33 results about "Semantic integration" patented technology

The invention relates to a field device information management system based on semantics and OPC UA and belongs to the combined field of the semantic network and the industrial Internet of things. Theintegral configuration of the system comprises a sensing layer, a network layer, a semantic layer and an application layer, wherein the sensing layer comprises a bottom field device and an OPC UA server, the network layer comprises each network system accessed by the OPC UA, the semantic layer comprises an OPC UA device information acquisition end, a device information management domain knowledgeontology model, a device information semantic annotation module, a semantic reasoning and query module, an ontology database and a semantic rule file, and the application layer comprises a device running state management module, a device operator management module, a device machine account management module, a device spare part management module, a device maintenance management module and a device repair management module. The management system is advantaged in that semantic integration of the heterogeneous device information is realized, through semantic reasoning and query, the relatively rich semantic knowledge in the field can be acquired, and needs of each functional module of the system are satisfied.

The invention discloses a system and a method for detecting access control strategy collisions in a collaborative environment. Firstly the problem of semantic isomerism of access control strategy used by each region in the collaborative environment among mutually-operated regions based on role mapping is solved, and on the basis, the problem of the access control strategy collisions in the collaborative environment is solved; therefore, the system and the method are particularly suitable for use in a collaborative environment with semantic isomerism. The technology adopts an improved strategy integration algorithm based on semantic integration and a strategy collision detection algorithm based on subgraph isomorphism as main algorithms. The main activities of the detection technology for the access control strategy collision in the collaborative environment comprise strategy pre-processing, multi-region strategy integration and strategy collision detection activity. The invention can detect the access control strategy collisions caused by mutual operation among regions by role mapping, so as to avoid the safety problems of unauthorized access and the like.

The invention provides a cross-media information analysis and retrieval method, which comprises the following steps of: performing semantic integration processing on multimode information; performing expansion according to a probability latent semantic analysis model to obtain a multilayer continuous probability latent semantic analysis model for processing a continuous feature vector; learning the multilayer-continuous probability latent semantic analysis model by adopting an asymmetric learning method, and calculating the visual feature vector distribution of an image, the visual feature vector distribution of an audio and topic probability distribution; submitting a training set and a tested media object which serves as a retrieval example by a user, and calculating intra-mode and inter-mode initial similarity values of the image and the audio in the retrieval sample; constructing a propagation model, and updating the intra-mode and inter-mode similarity values according to the propagation model; and performing secondary retrieval according to the updated similarity values.

The invention discloses a data mining method based on multi-source heterogeneous patent data semantic integration. The data mining method sequentially comprises the following steps that (1) global patent data and inter-translation dictionaries are taken as data sources, and ontology study is conducted, and a patent global ontology is constructed; (2) according to corresponding attributes, a function used for judging the individual information similarity degree in each data source is constructed; (3) according to the constructed similarity degree functions, similarity information of individuals in different data sources is calculated under the guidance of the patent global ontology; (4) data mining is conducted according to the similarity information. According to the data mining method based on multi-source heterogeneous patent data semantic integration, the extracting precision of concepts and concept relations is greatly improved, patent global ontology study is achieved, and data mining efficiency and accuracy degree are greatly improved.

The invention relates to a semantic-based data service blending method, which resolves semantic character and high efficiency of the traditional Web service use and resolves supports for the Web service structured data, by encapsulation of local application, the existing data resources released via different forms in the Internet, and semantic integration and release of the Web services. The semantic-based data service blending method effectively realizes semantic integration and releases to the local application, the structured data sources and the Web services, and thereby users can high-efficiently use the existing Web services and data resources. The invention has the advantages that the invention mainly functions in semantic integration to the local application, the structured data sources and the Web services by a system, and performs Mash-up data service assembly on the base, and finally releases the assembled semantic data services. In addition, the invention realizes seamless connection for data sharing during services assembly.

The invention belongs to the technical field of computer vision, and particularly discloses a weak supervision semantic segmentation method and an application. The method comprises that: a pre-trainedsemantic erasure type region expansion classification network used for weak supervision semantic segmentation is adopted, first-stage feature extraction and high-level semantic integration classification are sequentially carried out on a picture to be semantically segmented, and a first class response graph corresponding to the picture is obtained; an area with high responsivity in the first category response diagram is erased, and second-stage high-level semantic integration classification is performed on the erased category response diagram to obtain a second category response diagram; andthe corresponding positions of the first category response diagram and the second category response diagram are added and fused to obtain a fused category response diagram, and background threshold segmentation processing is performed on the fused category response diagram to obtain a category segmentation region diagram. The erasure type region expansion classification network structure is greatly simplified, the expansion effect is good, the region expansion exploration efficiency is greatly improved, and the weak supervision semantic segmentation effect is further enhanced.

