6701 results about "Knowledge graph" patented technology

Methods and systems for creating a knowledge graph that relates terms in a corpus of literature in the form of an assertion and provides a probability of the veracity of the assertion are disclosed herein. Various aspects of the invention are directed to and / or involve knowledge graphs and structured digital abstracts (SDAs) offering a machine readable representation of statements in a corpus of literature. Various methods and systems of the invention can automatically extract, structure, and visualize the statements. Such graphs and abstracts can be useful for a variety of applications including, but not necessarily limited to, semantic-based search tools for search of electronic medical records, specific content verticals (e.g. newswire, finance, history) and general internet searches.

The invention discloses a mapping knowledge domain questioning and answering system and method based on a template matching technique. The system comprises an offline module and an online module, wherein the offline module is mainly used for offline data preparation, and the online module is mainly used for system service. The system can support a friendly natural language question of a user to serve as a query, grammatical analysis is conducted on the question of the user, an offline-defined manual template library is searched for a matched question template according to a result obtained through grammatical analysis, a final structurized mapping knowledge domain query statement is generated according to an entity matching method and a relation matching method, retrieving is conducted in a mapping knowledge domain according to the structurized query statement, and then a final result is returned. According to the mapping knowledge domain questioning and answering system and method based on the template matching technique, the precise retrieving result can be given for the question of the user, and the searching satisfaction degree of the user is improved.

Aspects discussed herein present a solution for utilizing large-scale knowledge graphs for inference at scale and generating explanations for the conclusions. In some embodiments, aspects discussed herein learn inference paths from a knowledge graph and determine a confidence score for each inference path. Aspects discussed herein may apply the inference paths to the knowledge graph to improve database lookup, keyword searches, inferences, etc. Aspects discussed herein may generate a natural language explanation for each conclusion or result from one or more inference paths that led to that conclusion or result. Aspects discussed herein may present the best conclusions or results to the user based on selection strategies. The presented results or conclusions may include generated natural language explanations rather than links to documents with word occurrences highlighted.

Embodiments of the present invention provide for a method, system and computer program product for fast heterogeneous multi-data source search and analytics. In an embodiment of the invention, a method includes receiving a specification of multiple different data sources in a search and analytics engine, establishing communicative links between the engine and the data sources, and identifying a data source type and corresponding data fields storing respective data for each data source. The method further includes specifying a multi-hop graph traversal query implicating data across the different data sources, decomposing the query into constituent components and mapping each of the constituent components to each of the data sources based upon the corresponding data fields. The method even further includes formulating a specific query for each of the data sources, transmitting each query to each data source and populating in a knowledge graph each result set received for each query.

The invention belongs to the technical field of Chinese knowledge base application, in particular to a book-oriented reading field knowledge map construction method. The method includes general knowledge map construction, namely acquiring knowledge on the internet, and integrating a general knowledge map; field knowledge map construction, namely combining with the general knowledge map and expending relative concepts and entities of books in an iterative manner, and extracting entity relation by combining with an entity Infobox table and traditional relation; and intelligent reading recommendation, namely marking core entities in electronic books from longer entities to shorter entities, and establishing links between the entities and a book knowledge map so as to realizing the intelligent reading recommendation. According to the method, by means of establishing the book-oriented reading field knowledge maps, the entities in the books can be explained or knowledge can be recommended, depth of knowledge is increased, facilitation, intelligentization and humanization of electronic reading are realized, and better user experiences are provided.

The invention provides a question and answer method based on a knowledge graph, and an agricultural encyclopedia question and answer system. A natural language question raised by a user can be automatically analyzed; a topological structure based on a syntax tree is formed; retrieval and comparison are carried out through the topological structure and a question template in a grammar library; according to a mapping relation between the topological structure and a predicate nominatum, and a mapping relation between a synonym set and a relation or an attribute in the knowledge graph, a question-mapped predicate is obtained; in combination with an entity identified in the question, a final structured knowledge graph query statement is generated; retrieval is carried out in the knowledge graphaccording to the query statement; and a final result is returned. When the relevant topological structure cannot be retrieved in a question template library, the question answering is carried out bycalling common question-answer pairs of an FAQ question library. The question and answer system can give accurate answer retrieval for the question posed by the user, so that the satisfaction degree of the user to the agricultural encyclopedia question retrieval is improved.

