337 results about "Semantic relationship" patented technology

A method and system for automatically extracting relations between concepts included in electronic text is described. Aspects the exemplary embodiment include a semantic network comprising a plurality of lemmas that are grouped into synsets representing concepts, each of the synsets having a corresponding sense, and a plurality of links connected between the synsets that represent semantic relations between the synsets. The semantic network further includes semantic information comprising at least one of: 1) an expanded set of semantic relation links representing: hierarchical semantic relations, synset / corpus semantic relations verb / subject semantic relations, verb / direct object semantic relations, and fine grain / coarse grain semantic relationship; 2) a hierarchical category tree having a plurality of categories, wherein each of the categories contains a group of one or more synsets and a set of attributes, wherein the set of attributes of each of the categories are associated with each of the synsets in the respective category; and 3) a plurality of domains, wherein one or more of the domains is associated with at least a portion of the synsets, wherein each domain adds information regarding a linguistic context in which the corresponding synset is used in a language. A linguistic engine uses the semantic network to performing semantic disambiguation on the electronic text using one or more of the expanded set of semantic relation links, the hierarchical category tree, and the plurality of domains to assign a respective one of the senses to elements in the electronic text independently from contextual reference.

A method of disambiguating user intent in conversational interactions for information retrieval is disclosed. The method includes providing access to a set of content items with metadata describing the content items and providing access to structural knowledge showing semantic relationships and links among the content items. The method further includes providing a user preference signature, receiving a first input from the user that is intended by the user to identify at least one desired content item, and determining an ambiguity index of the first input. If the ambiguity index is high, the method determines a query input based on the first input and at least one of the structural knowledge, the user preference signature, a location of the user, and the time of the first input and selects a content item based on comparing the query input and the metadata associated with the content item.

A method and system for automatically extracting relations between concepts included in electronic text is described. Aspects the exemplary embodiment include a semantic network comprising a plurality of lemmas that are grouped into synsets representing concepts, each of the synsets having a corresponding sense, and a plurality of links connected between the synsets that represent semantic relations between the synsets. The semantic network further includes semantic information comprising at least one of: 1) an expanded set of semantic relation links representing: hierarchical semantic relations, synset / corpus semantic relations verb / subject semantic relations, verb / direct object semantic relations, and fine grain / coarse grain semantic relationship; 2) a hierarchical category tree having a plurality of categories, wherein each of the categories contains a group of one or more synsets and a set of attributes, wherein the set of attributes of each of the categories are associated with each of the synsets in the respective category; and 3) a plurality of domains, wherein one or more of the domains is associated with at least a portion of the synsets, wherein each domain adds information regarding a linguistic context in which the corresponding synset is used in a language. A linguistic engine uses the semantic network to performing semantic disambiguation on the electronic text using one or more of the expanded set of semantic relation links, the hierarchical category tree, and the plurality of domains to assign a respective one of the senses to elements in the electronic text independently from contextual reference.

It is an object to highly accurately perform semantic relationship extraction from text data by performing supervised learning of multiple classes using an existing thesaurus as a correct answer. Concerning any pair of words in a text, a plurality of kinds of similarities are calculated and a feature vector including the similarities as elements is generated. A label indicating a classification of a semantic relationship is given to pairs of words on the basis of the thesaurus. Data for semantic relationship identification is learned as an identification problem of multiple classes from the feature vector and the label. Identification of an inter-semantic relationship of two words is performed according to the data for semantic relationship identification.

Embodiments of the present invention provide techniques for searching enterprise data using semantic information associated with enterprise applications. One or more searchable data definitions that describe searchable data associated with one or more enterprise applications may identify semantic relationships among searchable data in the application. An ontology for the application that describes semantic relationships among data associated with the application may be generated from the searchable data definitions. The ontology may be used to execute search queries and provide search results that include or result from semantic relationships among the searched data.

A method and system for automatically creating an image product based on assets stored in a user database. A number of stored digital media files are analyzed to determine their semantic relationship to an event and are classified according to requirements and semantic rules for generating the image product. These requirements and rules refine the association between the assets and the event for which the image product is generated. The assets which best meet the requirements and rules of the image product are ranked and the highest ranking ones are included in the image product.

