132 results about "Semantic expansion" patented technology

The invention relates to an establishing and retrieval method for a characteristic matrix of a semantically based Web document, belonging to the information retrieve technical field. During the process of establishing the characteristic matrix for the Web document, position information and particular expression form information are added into an index process of a prior LSA model by utilization of the particular position information and the particular expression form information in the Web document, thereby the prior LSA method is effectively improved. The retrieval process is as follows: firstly, semantic expansion of a concept in a query sentence is performed according to a body; secondly, a query vector is generated according to the query concept and an enlarged concept of the query concept, and the similarity of the query concept and the enlarged concept can be taken into consideration by a vector value, thereby semantic deletion of the prior LSA model is made up in a certain extent. The establishing and retrieval method for the characteristic matrix of tbe semantically based Web document has the advantages of scientific index and effective retrieve of unstructured document information, realization of retrieve of unstructured information in all locations at any moment, and assistance of convenient and in-time acquisition of required information of a user.

The invention discloses a multi-source heterogeneous data semantic integrated model constructed based on domain ontology. The multi-source heterogeneous data semantic integration model comprises a local ontology constructing module, a domain ontology merging module and a semantic inquiring dynamic propagation and protocol module. A multi-source heterogeneous data semantic integrating method comprises the following steps: constructing the domain ontology through an ontology merging technique and establishing a semantic mapping relation between a data source and the local ontology as well as between the local ontology and the domain ontology; combining the complementary advantage of social label and ontology on knowledge representation and performing inquiring protocol and propagation for the semantic inquiring request of the user, thereby generating a formal semantic inquiring statement; respectively inquiring a plurality of data sources, performing duplicate removal and aggregation optimization on the inquiring result, and lastly feeding back to the user. The invention provides an aggregation heterogeneous data semantic integration model constructed based on domain ontology and the method through the construction and mapping of the domain ontology, semantic inquiring propagation and result aggregation optimization.

The invention discloses a road network-based spatio-temporal trajectory clustering method. The method comprises the steps of data acquisition, spatio-temporal trajectory expression, spatio-temporal similarity measurement, sub-trajectory clustering and clustering result output. Through a trajectory recording device, spatio-temporal trajectory data of a moving object is acquired; a spatio-temporal trajectory model is built based on trajectory expression of a line segment; a trajectory file is output after linear interpolation and semantic expansion and is subjected to feature point selection; sub-trajectories are divided through feature points to perform trajectory reconstruction; a network distance between the sub-trajectories is calculated as a spatio-temporal similarity measurement basis; sub-trajectory clustering is realized by applying a label propagation algorithm; and finally a clustering result is output. According to the method, similarity and abnormal features in the spatio-temporal trajectory data can be extracted by performing clustering analysis on various spatio-temporal trajectory data, and meaningful trajectory modes in the data can be conveniently discovered.

The invention belongs to the field of multimedia information retrieval and relates to a method for realizing thesaurus-based query expansion and sort in image retrieval. The method comprises a WordNet-based English word semantic similarity metric algorithm, a HowNet-based Chinese word semantic similarity metric algorithm, an expansion rule-based query expansion word selection and optimization algorithm and a retrieval result evaluation and optimization algorithm. In the method, an image search engine is improved by the relevant text processing method and the relevant semantic network dictionary; and the retrieval result is sorted through semantic expansion, user interaction and improved similarity measurement. Compared with the traditional method, the method has the advantages of high accuracy rate, high integrality and low space-time cost. The method has very important significance for performing high-efficiency image retrieval according to image high-layer semantic information and on the basis of a large-scale image data set, and has wide application value in the field of cross-linguistic and cross-media retrieval.

The present invention provides a method and system for identifying, retrieving and presenting electronic documents responsive to user queries. The three distinct inventive concepts are relevancy ranking of responsive documents based on component query technique; cross-lingual searching; and search expansion using analytics of initial results to derive and generate a modified query. Each of these inventions enhances document search and retrieval systems and the three solutions may be used separately or in any combination. The three inventions apply in layers above an underlying search system, controlling the submission of requests to the underlying system in support of received search requests, typically originating with an end user.Invention (I) involves use of either of two general types of enhanced OR operator that may be offered by the underlying search system. Processing of the content domain informs automated generation of elaborated queries using these operators for submitting to such an underlying system so as to yield improved relevance ranking.Invention (II) introduces a cross-lingual search-term suggestion capability driven by the structured data being searched, requiring no dictionaries or thesauri and no rule-based or statistical language modeling.Invention (III) introduces a semantic-expansion capability specifically utilizing the availability of language independent fields in the data being searched with natural language query terms. This invention is enhanced by incorporating the preceding two inventions.

