139 results about "Semantic query" patented technology

A platform-independent process for data retrieval from ontology-oriented data systems over computer networks through a flexible system and method of query paraphrasing. The present invention uses a “common ontology” that is not tied to any particular data system. Thus, each client computer issues queries to a target data system in the common ontology. Of course, the target data system will not be able to directly process the query (as it is not in its local ontology). Instead, the query is first paraphrased back from the common ontology into local ontology by taking the semantic query, passing it through a query paraphraser, and then sending the paraphrased query to the data system. Once it is paraphrased successfully, the target data system can process it and produce a result using local ontology. The result may then be sent from the data system to an answer paraphraser for paraphrasing, and the paraphrased answer may be returned to its original query issuer and on to the client.

Semantically linked pages are queried based on a user supplied interest vector. The interest vector provides a weight for presenting results from the query of pages. The interest vectors are used to calculate the importance of pages of a query based on the weight of semantic links associated with the page known as page rank indicators. Optionally, the calculation is augmented by other page ranking algorithms. An indication of the resulting pages is displayed according to the calculated importance. Preferably the calculation utilizes a dot product of page rank and user interest vectors.

In the realm of managing relational databases, a system that uses both the data in a relational database and domain knowledge in ontologies to return semantically relevant results to a user's query. Broadly contemplated herein, in essence, is a system that bridges a semantic gap between queries users want to express and queries that can be answered by the database using domain knowledge contained in ontologies. In accordance with a preferred embodiment of the present invention, such a system extends relational databases with the ability to answer semantic queries that are represented in SPARQL, an emerging Semantic Web query language. Particularly, users may express their queries in SPARQL, based on a semantic model of the data, and they get back semantically relevant results. Also broadly contemplated herein is the definition of different categories of results that are semantically relevant to a user's query and an effective retrieval of such results.

A search engine for retrieving documents from a database comprising a semantic document editor that is operable to allow a user to edit an existing document by creating one or more searchable compound word that contains information contextually relevant to the contents of the document. The editor is operable to associate the one or more created compound words with the document to produce an enhanced document having the compounds words associated therewith. A database is provided for storing a plurality of enhanced documents and a semantic query editor is provided that is operable to enable a searcher to address the database of enhanced documents with a query. The query editor is operable to receive the query and convert it into one or more compound search word that contains contextually relevant information. A search module is provided that is operable to receive the searchable compound words and to locate the relevant enhanced documents that have compound words associated with the document matching the searchable compound words. An output means is provided for presenting any located documents to the searcher.

The embodiments of the present invention provide a computer-implemented method and system for translating user keywords into semantic queries based on domain vocabulary. The system receives the user keywords and search for the concepts. The concepts are transformed into a connected graph. The user keywords are translated into precise access paths based on the information relationship described in conceptual entity relationship models and then converts these paths into logic based queries. It bridges the semantic gap between user keywords and logic based structured queries. It enables users to interact with the semantic system by articulating the information in a structured query language. It improves the relevance of search results by incorporating semantic technology to drive the mechanics of the search solution.

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.

A semantic query over an RDF database is received with RDF database statistics and access methods for evaluating triple patterns in the query. The semantic query is expressed as a parse tree containing triple patterns and logical relationships among the triple patterns. The parse tree and access methods create a data flow graph containing a plurality of triple pattern and access method pair nodes connected by a plurality of edges, and an optimal flow tree through the data flow graph is determined such that costs are minimized and all triple patterns in the semantic query are contained in the optimal flow tree. A structure independent execution tree defining a sequence of evaluation through the optimal flow tree is created and is transformed into a database structure dependent query plan. This is used to create an SQL query that is used to evaluate the semantic query over the RDF database.

In a method of semantic querying in a peer-to-peer network, an item of information is mapped into a semantic vector based on the latent semantic indexing algorithm or any IR algorithms that can derive a vector representation. The semantic vector is associated with an address index as a key pair. The key pair is stored in an overlay network formed from the peer-to-peer network such that the stored key pair is proximally located to at least one other key pair having a similar semantic vector.