The invention is related to the AJAX field and the data mining and integrating technology field, and discloses an asynchronous interactive data mining system based on operation flow. The data mining system comprises a client-side using GWT-EXT for construction of AJAX user interface, and a server-side being arranged on a web container and including the following modules: a distributed database module based on semantic integration, an operator parameter module, an user management module and a Rapid Miner kernel module. The invention is characterized in having no use for installing software and easy operation.

The invention provides an emotion classification method. The emotion classification method comprises the steps of obtaining a word embedding matrix corresponding to a context and a word embedding matrix corresponding to a target word; according to the word embedding matrix corresponding to the context, the word embedding matrix corresponding to the target word and the first semantic activation model, obtaining context representation with enhanced target word meaning and target word representation with enhanced context semantics; obtaining context representation after semantic selection according to the context representation of the target word semantic enhancement, the target word representation of the context semantic enhancement and the semantic selection model; according to the semantic integration model, extracting syntactic representation in a syntactic dependency tree corresponding to the target sentence; and obtaining an emotion classification result corresponding to the target word according to the context representation, the syntax representation and the second semantic activation model after semantic selection. Compared with the prior art, the semantic information related to the target word in the context is fully captured, and the relationship among the context, the target word and the syntax is comprehensively considered, so that the accuracy of sentiment classification is improved.

The invention discloses a semantic decoupling-based no-proposal time sequence language positioning method, relates to the field of cross-modal content retrieval, solves the technical problems existing in an existing no-proposal positioning mechanism, and comprises the following steps: decoupling an original video, decomposing to obtain multi-level visual feature streams, and in each visual feature stream, performing in-modal and cross-modal context modeling on the basis of a candidate proposal method, so that the advantages of the candidate proposal method are kept, a cross-semantic integration mechanism is adopted, multi-modal context features after information interaction are integrated into fine-grained features, and finally, using a proposal-free positioning mechanism to directly solve the starting position and the ending position of a target video clip. The boundary positioning mechanism of the non-proposal positioning method is utilized, and meanwhile, the intermediate features are used for assisting the content understanding of the video, so that the defects of the existing non-proposal positioning mechanism are overcome.

The invention belongs to the field of spatial data integration and plan combination and relates to an instance-based semantic integration and multi-plan spatial data conflict detection method. The method includes steps of implementation of instance-based multi-plan spatial data classification system integration and automatic detection of multi-plan spatial data conflict patches. The instance-based semantic integration method is provided to realize integration of different land planning classification systems, and then a spatial analysis method is applied to automatic detection of land plots in land classification conflict in multiple plans. The method is applicable to quick detection and discovery of difference patches among multi-plan data in 'multi-plan integration' and 'urban space planning', and multi-plan difference patch discovery efficiency is improved.

The invention discloses a viewpoint tracking method based on geometrical reconstruction and semantic integration. The viewpoint tracking method is characterized by comprising the steps of 1, establishing the visual characteristic set of subgraphs, 2, integrating semantic characteristics, 3, active learning algorithm, 4, optimization updating, and 5, ranking and orderly linking the subgraphs to obtain a viewpoint tracking path. The viewpoint tracking method is capable of quickly and accurately detecting various remarkable regions of images, improving the accuracy of viewpoint tracking and then improving the prediction capacity of a viewpoint transfer route.

The invention discloses a traceable medical record classification method, and the method comprises the steps: carrying out the semantic integration on the context information of all the characters in the medical record by using the language model, and finally suppressing the influence of meaningless characters on the classification result through a gating mechanism, so the signal-to-noise ratio of intermediate data of the classification model is improved, and then the classification accuracy of medical record texts is improved. Meanwhile, meaningful characters for the classification result can be traced back through a gating value. Finally, while high-accuracy medical record classification is achieved, the gating values of the characters are output, and tracing of the character basis of medical record classification is achieved through the gating values of the characters.