A multimedia archive description scheme is provided for characterizing a multimedia archive having records and associated record descriptions. The multimedia archive description scheme provides a data structure which relates records by similarity measures. The principle data structure in the multimedia archive description scheme is a cluster. A cluster includes one or more attributes of the records in the archive and can include one or more cluster relationships. Cluster attributes can include feature space attributes, semantic attributes, media attributes and meta attributes of the records in the archive. The cluster relationships can relate records to clusters or clusters to clusters. Cluster relationships can include feature space (syntactic) relationships, semantic relationships, media relationships and meta relationships. The multimedia archive description scheme provides an efficient form for describing a collection of records.

A semantic service system may includes a tagging facility for collecting references to resources thereby providing tagged resources; a semantic database for storing the collected references; a semantic associating facility for determining semantic relationships among the references and the resources thereby providing semantic objects; and a user interface for presenting context-based views of one or more of the semantic objects, the references, and the tagged resources. A handheld tagging device includes a computing facility with a camera for acquiring images of resources and a motion sensor for detecting a tagging gesture.

Systems and methods of a knowledge management networking are disclosed here. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, of hosting a web-space having a plurality of objects, the plurality of objects to include one or more of, representations of a set of users, a set of web-items, and a set of nets; wherein a net of the set of nets is a subset of the web-space comprising a sub-plurality of the plurality of objects. One embodiment can include, tracking an explicit relationship between a first set of at least two objects of the set of objects; the explicit relationship to be pre-determined by a user of the set of users, identifying an implicit relationship between a second set of at least two objects of the set of objects; the implicit relationship to be identified based on a semantic relationship between the at least two objects, and determining a default set of privacy rules governing access between the at least two objects based on one or more of the identified explicit relationship and the implicit relationship.

An invention for generating standard description records from multimedia information. The invention utilizes fundamental entity-relation models for the Generic AV DS that classify the entities, the entity attributes, and the relationships in relevant types to describe visual data. It also involves classification of entity attributes into syntactic and semantic attributes. Syntactic attributes can be categorized into different levels: type / technique, global distribution, local structure, and global composition. Semantic attributes can be likewise discretely categorized: generic object, generic scene, specific object, specific scene, abstract object, and abstract scene. The invention further classifies entity relationships into syntactic / semantic categories. Syntactic relationship categories include spatial, temporal, and visual categories. Semantic relationship categories include lexical and predicative categories. Spatial and temporal relationships can be topological or directional; visual relationships can be global, local, or composition; lexical relationships can be synonymy, antonymy, hyponymy / hypernymy, or meronymy / holonymy; and predicative relationships can be actions (events) or states.

Determining a semantic relationship is disclosed. Source content is received. Cluster analysis is performed at least in part by using at least a portion of the source content. At least a portion of a result of the cluster analysis is used to determine the semantic relationship between two or more content elements comprising the source content.

The disclosed embodiments provide a system and method for using modified Latent Semantic Analysis techniques to structure data for efficient search and display. The present invention creates a hierarchy of clustered documents, representing the topics of a domain corpus, through a process of optimal agglomerative clustering. The output from a search query is displayed in a fisheye view corresponding to the hierarchy of clustered documents. The fisheye view may link to a two-dimensional self-organizing map that represents semantic relationships between documents.

A computer user interface may be used to create and maintain semantic relationships between data objects on a computer system. Multiple sets of data objects identified by user selections, queries, searches, or other criteria may be presented in display panes on the user interface. Users may then select data objects via the user interface in order to view or create new relationships between the data objects. Based on the selection of the data objects and a determination of the associated data object types, a set of semantic relationships applicable to the selected data object or objects may be retrieved and displayed in the user interface. A user may subsequently select and create an appropriate semantic relationship between data objects, thereby not only establishing an association between the objects, but providing a specific and useful meaning to the association.

A method and system for analyzing a corpus of data artifacts is disclosed. The method comprises obtaining, by a computer, a semantic representation of the data artifacts, where the semantic representation indicates (1) entities identified in the data artifacts, and (2) semantic relationships among the entities as indicated by the data artifacts. The method further comprises clustering the data artifacts into clusters of semantically related data artifacts based on the semantic representation and inferring additional semantic relationships between pairs of the entities. The inferring comprises applying, on a cluster-by-cluster basis, a multi-tiered network of inference engines to a portion of the semantic representation corresponding to the cluster, where the multi-tiered network of inference engines includes a domain-independent inference tier and a domain-specific inference tier.