The invention discloses a knowledge graph-based enterprise risk prediction method and system. The method comprises the steps of 1) constructing entities of an enterprise knowledge graph, establishingrelationships between the entities, and extracting knowledge from set data sources to form the enterprise knowledge graph; 2) capturing news information of enterprises and related entities, processingthe captured news information based on the enterprise knowledge graph, marking relationships between the related entities and events, and storing marked information in a structured knowledge base toform an enterprise news information knowledge graph; and 3) performing enterprise risk prediction based on the enterprise knowledge graph and the enterprise news information knowledge graph. The blankof an existing knowledge graph in the field of enterprise risk analysis is filled; and the constructed enterprise knowledge graph and enterprise news information knowledge graph have relatively highpracticality.

The invention provides a knowledge graph generating method and device. The method comprises the steps as follows: original text data in a designated field are subjected to morphological, semantic analysis and / or semantic analysis, and standard text data are obtained; factual information is extracted from the standard text data and comprises the following elements: the relation among entities, attributes and entities as well as the relation between the entities and the attributes; structural representation is performed on the factual information in a preset representation form, and structured data pairs of the factual information are obtained; the structured data pairs are adopted as knowledge entries for establishing of a knowledge graph. With the adoption of the knowledge graph generating method, the specific knowledge graph can be established, the intelligent interaction requirement in the specific field such as the children's filed is met, and the interactive experience of users with different requirements is improved.

The invention discloses a disease knowledge map construction method and a system, a device and a storage medium thereof, belonging to the technical field of disease knowledge maps. The disease knowledge map construction method comprises the following steps: extracting or learning entity, attributes and relationships among entities from a multi-source database to form ontological knowledge representation; B. knowledge fusion: integrate knowledge to eliminate contradictions and ambiguity; C. Knowledge processing: After quality assessment, the fused knowledge is added to the knowledge base. It also includes disease knowledge mapping platform system, equipment and storage medium. Aiming at the problems existing in the prior art, it extracts entities, attributes and their relationships from multiple data sources, and fuses and processes them, so as to quickly construct a normalized and standardized disease knowledge map.

A relation detection model training solution. The relation detection model training solution mines freely available resources from the World Wide Web to train a relationship detection model for use during linguistic processing. The relation detection model training system searches the web for pairs of entities extracted from a knowledge graph that are connected by a specific relation. Performance is enhanced by clipping search snippets to extract patterns that connect the two entities in a dependency tree and refining the annotations of the relations according to other related entities in the knowledge graph. The relation detection model training solution scales to other domains and languages, pushing the burden from natural language semantic parsing to knowledge base population. The relation detection model training solution exhibits performance comparable to supervised solutions, which require design, collection, and manual labeling of natural language data.

Processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation. Analyses of user inputs are coordinated via a knowledge graph constructed from categories, attributes, and attribute values describing relatively frequently occurring prior interactions of various users with an electronic marketplace. The knowledge graph has directed edges each with a score value based on: the conditional probabilities of category / attribute / attribute value interactions calculated from user behavioral patterns, associations between user queries and structured data based on historical buyer behavioral patterns in the marketplace, metadata from items made available for purchase by sellers used to better define buyers' requirements, and / or world knowledge of weather, locations / places, occasions, and item recipients that map to inventory-related data, for generating relevant prompts for further user input. The knowledge graph may be dynamically updated during a multi-turn interactive dialog.

The invention provides an abnormal information text classification method based on a knowledge graph. According to the method, first, a domain knowledge graph is constructed, and an entity identifierand an entity link based on the domain knowledge graph are constructed; second, text feature representation vectors v<text> and entity feature representation vectors v<ent> are constructed; and last,the text feature representation vectors and the entity feature representation vectors are merged to obtain new text representation vectors v<merge> fusing knowledge features, classified training is performed on the new text representation vectors, and a final classification result is obtained.

