85 results about "Semantic knowledge base" patented technology

Related free-text documents, a corpus, are used to empirically derive a semantic knowledge base through a method in which documents are segmented into unique sentences, and then used to define sentential propositions which are arranged in a knowledge hierarchy. The method takes compound natural language sentences and transforms them to simple sentences by a process that is a part of the invention. A knowledge editor enables a domain expert using the methods of the invention to map the sentences in the corpus to sentential proposition(s). The resulting knowledge base can be used to semantically analyze documents in data mining and decision support applications, and can assist word processors or speech recognition devices. The invention is illustrated in connection with radiology reports, but it has wide applicability.

The invention discloses a method for detecting and correcting error on the text after voice recognition, for the purpose of orderly detecting and correcting the sentence errors on the text after voicerecognition. The method comprises the steps of respectively creating a second syntactic knowledge base, a second semantic knowledge base and a second contextual knowledge base belonging to the fieldof the sentence on the text after voice recognition, locating heavy duty words on the text according to the second syntactic knowledge base and the second semantic knowledge base, extracting an example of the located heavy duty words based on the second contextual knowledge base, detecting the errors in the sentence based on a first semantic knowledge base and the extracted example and correctingthe wrong words which are detected based on the first semantic knowledge base and the extracted example. The method of the invention can exactly detect and correct the errors on the text after voice recognition to promote the efficiency of voice recognition and realize the free man-machine communication.

Novel tools and techniques for generating and/or implementing an applied semantic knowledgebase. Some tools allow for data integration into coherent, semantically connected networks and for generation of sets of query-based models describing complex functional relationships as sub-networks. In an aspect, an applied semantic knowledgebase may comprise collections of SPARQL network queries describing a specific set of sub-network relationships and their applicable ranges for each element in the query.

The invention belongs to the information retrieval technology and provides a full-text retrieval system based on semantic analysis of relevant words. The full-text retrieval system based on semantic analysis of the relevant words comprises an inquiry information receiving module, a concept semantic analysis module based on the relevant words, a semantic knowledge base module, a retrieval module, an index database, an index module, a theme semantic analysis module based on the relevant words, a result set processing module and a data server. The full-text retrieval system based on semantic analysis of the relevant words is based on the improvement on a traditional Internet search engine, and by the adoption of the system, concept semantic analysis based on the relevant words and theme semantic analysis based on the relevant words of a document can be achieved, and users can obtain search results which are more accurate, more comprehensive and more intelligent.

The invention provides a model driven based imaging design method for an embedded industrial real-time system, and belongs to the technical field of embedded application software and industrial control. The method comprises steps as follows: an embedded industrial real-time field imaging meta model is recognized and constructed; a semantic knowledge base of the imaging meta model is constructed; a model mapping rule system based on the semantic knowledge base is formulated; a visible source model design platform is established; and a model conversion environment which is compatible to various target systems is provided. By means of the method, new system application software can be re-customized by modifying the source model according to requirements of embedded products and changes of application characteristics, and the method has good applicability, maintainability and reusability, can effectively improve the developing efficiency of an embedded industrial terminal, and is real, feasible and suitable for industrial development of embedded products.

The invention discloses a hierarchical semantic tree construction method and system for language understanding. The method mainly comprises the steps as follows: segmenting terms of a statement and loading a semantic knowledge base; recognizing all nodes of the statement according to an LV rule, and recognizing the level of the nodes according to semantic knowledge and term positions and collocations; generating a special node by punctuation at the end of the statement, and taking the special node as a root node of a semantic tree; merging the nodes according to generated node information, recognizing semantic side chunks of the statement, and taking a level-0 semantic side as a child node to be hung on the root node; circularly traversing all child nodes of the statement till no low-level semantic side exists, and taking the child nodes as leaf nodes to be hung on the child node. According to the hierarchical semantic tree construction method and system, under the condition that no syntactic resource exists, the semantic structure tree is obtained through semantic information and the term positions and collocations only, so that a computer can enter a deep semantic layer of a natural language, various processing of the natural language can be finished on the basis of understanding, the first step of semantic understanding of the natural language is realized, and the hierarchical semantic tree construction method and system can be applied to information retrieval, automatic abstraction, machine translation, text categorization, information filtration and the like.

The invention provides a moving path hybrid forecasting method oriented to a data sparse environment. The moving path hybrid forecasting method comprises the steps of: acquiring mobile position data information; processing data , wherein data preprocessing and data semantic analysis are carried out on the data; constructing a semantic knowledge base, wherein original trajectory data is subjected to rich semantic transformation and fusion processing, so as to construct the semantic knowledge base; constructing a hybrid online prediction model which is based on the semantic knowledge base and established on the basis of forward pattern similarity degree matching calculation and a high-order Markov model; and outputting a predicted path, wherein a trajectory fragment to be predicted is input into the hybrid online prediction model for prediction, and the predicted path is output. The moving path hybrid forecasting method effectively overcomes the problem of pattern matching failure caused by data sparse condition, significantly improves the accuracy of path prediction, and satisfies the requirements on real-time performance, high efficiency, predictability and the like of mobile service application.

