327 results about "Semantic search" patented technology

A system and method for automatic question answering is provided. Wherein the system includes: a user inputting module configured to receive question information; a question analyzing module configured to analyze the question information, and determine a set of keywords, a question type and a user intention type corresponding to the question information; a syntax retrieving and ranking module configured to retrieve, in a question and answer library and a category tree, answer candidates based on the question information, the set of keywords, the question type and the user intention type, determine a retrieval relevance between each of the answer candidates and the question information and rank the answer candidates according to the retrieval relevance, each of the answer candidates having a sequence number; and an outputting module configured to output an answer candidate ranked a specified sequence number. By using the application, lower costs for collection and improve successful rate of answers are provided by the system for automatic question answering.

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 system and method for semantic search for electronic documents stored on a computer readable media, and providing a search result in response to a query. The system includes a corpus including a plurality of electronic documents that are domain tagged at a document level and analyzed based on the tags to identify word usage patterns. An index of word usage patterns is provided that indexes the plurality of documents in the corpus according to their word usage patterns. The system also includes a query pre-processing module that receives a query from a user, and analyzes the query to determine probable word usage patterns in the query. The system further includes a processor that uses the index to identify documents having word usage patterns that matches the probable word usage patterns in the query as a candidate electronic document, and retrieves the candidate electronic document.

In one embodiment, a method is illustrated including receiving a search query, parsing the search query to identify first and second search terms, determining a relationship between the first and second search terms based on their respective domain assignments, conducting a search based on the respective domain assignments, and displaying a result of the search based on the respective domain assignments, and the relationship between the first and second search terms, as at least one rephrasing of the search query.

A computer implemented method, apparatus, and computer usable program code for determining aggregate values of health data items from heterogeneously coded databases containing heterogeneously coded medical data. The data, in heterogeneous databases, is queried using a series of semantic layers including i) cascaded asymmetric association tables and ii) semantic search. The heterogeneously coded medical data items are translated into conformal dimensions and denominator files of combinations of disease data are derived. The denominator files of combinations of disease are aggregated based on a mapping of the coded medical and demographic conditions. The data is stored in a target data repository.

A method, device and system for acquiring information related to annotations and the content of a document. Annotations are isolated from document content and are associated with portions of the content of the document. Annotations and content are used as a basis for a semantic search of a corpus of other documents. From the corpus, related information is extracted and presented or made available along side of or with the original content and annotations of the document. Each version of a document is stored and made accessible. Any of the versions of a document, with or without a current set of annotations, may be distributed to others for further review and annotation. Annotations are protected and associated with a level of privilege or rights. Annotations are trackable over time and location and are associated with a particular annotator.

Techniques for providing semantic search of a data store are disclosed. A similarity metric of a document comprising the data store to a concept represented in a semantic model derived at least in part from a reference source that includes content not included in the data store is determined. A relevance metric of a search query to the concept is computed. The similarity metric and the relevance metric are used to determine, at least in part, a ranking of the document with respect to the search query.

Methods and systems for searching over a large corpus of data to discover relevant information artifacts based on similar content and / or relationships are disclosed. Improvements over simple keyword and phrase based searching over Internet scale data are shown. A search query may be modified or relaxed based on the search terms and a contextual relationship therebetween. The search results may be ranked based on both a data ranking corresponding to the data entries in the corpus and a query ranking corresponding to the search query and / or the modified or relaxed search query. In this manner, the accuracy and relevance of the search results is improved.

A computer-implemented system for searching a plurality of images for an image of interest including a database of semantic image representations corresponding to the plurality of images, wherein the semantic image representations link a semantic model of clinical properties, a syntactic model of high level image properties and an image vocabulary of low level image properties, a set of queries associated with the semantic image representations, and a semantic search engine, embodied as computer readable code executed by a processor, for receiving a search query, selecting at least one of the set of queries based on the search query, and searching the plurality of images for the image of interest by comparing the plurality of images against the semantic image representations associated with a selected query.

Mechanisms for searching XML repositories for semantically related schemas from a variety of structured metadata sources, including web services, XSD documents and relational tables, in databases and Internet applications. A search is formulated as a problem of computing a maximum matching in pairwise bipartite graphs formed from query and repository schemas. The edges of such a bipartite graph capture the semantic similarity between corresponding attributes of the schema based on their name and type semantics. Tight upper and lower bounds are also derived on the maximum matching that can be used for fast ranking of matchings whilst still maintaining specified levels of precision and recall. Schema indexing is performed by ‘attribute hashing’, in which matching schemas of a database are found by indexing using query attributes, performing lower bound computations for maximum matching and recording peaks in the resulting histogram of hits.

A search system analyzes a user's search requests and identifies prior semantically-similar search requests that have provided well-received results. Each search request is classified, based on the semantics of the search request, and the user's satisfaction with the effectiveness of the search request is monitored and recorded within the determined class (or set of classes). As a particular user's search session continues, the classification of the user's search request is also used to identify other searches in the determined class, and the user is provided the option of modifying or replacing the user's current request with one of these semantically similar searches. The system may also be configured to identify the most favored results provided by these semantically similar searches, and allow the user to select from among these results. The system may also provide incremental updates over time, as new results or new semantically-similar search requests are found.