The invention provides a knowledge graph construction method and device based on structural data. The method comprises the steps of obtaining one or more structural data containing entity names and corresponding entity attribute information, extracting the mapping relation between the entity names and the entity attribute information contained in the structural data, and generating a corresponding data structural pair; using the generated data structural pair as knowledge graph data items to be stored. A knowledge graph is constructed based on the structural characteristics of the structural data, the framework of data items in the knowledge graph is made to contain the entity names and the corresponding entity attribute information, and the entity attribute information can be visually and accurately used as a research result to be provided for a user when a search service is externally provided based on the structural data of the knowledge graph.

A method and a system for dynamically modifying at least one element of a User Interface (UI) of a first electronic device are provided. The method includes collating usage information of at least one data source in the first electronic device, categorizing the collated usage information into one or more knowledge clusters, forming a knowledge graph using the one or more knowledge clusters, and dynamically modifying the at least one element of the UI based on the knowledge graph.

The invention discloses an online learning cognitive map generation system and method for representing the knowledge learning mastering state of a leaner in a specific field. The system comprises an intelligent terminal and a server. The intelligent terminal is used for online learning interaction. The online learning cognitive map generation system runs in the server comprising a knowledge graph construction module, a learning interaction data acquisition module, a knowledge state calculation and prediction module and a visual output module. The online learning cognitive map generation system and method are suitable for a common online learning platform and can simulate the cognitive structure of the learner and diagnose the learning defect of the leaner to provide personalized learning resources and social network services for the learner, so that learning confusion is reduced, cognitive loads are reduced, and learning pertinency and accuracy are improved.

The invention relates to a network security knowledge graph construction method and device, computer equipment and a computer storage medium. The network security knowledge graph construction method comprises the following steps: extracting attack source information and attack target information from a preset network security knowledge base, and respectively identifying an attack entity associatedwith the attack source information and an attacked entity associated with the attack target information; detecting a threat alarm event between the attack entity and the attacked entity, and determining a relationship between entities according to the threat alarm event; and respectively extracting asset attributes of the asset entities, and constructing a network security knowledge graph according to the asset entities, the asset attributes and the relationship between the entities. According to the invention, the network security problem of the target network can be detected more completely, and the detection effect of the network security problem is improved.

The invention discloses a method and a device for acquiring knowledge graph vectoring expression. The method comprises the following steps of labeling an entity, existed in and belonging to a knowledge graph, in a given auxiliary text corpus by utilization of an entity labeling tool according to a to-be-processed knowledge graph so as to obtain an entity-labeled text corpus; constructing a co-occurrence network comprising words and entities on the basis of the text corpus so as to relate text information of the auxiliary text corpus to entity information of the knowledge graph, and then learning to obtain a text context embedded expression; respectively modeling the embedded expression of the entity and relation in the knowledge graph according to the text context embedded expression so as to obtain an embedded expression model of the knowledge graph; training the embedded expression model by utilization of a stochastic gradient descent algorithm so as to obtain the embedded expression of the entity and relation in the knowledge graph. The method and the device disclosed by the invention have the advantages that not only can the expression capability of the relation be improved, but also the problem of insufficient expression effect caused by sparseness of the knowledge graph can be effectively solved.

The invention relates to a network security dynamic early-warning method based on a knowledge graph. After the knowledge graph is constructed according to network security data and domain knowledge of a complicated heterogeneous environment, a graph query method toward a network security domain is used for extracting target information, through the disposal and analysis for the security data, precise description is carried out, and finally, the target information is obtained. According to the network security dynamic early-warning method based on the knowledge graph, the difficulty and complexity of network security data processing can be reduced.