The invention discloses a building method for a semantic knowledge base based on a dictionary. The method comprises the following steps that a knowledge database based on a dictionary semantic knowledge expression model is built; semantic knowledge paraphrased by semantic items are extracted according to a semantic knowledge extraction rule; the semantic item mapping for extracting the semantic knowledge is realized; the rest semantic knowledge paraphrased by the semantic items are extracted. The method provided by the invention can be used for flexibly and fast building the knowledge base containing rich semantics from the dictionary, and in addition, the knowledge expressed in the knowledge base has universality and authority. The invention simultaneously provides a deep semantic knowledge expression method and semantic similarity calculation method for semantic information processing of natural languages, wide application values are realized in the natural language processing field such as the information retrieval field, and the retrieval based on the semantics instead of keywords can be realized, so that the deep semantic retrieval result closer to the requirements of the user can be obtained.

The present invention describes a system, method and computer program which uses ontology method as basis, and can be used for representing and processing data/knowledge so as to solve technical problem in a sematic processing module. The basic components of said semantic processing module include a sematic knowledge base, an ontology knowledge base and/or an expert knowledge base. The solved problem can include a user's technical problem or an invention problem. Besides, said invention also provides the concrete steps of the invented method.

The invention discloses an interactive search method and device. The interactive search method includes the steps that firstly, inquiry information is acquired; secondly, based on historical search logs relevant to the inquiry information and a preset semantic knowledge base, intention clarification information of the inquiry information is generated and displayed on a client side page where the inquiry information is located. According to the interactive search method and device, based on the historical search logs relevant to the inquiry information and the preset semantic knowledge base, the intention clarification information of the inquiry information is generated and displayed to users through the client side page, the requirements of the users can be effectively clarified, the users can conveniently acquire information and content which are really needed, and then search experience of the users is improved.

The invention discloses a language entity relationship analysis method and relates to the field of natural language processing. Complete solution integral computing is carried out on input language strings by the method so as to select the optimal semantic tree. The invention further provides a machine translation device and a machine translation method based on the language entity relationship analysis method. The translation device comprises a semantic library module, a language entity relationship analyzer and a target language generator. The invention provides a novel language processing module. In a program, a complete language logic framework is established through the grasp of all natural language logics and the full utilization of combination explosion, the core problem of combination explosion in language is basically solved, and the accuracy and the translation speed can be obviously improved. The system does not have the massive production rules of a system of rules or the massive alignment corpora and the corresponding deep processing resources of a statistic system, thereby having remarkable advantages in engineering. A reliable basis can also be provided to various natural language applications.

The invention relates to a data processing method and device. The data processing method includes the steps of on the basis of a semantic knowledge base, constructing semantic graphs of question sentences; according to the semantic graphs, determining the answer types of the question sentences; according to the answer types, determining candidate semantic sub-graphs from the semantic graphs; according to the candidate semantic sub-graphs, determining one or more candidate triads; according to the question sentences, determining question triads; on the basis of the similarity between each candidate triad of one or more candidate triads and the question triads, determining answers to the question sentences. By using the data processing method and device, the answers to the question sentencescan be fast and reliably determined according to the semantic knowledge base.

The method is applied to the field of urban planning management. The invention relates to a BIM and 3D GIS fusion method for planning management characterized by comprising the following steps of: firstly, constructing a semantic knowledge base so as to quickly extract BIM data of a construction engineering design scheme and implant the BIM data into current situation three-dimensional geographicinformation data under a semantic retrieval condition; adopting each building component monomer in the three-dimensional layer as an independent control object, calculating the implantation range of the current situation model of the corresponding area in the scheme by utilizing an existing space analysis algorithm according to the area block range where the BIM scheme is located, building the current situation model of the area by automatically hiding the scheme, and implanting BIM data into the current situation GIS data. The method is oriented to the service requirements of urban planning management, and is a set of efficient and complete BIM and GIS fusion technology method.

The invention discloses a semantic knowledge base construction method. The method comprises the steps of firstly constructing a seed semantic knowledge base, then realizing the semantic interconnection of the semantic knowledge base and an industry knowledge base, then realizing the robot intelligence upgrading through the background semantic logic construction during the user interaction process,and then enriching and expanding the semantic knowledge base according to the current industry knowledge base. According to the method, by constructing the semantic knowledge base, the association and the butt joint of the newly constructed knowledge base and the semantic knowledge base are achieved, the problem that a conventional knowledge base is constructed from zero and the general knowledgelacking problem that the colloquial expressions of the written languages are not matched are solved, finally the more accurate user question answering is achieved, the intelligence level of a robot is greatly improved, and the customer experience degree is improved.

