by combining super edge correlation degree and information entropy modified vector space model;Finally, the query push of process knowledge is realized based on Cypher language, and the sorting recommendation of reference knowledge is completed by multidimensional comprehensive evaluation method.The application realizes the task-oriented knowledge structured expression, semantic correlation calculation and intelligent push in complex product design, and improves the enterprise knowledge utilization efficiency and design decision quality.

The invention belongs to the technical field of natural language processing, and relates to a knowledge association method based on semantic similarity and co-occurrence word matching degree. According to the method, keyword extraction is carried out on the text by utilizing a jieba word segmentation library, high-frequency and high-distinction-degree feature words are highlighted in combination with a classical TF-IDF algorithm, the matching degree of semantic distribution is quantified through a vector space model by adopting a measurement standard of cosine similarity, and the co-occurrence condition of vocabularies in the text is particularly concerned; and the semantic association information is used as an important index for measuring the text association degree, so that surface semantic association information possibly omitted by single semantic analysis is made up. According to the method, the semantic similarity, the co-occurrence word matching degree and the vocabulary co-occurrence condition are comprehensively considered, so that the correlation degree between the texts can be more comprehensively and accurately measured, the information retrieval accuracy is effectively improved, meanwhile, the calculation complexity is reduced, and more accurate and comprehensive information services are provided for users.

The application discloses a kind of based on BLS's NAVTEX message semantic automatic classification method, to NAVTEX message data application data cleaning and word segmentation operation, and proposed semantic label to give message label category, specifically as the summary of the message content contained in navigation warning, defined 7 kinds of semantic labels.By the application, redundant and complex original message data can be effectively simplified and the foundation for subsequent classification tasks is laid, making the trained features more representative.The application extracts features using the vector space model on the preprocessed NAVTEX message data, which has the advantages of easy calculation and simple structure.Then, based on BLS, the semantic classification is performed, which has the advantages of short training time and good classification effect, effectively alleviating the labor intensity of the crew and improving the emergency response speed and efficiency of responding to safety information, thereby improving the safety navigation performance of the ship.

The application discloses an AI Agent intelligent adaptation skill calculation method. The method comprises the following steps: constructing a three-dimensional unified vector space model of AI Agent capability image, skill demand label and task constraint condition; performing hard rule preliminary screening, filtering the skills that do not meet the strong constraint condition based on resource, time delay, permission dependence and necessity label hit constraint; calculating the discrete label accurate matching score, the Embedding semantic similarity, the capability adaptation weighted score and the quality stability correction score of the skills that pass the preliminary screening, wherein the upper limit of the single capability adaptation ratio is capped at 1; performing weighted summation on the scores of the above four dimensions to obtain a comprehensive matching total score, and scheduling based on the threshold and the total score ranking; and updating the AI Agent proficiency and the historical success rate in reverse after calling is completed. The application improves the accuracy, efficiency and scene adaptability of AI Agent skill adaptation.

The invention provides a knowledge graph-based complex product design knowledge service method and system. The method comprises the steps of performing semantic annotation on a reference document through a text tree structure and an improved TFIDF algorithm in combination with an N-gram strategy; designing process knowledge is extracted by utilizing a knowledge combing template and a rule extraction method, a knowledge triple is generated, and a knowledge graph in a Neo4j graph database is constructed; on this basis, a product design knowledge super network is constructed, and the semantic matching degree between the task and the knowledge is calculated by combining the hyperedge correlation degree and the vector space model corrected by the information entropy; and finally, querying and pushing process knowledge based on a Cypher language, and finishing sorting recommendation of reference knowledge through a multi-dimensional comprehensive evaluation method. According to the method, task-oriented knowledge structured expression, semantic association degree calculation and intelligent pushing in complex product design are realized, and the enterprise knowledge utilization efficiency and the design decision quality are improved.

The application provides an automatic reading method and system for antibody identification results based on a vector space model, which comprises the following steps: constructing an antibody specificity vector model, mapping the antigen spectrum corresponding to each antibody specificity in an antibody identification pedigree table into an antibody specificity vector with a fixed dimension; mapping the reaction results of antibody identification of a serum sample to be tested into a to-be-tested vector through the antibody specificity vector model; calculating the similarity between the to-be-tested vector and the antibody specificity vector; and performing reading based on reading logic according to the similarity calculation results to obtain antibody identification results. The antibody identification results can be automatically read by calculating the vector similarity, the antibody identification can be realized quickly, accurately and standardly, and the work efficiency is improved significantly, and the manpower is liberated.

A method for automated therapy discovery includes: accessing a corpus of scientific publications; compiling a population of semantic concepts from the corpus of scientific publications into a vector space model; deriving domains of concepts in the vector space model based on proximity to domain descriptors in the vector space model; deriving association scores and action characteristics between connected concepts, based on proximity and action descriptors in the vector space model; generating a semantic network; receiving a query for a target concept and a target domain at a research portal; isolating a set of edges between a target node and a subset of nodes; identifying subsets of concepts along the set of edges; generating hypotheses for directions and magnitudes of effects of subsets of concepts on the target concept based on association scores and action characteristics stored in connections along the set of edges; and returning hypotheses to the research portal.

A method for automated therapy discovery includes: accessing a corpus of scientific publications; compiling a population of semantic concepts from the corpus of scientific publications into a vector space model; deriving domains of concepts in the vector space model based on proximity to domain descriptors in the vector space model; deriving association scores and action characteristics between connected concepts, based on proximity and action descriptors in the vector space model; generating a semantic network; receiving a query for a target concept and a target domain at a research portal; isolating a set of edges between a target node and a subset of nodes; identifying subsets of concepts along the set of edges; generating hypotheses for directions and magnitudes of effects of subsets of concepts on the target concept based on association scores and action characteristics stored in connections along the set of edges; and returning hypotheses to the research portal.

This invention relates to a personal identification information classification method based on an information vector space model, belonging to the field of network information security technology. This method first extracts characteristic textual information transmitted in network traffic through network traffic analysis and transforms it into a dataset containing service, location, information, and frequency feature dimensions. Then, the dataset description is transformed into a sample space for text classification. Next, a generative model based on three-layer Bayesian methods is established in conjunction with the text classification model. Model parameters are obtained through data sample training, automatically representing services-locations and their transmitted information as vectors, and obtaining the probability distributions between services-locations, information, and types. Finally, new services are inferred by calculating the probability distributions between each service-location, information, and type. This method can more precisely describe the distribution characteristics of different information semantics transmitted in network traffic, achieving the goal of accurately classifying personal identification information.