The invention provides a method and a system for human-machine questioning and answering based on a knowledge graph. In the method for the human-machine questioning and answering based on the knowledge graph provided by the invention, word segmentation is carried out to a sentence input by a user when the sentence input by the user is input; entity extraction is carried out to words obtained by the word segmentation; knowledge inference is carried out to entity information obtained by the entity extraction through application of the knowledge graph; and according to results of the knowledge inference, a feedback is made to the sentence input by the user, so that an answer fed back by the human-machine questioning and answering will become more accurate, a question proposed by the user can be answered in a targeted manner, and the user's satisfaction degree can be increased.

The invention discloses a knowledge graph representation learning method. The method comprises the following steps: defining the correlation between entity vectors and relation vectors in a relation triple (head, relation, tail) by utilizing a translation-based model between the entity vector and the relation vector; defining the correlation between entity vectors and feature vectors in a feature triple (entity, attribute, value) by utilizing a neural network classification model; and correlating the entity vectors, the relation vectors and the feature vectors with one another through an evaluation function, and minimizing the evaluation function to learn the entity vectors, the relation vectors and the feature vectors so as to achieve the optimization aim. By adopting the method disclosed in the invention, the relation among the entity, the relation and the feature can be accurately represented.

The embodiment of the invention provides an answer providing method. The method comprises steps of receiving a question submitted by a user, extracting an entity node and an attribution node from the question, building a query chain corresponding to the question, retrieving an answer to the question in a pre-constructed knowledge graph according to the query chain corresponding to the question to designate the answer as an answer acquired according to a knowledge graph retrieval way to the question, and feeding back the answer acquired according to the knowledge graph retrieval way to the user. Possibility that an answer acquired via retrieval is the answer to the question submitted by the user can be improved to a certain degree, so accuracy of answers fed back to the user can be improved; and besides, the embodiment of the invention further provides an answer providing apparatus.

The invention discloses a knowledge graph-based interactive question and answer method and system. The method comprises the steps of constructing a knowledge graph, wherein data in the knowledge graphis from multiple open-source information sources; according to existing entities in the knowledge graph, forming a dictionary, forming mapping from a name to a professional field through a manual tagging method, and performing expansion in a conventional feature modeling mode to form a professional dictionary; according to the data in the knowledge graph, forming mapping from the entities to a training set of the field through the manual tagging method to establish a classifier; according to the professional dictionary, performing word segmentation on a natural question sentence through a forward maximum matching method, and according to results after word segmentation, inputting the results to the classifier for performing classification, thereby classifying the natural question sentenceinto questions in different fields; and mapping the classified questions to obtain corresponding question templates, and converting the question templates into query sub-graphs in the knowledge graph. Answer can be performed for sentences of more complex questions, so that the answer quality can be ensured and the manual intervention is effectively reduced.

Systems, methods, and computer program products for identifying a relevant candidate product in an electronic marketplace. Embodiments perform a visual similarity comparison between candidate product image visual content and input query image visual content, process formal and informal natural language user inputs, and coordinate aggregated past user interactions with the marketplace stored in a knowledge graph. Visually similar items and their corresponding product categories, aspects, and aspect values can determine suggested candidate products without discernible delay during a multi-turn user dialog. The user can then refine the search for the most relevant items available for purchase by providing responses to machine-generated prompts that are based on the initial search results from visual, voice, and / or text inputs. An intelligent online personal assistant can thus guide a user to the most relevant candidate product more efficiently than existing search tools.

Systems and methods for transforming formal and informal natural language user inputs into a more formal, machine-readable, structured representation of a search query. In one scenario, a processed sequence of user inputs and machine-generated prompts for further data from a user in a multi-turn interactive dialog improves the efficiency and accuracy of automated searches for the most relevant items available for purchase in an electronic marketplace. Analysis of user inputs may discern user intent, user input type, a dominant object of user interest, item categories, item attributes, attribute values, and item recipients. Other inputs considered may include dialog context, item inventory-related information, and external knowledge to improve inference of user intent from user input. Different types of analyses of the inputs each yield results that are interpreted in aggregate and coordinated via a knowledge graph based on past users' interactions with the electronic marketplace and / or inventory-related data.