Updating a semantic knowledge base derived from sentences obtained from users during the process of creating their documents could improve the comprehensiveness of a semantic knowledge base, and permit the timely acquisition and dissemination of new knowledge. The present invention attains faster updating of semantic knowledge by transmitting unknown sentences collected from users, distributed over a computer network. The user inputs a sentence while creating a document in a particular knowledge domain. If a sentence is not part of the knowledge domain, it is transmitted over the network to the central knowledge source, where it is analyzed and the corresponding sentence and semantic proposition(s) added to the knowledge base as needed. The invention is able to disseminate the knowledge, i.e. propositions, which belong to the knowledge domain, to clients using either a “push” or “pull” methodology.

The invention provides an intelligent manufacturing workshop digital twinning model construction method and system, and the method comprises the steps: 1, configuring sensing equipment in an intelligent manufacturing workshop, collecting the production data of a manufacturing site, and carrying out the deep fusion and analysis, and forming a manufacturing workshop real-time database; 2, constructing a manufacturing system semantic knowledge base based on the real-time manufacturing data and cross-domain manufacturing process expert knowledge; and 3, constructing a digital twinning model of theintelligent workshop based on the manufacturing workshop real-time database and the manufacturing workshop semantic knowledge base. The method and system aim to solve the problems that in the existing digital twin model construction process, manufacturing resource modeling is not comprehensive, and manufacturing system modeling is not aggregated.

The invention relates to a word sense disambiguation method which comprises the following steps of acquiring a to-be-processed text, determining a to-be-disambiguated word in the to-be-processed text, a preceding text of the to-be-disambiguated word and a following text of the to-be-disambiguated word, searching a plurality of definition interpretations corresponding to the to-be-disambiguated word from a preset semantic knowledge base, vectorizing the plurality of definition interpretations, obtaining a definition matrix, constructing a corresponding classifier based on the definition matrix, inputting the preceding text, the following text and the to-be-disambiguated word into the classifier, obtaining the predicted candidate words of the to-be-disambiguated word, replacing the to-be-disambiguated word with the predicted candidate words, and obtaining a disambiguated standard text. In addition, the invention also relates to a word sense disambiguation method and device, an apparatus and a storage medium. According to the present invention, the problem that the accuracy of word sense disambiguation is not high enough can be solved.

Disclosed is an optimization decision-making method of an industrial process fusing domain knowledge and multi-source data. The method comprises the steps of: acquiring the domain knowledge of the industrial process by using probability soft logic, and building an domain rule knowledge base of the industrial process; fusing multi-source data semantics and multi-source data features to form a new semantic knowledge representation of the industrial process, and constructing a semantic knowledge base of the industrial process; under a posteriori regularization framework, utilizing the domain rule knowledge base of the industrial process and the semantic knowledge base of the industrial process to obtain an optimization decision-making model embedded with the domain rule knowledge and obtain a posteriori distribution model; and migrating knowledge in the optimization decision-making model embedded with the domain rule knowledge into the posteriori distribution model through the knowledge distillation technology.

The invention discloses a disambiguation method and disambiguation equipment of an abbreviated word. The disambiguation method includes: for the abbreviated word in to-be-processed text, finding full-name entities which are in a co-occurrence semantics knowledge library and correspond to the abbreviated word; in a case where the multiple full-name entities are found, retrieving, for each full-nameentity, reference entities which appear in the to-be-processed text and have association relationships with the full-name entity in the co-occurrence semantics knowledge library; calculating co-occurrence probability of the full-name entities on the basis of co-occurrence support probability TF-IDF between the reference entities and the full-name entities; and selecting one of the multiple full-name entities on the basis of the co-occurrence probability to use the same as a full-name entity corresponding to the abbreviated word, wherein the TF-IDF equals the product of co-occurrence frequencyTF of the reference entities and the full-name entities and inverse document frequency IDF of the reference entities and the full-name entities.

The invention discloses a machine learning-based power grid infrastructure project data knowledge graph construction method, and the method comprises the following steps: extracting information in unstructured text data and semi-structured table data of a power grid infrastructure project by utilizing a dependency relationship model and a bidirectional long-short-term memory artificial neural network hybrid model; mining the valuable information contained in texts in the data through knowledge fusion, and then constructing the capital construction project data knowledge graph. According to the graph, natural languages in different types of files are converted into nodes and relationships in a semantic knowledge base, and intelligent search and associated information recommendation functions can be realized.