A method and system are disclosed for creating a hyperlink together with an associated semantic link between a source entity, and a target entity. The source entity includes descriptive text. The system includes means for selecting text within the source entity, and means for selecting the target entity. Also, means are provided for selecting a type of semantic link, and means are provided for creating a hyperlink between said selected text and said target entity. The system further includes means for creating a semantic link of said type between said source entity and said target entity, including means for including in the hyperlink a reference to said semantic link. In the preferred embodiment of the invention, the means for selecting the type of semantic link includes means for prompting a user to select from among a plurality of candidate types of semantic links.

The invention relates to an attention dual-layer LSTM-based long text emotional tendency analysis method, belongs to the field of natural language processing and machine learning, and mainly aims to solve the problem of difficulty in accurately judging an emotional tendency of a full text due to long comment length of the long text, discrete distribution of positive and negative emotional featuresand different emotional semantic contribution degrees of sentences. The method comprises the steps of firstly learning sentence-level emotional vector representation by utilizing LSTM; secondly coding semantic relationships between emotional semantics of all the sentences in a document and the sentences by adopting bidirectional LSTM, and based on an attention mechanism, performing weight allocation on the sentences with different emotional semantic contribution degrees; and finally, weighting the sentence-level emotional vector representation to obtain document-level emotional vector representation of the long text, and through a Softmax layer, obtaining the emotional tendency of the long text. An experiment is performed in Yelp2015 and IMDb film comment corpora; and a result shows thata relatively good classification effect can be achieved, so that the emotional classification correctness is further improved.

A computer natural language translation system inputs source language text and outputs target language text. The target language text is generated from the source language text using stored translation data generated from examples of source and corresponding target language texts. The stored translation data includes a plurality of translation components, each having surface data representative of the order of occurrence of language units in the component; dependency data related to the semantic relationship between language units in the component; and link data linking dependency data of language components of the source language with corresponding dependency data of language components of the target language. The surface data of the source language is used in analyzing the source language text, and the surface date of the target language is used in generating the target language text. The dependency data and link data is used in transforming the analysis of the source text into an analysis for the target language.

The invention discloses a sentiment classification method of commodity comment data based on a convolutional neutral network. Focusing on the problem that according to traditional word vector representation methods, only vocabulary characteristics and syntax characteristics are extracted, while the semantic relationships among words are ignored, and aiming at the purpose that the classification effects of traditional classification algorithms can be improved, the sentiment classification method of the commodity comment data based on the convolutional neutral network is put forward. Initially the word vector characteristics of each piece of comment data are extracted by means of a CBOW model, the semantic relationships among the words and the representation capability of the word vector characteristics are enhanced, then a sentiment classification model is built by means of the deep learning network model, the convolutional neutral network model, and the sentiment classification performance of the comment data is improved. According to the technical scheme, the method has the advantages of being simple and fast, and the sentiment classification performance of the comment data can be well improved.

The invention discloses a method and a device for constructing a patent data knowledge map. The method comprises the following steps: obtaining patent data of an existing patent database, preprocessing the patent data to unify a patent data format, and segmenting the patent data after being merged with the same type to obtain the segmentation data of each type of patent data; performing knowledgeextraction of preprocessed patent data, performing data cleaning of word segmentation data of each type of patent data to obtain the corresponding subject original file, extracting keywords to obtainsubject words, and constructing a patent subject database for each type of patent data; defining the entity of patent data, determining the subject of patent data, identifying the entity and subject of patent according to the general knowledge map, mining the semantic relationship between the entity and the subject, and constructing the patent data knowledge map.

Methods and computer-readable media for associating words or groups of words distilled from content, such as reported speech or an attitude report, of a document to form semantic relationships collectively used to generate a semantic representation of the content are provided. Semantic representations may include elements identified or parsed from a text portion of the content, the elements of which may be associated with other elements that share a semantic relationship, such as an agent, location, or topic relationship. Relationships may also be developed by associating one element that is in relation to, or is about, another element, thereby allowing for rapid and effective comparison of associations found in a semantic representation with associations derived from queries. The semantic relationships may be determined based on semantic information, such as potential meanings and grammatical functions of each element within the text portion of the content.

The invention provides an individuation searching method based on book domain ontology, belonging to an individuation network searching service. The individuation searching method comprises the following steps of: establishing the domain ontology, introducing a collaborative filtering idea, and adding semantic relationships which present influences among users; analyzing and processing logs and establishing a user model based on user interests and preferences; computing individuation scoring through spreading Activation (SA) based on the user model and the domain ontology; in addition, rearranging searched results, rearranging the results returned by a primary search engine according to an individuation scoring sequence from high to low, and returning to the users. Through introducing the collaborative filtering idea into the domain ontology, establishing the user model which presents user interest changes timely, and analyzing user requirements accurately by the SA, the invention effectively eradicates ambiguity of key words and greatly improves satisfaction degree of the users on the searched results.