The invention belongs to the technical field of text semantic processing and semantic webs, and particularly relates to a knowledge graph management method and system based on semantic space mapping. The method comprises the steps of semantic vector construction, semantic space mapping and knowledge graph management, wherein the step of knowledge graph management comprises three sub-steps of semantic clustering, semantic duplication eliminating and semantic annotation. A text unit describing edge / nodal points of a knowledge graph is projected to a semantic space, and vector representation of the edge / nodal points on the semantic space is obtained by vector accumulation; on the basis, multiple management tasks of the knowledge graph are achieved. The system correspondingly comprises a semantic vector construction module, a semantic space mapping module and a knowledge graph management module. The defects that a conventional knowledge graph management method is sensitive to factors such as word deformation, synonym variation and grammatical form variation are overcome, the situation of difference of the number of words can be easily handled in a vector accumulation mode, and further knowledge graph management tasks such as semantic clustering, semantic duplication eliminating and semantic annotation are easily achieved.

The invention belongs to the technical field of wireless communication networks and particularly discloses a crowd portrayal system and a crowd portrayal method based on a microblog label. The system comprises two main modules of a microblog label recommendation module and a label theme clustering module; the first module adopts a label recommendation algorithm covering three steps, wherein a first step is homogeneous label recommendation, a second step is co-occurrence label extension, a third step is that a semantic network is built on the basis of a Chinese mapping knowledge domain, the semantic similarity between labels is measured by using a network topology property, the labels with same or similar semantics are thus removed, and the refining property of the label used for portraying a user is ensured. According to the system and the method, the condition that the commercial application value of the label of the microblog user is wide is utilized, and the research direction is indicated for the mining algorithm of labels of internet users and the application of the Chinese mapping knowledge domain.

The invention provides a knowledge graph construction method and system based on legal data. The method comprises a step of constructing a legal dictionary, a step of extracting a legal phrase, a stepof pre-processing a legal text, a step of initially constructing legal knowledge, a step of identifying a legal entity, a step of identifying a legal relationship and a step of constructing a legal knowledge chain. The system comprises a legal basis dictionary construction module, a legal basis knowledge base construction module, an automatic legal entity identification module, an automatic legalrelationship identification module and an automatic legal knowledge chain generation module. According to the knowledge graph construction method and system based on the legal data provided by the invention, a knowledge graph is constructed for the legal data; a knowledge graph can be constructed according to action cause data of multiple legal action causes, such as divorce dispute, contract dispute and traffic accident liability dispute of motor vehicles; and intelligent data is provided for upper applications in the legal industry.

Systems and methods for generating prompts for further data from a user in a multi-turn interactive dialog. Embodiments improve searches for the most relevant items available for purchase in an electronic marketplace via a processed sequence of user inputs and machine-generated prompts. Question type prompts, validating statement type prompts, and recommendation type prompts may be selectively generated based on whether a user query has been sufficiently specified, user intent is ambiguous, or a search mission has changed. Detection of a new dominant object denotes search mission change. Contextual associations between prompts and user replies are maintained, but a search mission change results in previous context data being disregarded. Prompts can be directed to unspecified knowledge graph dimensions based on data element association strength values, relative data element positions and depths in the knowledge graph, and generated following a predetermined order of data element knowledge graph dimension types.

In an exemplary method, a media content recommendation system interfaces with a knowledge graph system to access knowledge graph data that represents a knowledge graph relationship between a subject and a media program in a knowledge graph, generates, based on the knowledge graph data, a media content recommendation that includes recommendation content indicating the media program and the knowledge graph relationship between the subject and the media program, and outputs the media content recommendation for presentation to a user by a user computing device.

The invention discloses a domain knowledge map construction method and system based on big data driving, and the method comprises the following steps: crawling a data source in a network, and obtaining first data information; Performing data information extraction on the data source to extract association information between the entities; carrying out Knowledge fusion on the association information between the entities, and establishing a relational database; And converting the relational database into a graph database model to construct a knowledge graph. The method can provide strict and rich data modes, assists various complex analysis applications or decision supports, is high in accuracy, has a guidance value in the actual construction process of the knowledge graph, and has industrial significance.