A Domain Ontology Oriented Resource System (DOORS) and a Problem Oriented Registry of Tags And Labels (PORTAL) are infrastructure systems for resource metadata within a paradigm serving as a bridge between the original web and the semantic web. IRIS registers domain names while DNS publishes domain addresses with mapping of names to addresses for the original web. Analogously, PORTAL registers resource labels and tags while DOORS publishes resource locations and descriptions with mapping of labels to locations for the semantic web. They provide an analogous resource label system, semantic search applications, and the benefits of collaborative semantic networks. Advertising is supported in several ways. Businesses purchase the right to display their products or services in association with searches. Also, content providers accept placement of advertising. Ads are then selected for display based on the content displayed utilizing the invention to match service providers with advertisers.

Embodiments of the invention are generally related to semantic search and service metadata repositories, particularly with regards to methods and systems for performing a semantic asset search in a service metadata repository. One embodiment includes identifying service metadata assets with similar metadata, relationships, and categorizations to the service metadata assets with the most relevant keywords and identifying service metadata assets have been used in conjunction with the one or more selected service metadata assets.

A Domain Ontology Oriented Resource System (DOORS) and a Problem Oriented Registry of Tags And Labels (PORTAL) are infrastructure systems for resource metadata within a paradigm serving as a bridge between the original web and the semantic web. IRIS registers domain names while DNS publishes domain addresses with mapping of names to addresses for the original web. Analogously, PORTAL registers resource labels and tags while DOORS publishes resource locations and descriptions with mapping of labels to locations for the semantic web. They provide an analogous resource label system, semantic search applications, and the benefits of collaborative semantic networks. Advertising is supported in several ways. Businesses purchase the right to display their products or services in association with searches. Also, content providers accept placement of advertising. Ads are then selected for display based on the content displayed utilizing the invention to match service providers with advertisers.

The invention provides an image semantic retrieving method based on a visual attention mechanism model, which is driven by data completely, thus understanding the semantics of images from an angle of a user as far as possible under the condition of no need to increase the interactive burden of the user, and being close to the perception of the user so as to improve the retrieving performance. The image semantic retrieving method has the advantages that: (1) a visual attention mechanism theory in a visual cognition theory is introduced into image retrieve; (2) the method is a completely bottom-up retrieve mode, thus having no need of user burden brought by user feedback; and (3) obvious edge information and obvious regional information in images are simultaneously considered, the mode of retrieval integration is realized, and the performance of image retrieval is improved.

The invention discloses a semantic search method based on multi-semantic analysis and personalized sequencing, and belongs to the field of information search. The semantic search method adopts the technical scheme comprising the following steps: firstly, by a crawler technology and other technologies, acquiring webpage documents from the Internet, classifying the webpage documents by using a support vector machine, establishing a word vector library by a multi-semantic analysis method, and writing multi-classification results into an index to form an index library; secondly, based on the word vector library, forming search keywords input by a user into a query vector, performing class matching query with the index library to obtain an initial sequencing result; and finally, according to personalized information and history access information of the user, optimizing the initial sequencing result, and returning the optimized result to the user. By the semantic search method based on the multi-semantic analysis and the personalized sequencing, the word vector library and the index library with rich semantemes are formed; and through the personalized information and the history access information, a search result can meet a search demand of the user better and search satisfaction of the user can be improved.

A method and system for searching for information contained in a database of documents each includes an offline part and an online part. The offline part includes predicting, in a first computer process, semantic data for sentences of the documents contained in the database and storing this data in a database. The online part includes querying the database for information with a semantically-sensitive query, predicting, in a real time computer process, semantic data for the query, and determining, in a second computer process, a matching score against all the documents in the database, which incorporates the semantic data for the sentences and the query.

A semantic search engine is enhanced to employ user preferences to customize answer output by, for a first user, extracting user preferences and sentiment levels associated with a first question; receiving candidate answer results of a semantic search of the first question; weighting the candidate answer results according to the sentiment levels for each of the user preferences; and producing the selected candidate answers to the first user. Optionally, user preferences and sentiment levels may be accumulated over different questions for the same user, or over different users for similar questions. And, supplemental information may be retrieved relative to a user preference in order to further tune the weighting per the preferences and sentiment levels.

A semantic search is provided. The semantic search generates related terms that relate to a search term using a selected ontology domain. The related terms may be compared to information in a medical document. The semantic search may also generate related terms for the medical document. The related terms for the medical document may be compared to the related terms that relate to the search term.

A consensus search device is provided, which includes a semantic data indexing unit configured to divide text data of an electronic document written about at least one object into segments, to extract at least one semantic descriptor from the each segmented text data, and to generate a semantic data index matching each of the extracted semantic descriptor to the object and the each segmented text data. The consensus search device also includes a semantic searching unit configured to retrieve an object related to a query, based on the semantic data index. The text data is divided into the segments by units of meaning.