UDDI is not capable of handling semantic markups for Web services due to its flat data model and limited search capabilities. The present invention provides semantic service description and matchmaking with registries that conforms to UDDI specification. Specifically, the present invention stores complex semantic markups in UDDI data model and uses that information to perform semantic query processing. The present invention does not require any modification to the existing UDDI registries. The add-on modules reside only on clients who wish to take advantage of semantic capabilities. This approach is completely backward compatible and can integrate seamlessly into existing UDDI infrastructure.

The embodiment of the invention provides a method and a device for identifying query intention based on a mixed strategy. The method comprises the following steps: acquiring a query sentence of a user; obtaining a first candidate query intention by using a semantic rule template according to a query statement of a user; according to the user's query statement, using the machine learning model to obtain the second candidate query intention; matching the first candidate query intent with the second candidate query intent; if the first candidate query intent and the second candidate query intentmatch successfully, outputting as semantic query intent. The first candidate query intent, the second candidate query intent, and the semantic query intent are at least one. The embodiment of the invention combines the semantic rule template and the machine learning model to carry out query intention identification, can better understand the query intention of a user's query sentence, and improvesthe semantic identification ability and the understanding effect.

A system and method for responding to a query. An original query is received. A first semantic query and a second semantic query are extracted from the information of the original query. The first semantic query is transformed, based upon semantic analysis, into a numeric model query. The second semantic query is submitted to a semantic analyzer and the numeric model query is submitted to a numeric model analyzer. A response for the second semantic query and a response for the numeric model query are integrated into an answer for the original query.

The invention discloses an image search method, and relates to the field of information search. The method comprises the following steps of: acquiring semantic description of an object to be queried; building an image body library according to the type of images in an image library; performing semantic query in the image body library according to the semantic description of the object to be queried to acquire image examples similar to the object to be queried and semantic descriptions related to the image examples; and sequencing the similarities of the image examples to acquire and output the image example with the highest similarity and corresponding semantic description. Through the method, the image example which is most similar to the object to be queried and the corresponding semantic description can be obtained so as to improve the recall ratio and precision ratio of search and image search efficiency and expand the search range.

Systems, methods and computer-storage media are provided for identifying query formulation suggestions in response to receiving a search query. A portion of a search query is received. Query formulation suggestions are identified by semantically analyzing the search query. The query formulation suggestions are used to further formulate the received search query. The query formulation suggestions include semantic-pattern-based query suggestions that are derived from semantic query patterns, one or more entities, and information associated with these entities. The query formulation suggestions are transmitted for presentation.

The invention discloses a device and a method for inquiring knowledge. The device comprises a domain knowledge database, a data extracting preprocessing module, a parallel data mining module, an inquiring engine module and a storage and computing module, the storage and computing module comprises a plurality of parallel storage and computing nodes, the data extracting preprocessing module, the parallel data mining module and the inquiring engine module are respectively connected with the domain knowledge database and the storage and computing module. The concept of KaaS (knowledge as a service) is proposed in the classificatory level of cloud computing services, and concepts analyzed from data sources and mined overall rules are updated into the domain knowledge database so as to provide semantic inquiry and knowledge services to the client side conveniently.

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 invention belongs to the field of big data processing technology and discloses a thematic knowledge automatic mining system and method. The system comprises an Internet-of-Things interface module,a semantic query module, a data mining module and a map aggregation and visualization module. The method comprises the steps that first, data collection and database establishment are performed, wherein data acquired based on a web ubiquitous network, data of monitoring sensors and all types of thematic data are collected, sorted out and stored into a thematic database; next, thematic knowledge mining is performed, wherein thematic knowledge is formed through ontology construction, semantic query and depth information mining, and a file is transmitted into an FTP file server; the thematic knowledge acquired from the FTP file server and a geographic base map are subjected to geographic correlation, and cartographic data is formed; surface format design, thematic chart design and surface finishing are performed; and finally a thematic map is output. According to the thematic knowledge automatic mining system and method, by aid of a machine learning algorithm under big data, huge territorial resource information can be processed and analyzed through an efficient and accurate method.

