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51 results about "SPARQL" patented technology

SPARQL (pronounced "sparkle", a recursive acronym for SPARQL Protocol and RDF Query Language) is an RDF query language—that is, a semantic query language for databases—able to retrieve and manipulate data stored in Resource Description Framework (RDF) format. It was made a standard by the RDF Data Access Working Group (DAWG) of the World Wide Web Consortium, and is recognized as one of the key technologies of the semantic web. On 15 January 2008, SPARQL 1.0 became an official W3C Recommendation, and SPARQL 1.1 in March, 2013.

Volume radio direction finde (RDF) data distribution type query processing method based on Hadoop

The invention discloses a volume radio direction finde (RDF) data distribution type query processing method based on a Hadoop platform and belongs to the field of the computers. The method mainly comprises the following steps. Step a, RDF data can be uploaded to a hadoop distributed file system (HDFS), data can be read by a MapReduce frame of the Hadoop platform and stored in a distributed database HBase. Step b, a simple protocol and RDF query language (SPARQL) inquiry statement section which is provided by a user can be preprocessed. Statements can be analyzed and extracted a prefix statement, an outcome variable and a picture-model sub sentence. Step c, prefix characters of the picture-model sub sentence can be restored, and the restored picture-model sub sentence can be converted into a tree model. Step d, the tree model can be resolved. Tree joints can be traversed in a bottom-up method and a left-to-right method and inquiry plans can be generated, wherein the inquiry plans are matched with each joints. The final inquiry plans can be sent to the Hadoop platform. Step e, data can be read form the HBase through the MapReduce frame. Distributed query can be implemented according to the inquiry plans. Eventually, the outcome variable can be returned to an inquiry result.
Owner:CHONGQING UNIV

A construction method of a literature compilation life history question and answer system based on a knowledge graph

ActiveCN109766417AImprove the efficiency of acquiring specific knowledgeAccurate analysisNeural architecturesSpecial data processing applicationsQuestion analysisTight frame
The invention discloses a construction method of a literature history question and answer system based on a knowledge graph, which comprises the following steps of constructing the knowledge graph ofa literature history vertical field by taking structural data related to Chinese literature history as a basis and combining a literature history ontology structure created from top to bottom; designing a semantic analysis framework which comprises two user question analysis modules characterized in that one module is based on regularization and rules, and the other module is based on a neural network; organizing the results obtained through problem analysis into corresponding SPARQL query statements, and searching the corresponding results for in the constructed knowledge graph; organizing the result as a reply, and returning the reply to the user; designing a webpage side and a WeChat official account service as a window for interaction between the system and a user; designing a user uses a log and feedback collection module, wherein the related data is used for iteratively training a neural network model, and the generalization capability of the model is enhanced. According to the present invention, the natural language query of a user can be directly processed, an accurate result is returned, and the method plays an important role in improving the knowledge acquisition efficiency, promoting Chinese culture research and the like.
Owner:ZHEJIANG UNIV

SPARQL parallel query method facing large-scale RDF graph data

The invention relates to RDF (Resource Description Framework) graph data processing. In order to provide a high-efficiency parallel query processing method for the large-scale RDF graph data, reduce read-write times of disks and improve query efficiency, the invention adopts the technical scheme that an SPARQL (Simple Protocol And Rdf Query Language) parallel query method facing the large-scale RDF graph data comprises the following steps: 1, describing the RDF graph data by using a bulk synchronous parallel (BSP) model; 2, marking by using URIs (Uniform Resource Identifiers) of resources; 3, for each triple in an RDF graph data set, i.e. a subject calculating unit S, a predicate P and an object calculating unit O, establishing a directed edge e from the subject calculating unit S to the object calculating unit O, using an URI of the predicate P as a mark of the e and storing related information of the e in a local data field of the subject calculating unit S; 4, for each edge e in the step 3, using an URIr as a mark of an er; 5, acquiring an query request q0 submitted by a user; 6, selecting different propagation paths to carry out propagation; 7, estimating a quantity of information contained in each clause in the qi-1 by utilizing a greedy algorithm; 8, repeatedly carrying out the steps 6 and 7 until all the clauses are bound. The SPARQL parallel query method is mainly applied to graph data processing.
Owner:TIANJIN UNIV

System and method for generating SPARQL query statements in field of medical treatment

The invention discloses a system and method for generating SPARQL query statements in the medical field, and belongs to the field of machine translation. The system comprises a generator which takes aquery template library and a knowledge base as input and is used for extracting entities and attributes from the knowledge base and filling the entities and attributes into a Chinese question template and an SPARQL query template to generate a training set; the word segmentation module is used for carrying out word segmentation processing on the Chinese questions in the training set and forwarding a word segmentation result to the learner; performing word segmentation processing on the target Chinese question, and forwarding a word segmentation result to an interpreter; the learner is used for training the neural network model according to the Chinese training set after word segmentation to obtain a trained model; and the interpreter is used for predicting the target Chinese questions after word segmentation by using the trained neural network model to obtain predicted SPARQL query statements, so that complex statistical and manual models are not used any more, and the medical healthquery Chinese questions are directly converted into the SPARQL query statements.
Owner:HUAZHONG UNIV OF SCI & TECH
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