The invention discloses a semantic search method and device based on a knowledge map, relating to the search engine field, which is used for satisfying the search requirements of a user at the semantic level, thereby providing an accurate and efficient search experience for the user. The method comprises the following steps of: obtaining search information; Carrying out semantic identification onthe search information according to the knowledge map to obtain the user intention; Reasoning and querying the first set of entities according to the user intention to obtain a second set of entities,wherein the second set of entities is a set of entities of high relevance to the user intention in the first set of entities; notifying The user of the second set of entities as a search result. Embodiments of the present application are applied to a semantic search based on a knowledge map.

Systems and methods for populating a database. An ontology is parsed to determine a plurality of keywords. A string-based search engine is utilized to perform a search of documents on a network based on the determined keywords, and at least one document is retrieved. A relation is established between the retrieved document and the ontology, and it is determined if the at least one document is to be stored in the database based on the established relation. If so, the document is stored in the database. The database can be used as part of a standalone or plug-in search engine for retrieving online documents.

A data dependency path calculator for a semantic search engine is provided. A body of semantically related data is modeled according to a semantic data model. A user is presented a list of data elements from which they may select desired data elements. The system automatically calculates all of the possible paths through the database that may be used to retrieve meaningful data based on the selected data elements. The available data dependency paths are returned to the user for selection. The system further provides a type of data permission that allows restricted data elements to be used as a pass-through data element for relating, connecting and retrieving non-restricted data. Thus, a user can use restricted data to create data dependency paths to retrieve meaningful data. The system further provides for defining access privileges for all levels of data structures, allowing data to be secured with an increased level of granularity than previously possible.

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 visual keyword based remote sensing image semanteme searching method. The method comprises the following steps: setting visual keywords which describe image contents in an image base; selecting a training image from the image base; extracting remarkable visual characteristics of each training image, wherein the remarkable visual characteristics include remarkable points, main dominant tone and texture; acquiring a key mode through a cluster center of a cluster algorithm; establishing a visual keyword hierarchical model by adopting a Gaussian mixture model; extracting the remarkable visual characteristics of all images in the image base, setting weight parameters, and constructing a visual keyword characteristic vector describing the image semanteme; and calculating the similarity between an image to be searched and all images according to the similarity criterion, and outputting a search result according to the high-low sequence of the similarity. The method can effectively improve the recall ratio and the precision ratio of image searching by establishing the correlation between low-layer remarkable visual characteristics and high-layer semantic information through the visual keywords, and the technical scheme provided by the invention has excellent expansibility.

The invention discloses a method and a device for searching an information object. The information object is information content with semantic structure, comprising a plurality of information properties with semantic correlative relation, wherein the information properties are units of different semantic aspect content of information objects; and the semantic structure is collection of semantic metadata. Target webpage URI can be utilized to query correlative library to obtain semantic structure by establishing semantic structure and network resource URI or correlative library of prefix of network resource URI; webpage information is formatted and indexes are established with the guidance of semantic structure; user request is responded, searching results are provided according to correlation order, and simultaneously layering semantic navigation trees are provided; and refine searching results are provided to a user when the user clicks the node of the semantic navigation tree. The invention belongs to the technical field of computer science, is applicable to the application fields of network information extraction, full text search, semantic search, mobile phone search and the like.

The invention discloses an Internet of Things capability and knowledge mapping and a construction method thereof. The capability and knowledge mapping comprises a model layer and a data layer, whereinthe model layer is used as a capability ontology, comprises specification concept sets and logical relationships of the specification concept sets, and particularly comprises capability concepts, capability relationships, capability attributes, and definition domains and value domains of the capability attributes; the data layer is used as an entity set of the capability and knowledge mapping andinstantiation of the model layer, and particularly comprises capability entities, capability attribute values, relationships between capability entities, object entities, and relationships between the object entities and the capability entities. According to the Internet of Things capability and knowledge mapping and the construction method thereof, capabilities are separated to serve as core nodes of the mapping, meanwhile, the capabilities are distinguished from other attributes of objects, the capabilities can be searched rapidly and relationships between the objects can be established through the capabilities, and supports can be provided for semantic search, service composition and user recommendation in the field of the Internet of Things.

The invention discloses a semantic retrieval method oriented to field data. Semantic information of a certain field is described by using a body, so that a computer understands content inquired by a user more accurately, and retrieval on a semantic level is realized. A semantic retrieval model oriented to field data is provided in combination with the structural characteristics of a semantic network body on the basis of analyzing the prior art. The semantic relativity between a keyword and different examples is deduced by using the model according to the position of the keyword in the body, and a relativity weight is written into an example inverted list which corresponds to the keyword, so that the similarity computation complexity during semantic retrieval is lowered, and the retrieval efficiency is increased. According to the method disclosed by the invention, retrieval is raised from a grammatical level of simple keyword matching to a semantic level understandable for the computer, and the computer can understand the input keyword, so that retrieval intelligence is realized.