The invention discloses a digital vector sea and land map fusion method and a digital vector sea and land map fusion system. The digital vector sea and land map fusion system comprises a domain ontology construction part and a semantic fusion part, wherein the domain ontology construction part is used for constructing geographic ontologies in various domains in a sea map, a land map and a sea and land integrated map and constructing a mapping relation among the geographic ontologies in the various domains; and the semantic fusion part is used for performing semantic fusion on the sea map, the land map and the sea and land integrated map based on the ontologies and the mapping relation thereof which are constructed by the domain ontology construction part to realize the classification, hierarchical fusion and semantic intercommunication of geographic elements among the domains, and providing cross-semantic query of the geographic elements, geographic ontology query and browse, and free fusion and conversion among the sea map, the land map and the sea and land integrated map. By the digital vector sea and land map fusion method and the digital vector sea and land map fusion system, sea and land integrated management is realized.

The invention belongs to the technical field of software engineering and intelligent software development, and particularly relates to an API knowledge graph construction method based on a reference document. The method comprises the steps that a basic skeleton structure of an API element is obtained through document structure analysis, and function description and use mode description are recognized by automatically classifying sentences in descriptive content of the API element; performing common concept identification and linking among the description information of different API elements to realize internal knowledge fusion; and performing concept linkage between the common concept in the API element description information and the related technical concepts in the general knowledge graph to realize external knowledge fusion. The constructed API knowledge graph comprises API packets, classes, interfaces, methods, attributes, abnormalities, method parameters, return values and relations among the elements. According to the constructed API knowledge graph, intelligent applications such as API knowledge semantic query, automatic question answering, auxiliary code understanding andcode recommendation are supported through structured knowledge representation.

A computer-implemented method, computer-implemented system, and a computer program product for answering a database-based query of a computerized database system. The method includes: generating a canonical individual ind′ with respect to a role and a concept, for an existential restriction in an ontology used in the computerized database system; converting implicit data in original data into explicit data using the canonical individual ind′ and the ontology; and retrieving an answer meeting all query conditions in a query from the original data and the converted explicit data. The computer-implemented system includes: a canonical individual generation unit; a data conversion engine; and a query unit.

The invention provides an automatic knowledge graph construction method based on multi-source heterogeneous power equipment data, and aims at structured data such as table data, various types of table data are transferred into an Excel table, and a data conversion rule and a data processing rule are defined, so that mapping from the structured data to a knowledge graph is completed; automatic extraction from a database to a knowledge base is performed, information extraction is performed on power text and webpage information by mainly adopting a deep learning method aiming at unstructured text data, an entity identification relation extraction process is completed, SPO triple extraction on the unstructured data is achieved, and finally integrated data is stored into Neo4j in a form of an RDF (Resource Description Framework) file. Visual display of the knowledge graph is achieved through a Neo4j graph database, and semantic query is performed by using a cypher query language.

The invention discloses an individuation privacy protection method for sensing a semantic query based on a road network, and relates to the technical field of services specially used for users or terminal locations. The method comprises the following steps that A, a mobile user sends a query request to a credible anonymous server; B, a knowledge base of the credible anonymous server firstly converts the real identifier of the user into a fake name; C, an anonymous engine in the credible anonymous server carries out anonymity processing on a service request based on an individuation-sensitivity anonymous model; D, an anonymous road segment set RS corresponding to the user u is found in an idlist by the credible anonymous server, and the query request carrying the anonymous road segment set is sent to a service provider. The method can solve the problem that individual privacy disclosure happens to the user in the road network under the conditions of providing location-based services and combining the semantic query, the identifier, location and sensitive information of the user are protected, and the user can enjoy the fast and safe location-based services.