The invention discloses an emotion data analysis method. The method comprises the steps of obtaining to-be-analyzed data; performing word segmentation processing on the to-be-analyzed data to obtain word segmentation feature data; generating distributed word vectors used for determining semantic relationships among words in the word segmentation feature data according to the word segmentation feature data; according to the feature data of the words in the distributed word vectors, obtaining complete data vectors of the to-be-analyzed data; and performing classified calculation on the complete data vectors according to a learning model to obtain emotion attributes used for determining the to-be-analyzed data. Meanwhile, the invention furthermore discloses an emotion data analysis apparatus.

The invention provides an ontology-driven geographic information retrieval system and an ontology-driven geographic information retrieval method, and a corresponding geographic space event ontology library and a construction method thereof. By utilizing an ontology to describe relationships among semantics, timing sequence, space and other aspect of a geographic space event, the technical problems of geographic space event ontology construction, ontology space relationship and semantics relationship inference and ontology-based intelligent retrieval and user-based personalized geographic service are solved; and an ontology-driven geographic space event information retrieval mechanism is established and a corresponding search engine is created, so automatic search, on-demand filtering, intelligent organization and personalized display of the geographic space event information are realized.

The invention discloses a cross-modal retrieval method based on a cyclic generation antagonistic network. The method designs a novel dual-channel cyclic generation antagonistic neural network and establishes semantic correlation of cross-modal data by training the neural network. Given different modal data can flow bi-directionally in the network, each modal data can generate another modal data through a group of generative antagonistic networks, and the generated data can be used as the input of the next group of generative antagonistic networks, so that the bi-directional loop generation ofdata can be realized, and the network continuously learns the semantic relationship among the cross-modal data. In order to improve the efficiency of retrieval, The method also uses threshold functionand approximation function to approximate the result of the middle layer of the generator to the corresponding binary hash code, and designs a variety of constraints to ensure the same mode, the similarity and cross-mode of the same kind of data, and the difference of data between classes, thus further improving the accuracy and stability of retrieval.

The invention discloses an internet-oriented place name extraction and standardization method. According to the method, aimed at existing ways and structural features of place names and addresses in an internet page, identification is performed based on state administrative division information and a nationwide basic place name and address library by utilizing identification rules and dynamic relationships of the place names and the addresses, a multi-stage place name and address expression model and an extraction method are researched, automatic identification, extraction and standardization of Chinese place names and addresses in text information of the internet page are realized with reference to a place name and address standard model through superior-subordinate semantic relationships of the place names and the addresses in the text, and a technical basis is provided for spatial positioning of related geographic information of geographic entities, events and the like.

The invention discloses a construction method of a knowledge graph library for network fault analysis and application thereof, and relates to the technical field of network communication. The method comprises: using an intelligent method to carry out data acquisition on communication network data; constructing ontology libraries in different fields, constructing a global ontology library by adopting a data fusion and mapping technology according to the ontology libraries in different fields, and finally constructing a knowledge map library with consistent semantic relationships of various datasources through entity alignment, entity linking and knowledge calculation in sequence. The constructed knowledge graph library is used for retrieving possible reasons for causing the network fault,suggestions of a fault solution are provided for a user, rapid positioning and solution of the network fault are facilitated, the fault processing efficiency is improved, and meanwhile the influence of human factors is reduced.

The invention discloses a medical entity relationship extraction method based on feature fusion, and the method comprises the steps: enabling entities in a knowledge base to be aligned to medical corpora through a remote supervision and rule combination method, and constructing an entity pair sentence set; performing word-level vector coding on the sentences based on a convolutional neural networkmodel to obtain overall feature vector representation of the sentences; extracting features in left and right subtree directions on the shortest dependency path of the sentences by using a recurrentneural network respectively, and performing splicing operation; and fusing the sentence overall features and the dependency syntax features which are extracted from the two parts respectively, and performing final relation extraction on the obtained fusion features. According to the method, on the premise that a dependency syntax structure is utilized; entity type characteristics capable of expressing entity relationship types among entities are introduced; the position features and the overall features of the sentences are integrated with the dependency syntactic features, the semantic relationship between the sentences is better learned, the interference of noise data on medical entity relationship extraction is reduced, and the accuracy of medical entity relationship extraction can be improved to a certain extent.