The invention provides a method and a device for processing medical intelligent question and answer data, and relates to the technical field of intelligent questions and answers. The method includes extracting question keywords in user query question data; performing synonymy transformation, and determining synonym sets of the question keywords; performing matching lookup in a preset question andanswer knowledge base and a preset rule knowledge base according to the synonym sets of the question keywords; if succeeded, outputting answer result data corresponding to the question keywords and synonyms thereof in the synonym sets of the question keywords; if failed, subjecting the question keywords to semantic extension to acquire the question keywords and the synonym sets of hyponyms of thesynonyms; continuing matching lookup according to the question keywords and the synonym sets of the hyponyms of the synonyms, and generating a retrieval result list containing retrieval results; performing similarity calculation on the retrieval results in the retrieval result list to determine similarities among the retrieval results; sequencing and outputting the retrieval results according to the similarities of the retrieval results.

The invention provides a text search method. The text search method comprises the following steps that an inquiry statement is divided into a key semanteme and a key word according to the input inquiry statement, semantic extension based on the domain ontology is carried out according to the key semanteme so that the extended semanteme can be generated, an extended inquiry statement is generated according to the extended semanteme and the key word, similarity grading is carried out on the extended semanteme, semantic weighing is carried out on the extended inquiry statement, text search is carried out on the extended inquiry statement so as to obtain the corresponding inquiry result, and the searched inquiry result is displayed. According to the method, the search recall ratio and the precision ratio of a search engine can be increased in the text search process. The invention further provides a text search system.

The invention relates to an agricultural field ontology library based semantic retrieval system and method, belonging to the technical field of intelligent retrieval. In order to improve the accuracy and the efficiency of an agricultural field information semantic retrieval process, only the useful structured data in a webpage are extracted by using an information extraction technology and used as the basic resource for retrieving, thus the structural property and the accuracy of the retrieval data source are greatly ensured in the stage of the basic resource of data; and then the comprehensive and professional agricultural-industry oriented ontology library is established, the semantic extension and inference is carried out according to the inquiry request of the user on the basis of a semantic ontology inference engine through the participation of the user, and the natural language submitted by the user is processed or the extension result is returned to the user once again so that the weight of each ontology example in a semantic extension set can be determined accurately in the participation process of the user, the extended ontology example set meets the inquiry requirement of the user, and further the final retrieval precision and recall rate are improved.

The invention relates to a quick multi-keyword semantic sorting search method for protecting data privacy in cloud computing. A domain weighted scoring concept is introduced in document scoring, and keywords in different domains such as a title, an abstract and the like are endowed with different weights to be distinguished; a retrieval keyword is subjected to semantic expansion, semantic similarity is calculated, a three-factor sorting method is designed by combining the semantic similarity, the domain weighted scoring and a correlation score, and a cloud server can perform accurate sorting on search results and return a sorting result to a search user; and for the defect of low query efficiency of a searchable encryption scheme, a vector block segmentation marking matching algorithm is designed, and a document vector created by a vector space model is subjected to block segmentation to generate a marking vector with a relatively small dimension number. According to the method, the query efficiency can be improved, the index creation time can be shortened, and semantic ciphertext keyword search is realized.

The invention relates to a method and a system for designing a searchable encrypted cloud disc with fuzzy semantics. The method comprises the following steps: firstly, inputting a key pair to encrypt a file which is needed to be uploaded by a user through a client side; extracting keywords of the encrypted file, and carrying out semantic extension to build a synonym set; secondly, building a safety index to the built synonym set by a bloom filter, and uploading the safety index to a cloud storage server side; generating a keywords trap door according to the key input by the user by the client side, and submitting the trap door to the cloud storage server side; finally, searching for a corresponding file by the safety index according to the keywords trap door submitted by the user by the cloud storage server side, and returning the file back to the client side; decrypting the file returned by the cloud storage server side through the key pair by the client side. The method and the system are mainly used for providing the synonyms semantic fuzziness search function for the information search of the cipher text file, and supporting the search of many keywords, so that the file can be updated with higher efficiency.

The invention discloses a method for realizing semantic extension on a retrieval request. The method comprises the following steps of: receiving the retrieval request from a user; segmenting the retrieval request to generate keywords; conducting the semantic extension on the keywords based on a knowledge tree to generate an extended keyword combination; carrying out optimized matching combination query in combination with the a retrieval combination database on the keyword combination to obtain an effective retrieval combination; and combining the effective retrieval combination with the retrieval request inputted by the user to obtain an effective retrieval combination set. The method provided by the invention can realize the multi-dimensional semantic extension, and can rapidly collecting the large amount of multiple combination sets to the effective retrieval combination set through the optimized matching combination query, thereby realizing the organic combination of the full semantic extension with the real and meaningful retrieval combination, and effectively improving the accuracy of retrieval results.

The invention provides a keyword-based WEB server expansion search method. The method comprises that: a search system provided by a WEB server automatically expands out expanded words corresponding to keywords input by a user to perform auxiliary search, namely the search system of the WEB server automatically performs semantic expansion on the keywords searched by the user according to the expansion conditions so as to form new expansion conditions before searching and then returns the searched matched information texts to the user. By the method, the semantic expansion search performance ofthe WEB server can be enhanced, the expansion conditions can be flexibly selected, and the requirement of the user on flexibility of semantic information search can be met better.

The invention discloses a semantic-based multi-keyword sorting search privacy protection system and method, and relates to three entities: a cloud data owner, a cloud data user and a public cloud server. The cloud data owner performs grammar and lexical analysis on plaintext documents in a client to generate a keyword set, calculates domain weighted scores and correlation scores of keywords, generates plaintext document vectors, establishes document indexes through a hierarchical clustering algorithm, and reserves a relationship among the plaintext documents. The cloud data user performs semantic extension on input search keywords to generate a semantically extended word set, and performs vectorization and encryption to generate trap door query vectors. The public cloud server matches thetrap door query vectors with cluster center vectors. Therefore, the trap door query vectors are prevented from being matched with a large amount of ciphertext document vectors; and the retrieval efficiency is greatly improved.

A new-generation industry knowledge full-text search method includes: 1, segmentation dictionary setup, namely setting up a segmentation dictionary, and storing dictionary information into a database; 2, full index setup, namely reading, dividing words and analyzing existing full-text documents called 'knowledge point documents' to set up an index file; 3, increment index setup, namely processing newly added documents and updating an index file on a hard disk; 4, memory index setup and memory segmentation dictionary setup, namely reading segmentation dictionary data in a memory to set up a memory segmentation dictionary data structure; 5, full-text search, user question standardization, word dividing, semantic comprehension, semantic extension, candidate document acquisition and candidate document ordering, wherein segmentation dictionary setup is conducted during system initialization; full index setup includes reading all knowledge documents to fully set up a hard disk index file called 'index file' for short; increment index setup is conducted when full-text files are newly added; the three events are independent from a full-text retrieval module and in independent operation.

The invention provides a control method for calculating Chinese word semantic similarity. The method is combined with a method based on Chinese thesaurus and based on statistic. The method is characterized by comprising the following steps of: a, obtaining a word pair, and obtaining a corresponding feature vector according to the word pair; b, carrying out semantic expansion on the feature vector so as to generate an expanded feature vector; c, carrying out semantic mapping on the feature vector so as to generate a mapped feature vector; and d, calculating the word similarity according to the expanded feature vector and the mapped feature vector.

The invention belongs to the technical field of service computation, and relates to a service and tag recommendation method based on random walk. The method comprises a service recommendation step and a tag recommendation step, wherein the service recommendation step comprises the following process of: collecting the information of a Mashup service and an API (Application Program Interface) service, abstracting the relation between the Mashup service and the API service by association, sorting each tag after performing random walk on a co-occurrence network between the tags, and recommending the Mashup service through the association relation between the tag and the Mashup service and the Mashup semantic expansion method. The tag recommendation method comprises the following process of: searching the nearest neighbor according to the similarity of the API service description texts, and performing tag recommendation of the API service through the random walk and tag sorting method. The recommendation method can be used for improving the accuracy of the service searching result, and solving the problem of tag randomness.

The invention discloses a microblog topic detection and hotspot evaluation method based on semantic expansion, belongs to the field of text information processing, and particularly relates to microblog noisy data filtering and a microblog topic detection and topic hotspot evaluation method and system based on semantic expansion. The method comprises the steps of firstly giving out a microblog noisy data filtering method for filtering microblogs with low information content, then supplementing effective semantic information in microblog comments into microblog semanteme so as to improve the detection effect of microblog topics, finally carrying out microblog topic hotspot evaluation, and thus obtaining hot topics.

The invention provides a mass short message information filtering method based on semantic extension and can solve the short message feather sparsity problem. The method comprises the following steps that 1, an initial training sample set is built, and expansion is carried out for each sample of the training sample set on the basis of the context information; 2, the expanded training sample set is subjected to text preprocessing; 3, a theme feather dictionary is built on the basis of the training sample set subjected to the preprocessing; 4, each text of the training sample set is subjected to text expression in a hidden theme space; 5, an SVM (support vector machine) filter is built; 6, the text to be filtered is subjected to expansion and text preprocessing to be converted into a feather word set on the basis of the context information, is then subjected to text expression in the hidden theme space, and is filtered by a filter; and 7, novel samples are regularly collected, the word item probability distribution of the theme is updated in the existing hidden theme space, the novel samples are subjected to text expression, and the SVM filter is rebuilt.

The invention relates to a knowledge acquiring system field, in particular to a knowledge intelligent acquiring system of vegetable supply chains, aiming at designing a set of knowledge intelligent acquiring system of vegetable supply chains, realizing the effect knowledge spreading of the vegetable supply chains by means of the computer media, and particularly realizing the knowledge transfer in the field of the vegetable supply chains at home and aboard. The system takes the ontology model of the vegetable supply chains as a support and provides three retrieval modes, i.e. keyword retrieval, semantic extension retrieval and semantic retrieval based on the ontology model as a tool for acquiring the knowledge in the professional field. The system has high query efficiency and facilitates the participants of the vegetable supply chains and scientific researchers to acquire the professional knowledge.

The invention provides an information retrieval optimizing method based on domain ontology, comprising the steps of: obtaining query key words submitted by users via a retrieval interface of a retrieval system; performing lexeme expansion for the query key words submitted by users via domain ontology inference in a desired domain of users and according to the built domain ontology, so as to obtain one or more sets of new query strings; submitting the expanded query strings to one or more search engines for retrieval; performing repetition removal and re-sequencing integration for the results fed back by the various search engines; displaying the final result to the user via the retrieval interface. In the invention, the efficiency of the information retrieval relevant to the domain is improved by the lexeme advantage of the domain ontology.

The invention discloses a topic detecting system based on an atlas model. The topic detecting system comprises a preprocessing module, an atlas structure constructing module and a subgraph detecting module. The preprocessing module is used for preprocessing a corpus test set. The atlas structure constructing module is used for constructing an atlas structure for expressing an original model, the vertex of the atlas structure is a lexical item of a corpus file, the edges of the atlas structure are the relevance among word pairs, different weights are given to the relevance among the word pairs according to whether words are entity words or not, whether dependence relationships exist among the word pairs or not and the distances among the word pairs, and an undirected graph with the entity words as the atlas center is constructed. The subgraph detecting module is used for processing relevancy atlases to obtain vocabulary sets corresponding to all subgraphs, and then ranking and screening are conducted to obtain final results of all subtopics. By means of the topic detecting system based on the atlas model, implicit topics in a discrete text set can be automatically obtained, and the topic detecting system based on the atlas model can be applied to characteristic space dimensionality reduction, relevancy calculation, semantic extension and other related field of natural language processing.

The invention provides a semantic search method and device based on a knowledge graph, which can provide accurate personalized search for a user and can improve search efficiency and search accuracy.The method comprises the steps that keywords in a to-be-queried request input by a user are extracted, concept matching is conducted on each extracted keyword in an ontology library, and the to-be-queried request is used for querying pension institution resources; if the matching succeeds, ontology-based semantic extension is executed; if the execution fails, semantic extension is executed based on the knowledge graph; semantic similarity is determined based on user behaviors according to the historical behaviors of the user; and according to the determined semantic similarity and semantic extension result based on the user behaviors, scores of the user for the pension institution resources are predicted, and a plurality of pieces of pension institution resource information with high scores are returned to the user as a recommendation list. The invention relates to the field of search engines and computer information processing.

The invention discloses a water conservation domain information retrieval system and a water conservation domain information retrieval method based on semanteme. The system comprises an information acquisition and storage module for acquiring water conservation subject information from the Internet, storing the water conservation subject information into a water conservation subject information base and constructing an index, a semanteme body module for storing a water conservation domain body and a hownet semanteme body and finishing calculation for similarity of domain vocabularies, and a semanteme query processing module. The method comprises the following steps of: describing domain information by using a fuzzy resource description framework, and constructing the index for the water conservation information by adopting Lucene; constructing a water conservation domain body on the basis of a water conservation official document subject vocabulary table, and performing semanteme expansion on the water conservation professional vocabularies through a Jena inference engine according to the water conservation body to perform the semanteme expansion on general vocabularies on the basis of Hownet; and calculating the relevancy between an expanded vocabulary and a retrieval vocabulary by an improved hownet semanteme vocabulary similarity calculation method. By the system and the method, semanteme expansion is performed on the retrieval vocabulary, and the recall ratio and the precision ratio of the information are improved.

The present invention discloses a commodity name classification method and system based on semantic similarity expansion. The offline algorithm calculation of historical invoice data sets of commodities of existing categories is carried out to obtain a naive Bayesian model and a word vector of each type of commodities. According to different conditions, whether a commodity name word vector inputted by a merchant exists in the naive Bayesian model of the commodities of the existing categories is analyzed, if the naive Bayesian model does not have a uniterm after a commodity name participle, thecommodity name and existing commodity category keywords are subjected to word vector similarity calculation, the probability that the commodity name inputted by the merchant belongs to an existing commodity category is calculated, and a category corresponding to a maximum value is the category to which the commodity belongs. Experimental results show that the accuracy of the commodity category keyword similarity calculation based on semantic expansion is high, and a calculation result accords with common sense judgment.

The invention discloses a distributed searching method based on cloud computing. The method includes the steps that network files of various formats are crawled through a distributed web crawler; a document table format with a user-defined format is extracted through the files crawled by the distributed parallel extraction analysis crawler; the extracted document content is stored into a distributed database, and a document table database is established; an index table is established through the document table database, and a parallel computing technology is also adopted; an index table format is also of a user-defined format; index files are imported to an index database, and index data are provided for a searcher; a PageRank and optimized on-line sorting algorithm is adopted in search results. The distributed searching method based on cloud computing has the advantages that the distributed storage and computing characteristic is adopted, by the aid of the improved and optimized sorting algorithm, the search results are more accurate, and due to the fact that the semantic extension keyword technology is used, and the search results are richer.

The invention belongs to the technical field of network communication and computers, particularly relates to an intelligent interactive question and answer method, system and device based on a Markovlogic network, and aims to solve the problems that an intelligent question and answer system cannot effectively combine context and background in practical application, cannot feed back in real time and is low in efficiency. The method comprises the following steps: analyzing input information, extracting a structured tuple, and performing semantic expansion by adopting a domain knowledge map; Activating related rules in the domain knowledge map, assigning values to the evidence tuples by adopting an approximate reasoning and / or information input mode, and calculating the posterior probabilityof the candidate response information; And outputting a preset number of pieces of response information with high posterior probability. According to the method, context operation and domain uncertainty knowledge can be effectively fused, approximate deduction and user interaction are powerfully combined, an effective solution is truly provided, meanwhile, automatic induction of knowledge can beachieved, and labor and data cost is reduced.

The invention brings forward a question-answer knowledge base and an establishing method thereof, an intelligent question-answering method and system. The question-answer knowledge base comprises multiple knowledge points. Each knowledge point comprises one or more questions. At least a number of problems are with semantic expressions. Each semantic expression comprises one or more first-stage word classes. The first-stage word class comprises multiple related words. A part of first-stage word classes in semantic expressions of questions in at least partial number further comprises semantic segments. Each semantic segment comprises more than one second-stage word class including multiple related words. The question-answer knowledge base and the establishing method thereof, the intelligent question-answering method and system have following beneficial effects: time cost for semantic expansion when knowledge engineers write semantic expressions so that information search efficiency of the intelligent question-answering system is improved.

The invention discloses a method and a system for generating search engine query expansion terms. The method includes the following steps: acquiring network session log, and extracting query terms of all network users to obtain a query term corpus training library; training a term vector model with the aid of the query term corpus training library; receiving query text and inputting the query text to the trained term vector model to obtain expansion terms. Semantic expansion of the query terms is realized to further improve accuracy and increase recall rate of search engine query results, and the problems of no results and few results of search query are solved effectively.

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.