100 results about "Entity–relationship model" patented technology

A method and system for searching, filtering, creating, displaying, and managing entity relationships from a repository of data hierarchies through a user interface is provided. Relationships of a primary entity and its related secondary entities are retrieved and displayed in a unified view in graphical or text view. The unified view may indicate a “cross” relationship between first and second entities through another entity that connects the first and second entities, the first and second entities originating from different data hierarchies and/or data sources. Relationships of a selected secondary entity may be displayed in a unified view and entities or relationships may be updated or stored to a separate storage area. The method and system may be used within an enterprise for implementing Master Data Management or Customer Data Integration for managing data hierarchies containing customer information, human capital information, supplier information, asset information, product information, or financial information.

Disclosed is a system for detecting security threats in a local network. A security analytics system collects data about entities in the local network. The security analytics system identifies the entities in the raw data and determines a set of properties about each of the identified entities. The entity properties contain information about the entity and can be temporary or permanent properties about the entity. The security analytics system determines relationships between the identified entities and can be determined based on the entity properties for the identified properties. An entity graph is generated that describes the entity relationships, wherein the nodes of the entity graph represent entities and the edges of the entity graph represent entity relationships. The security analytics system provides a user interface to a user that contains the entity graph and the relationships described therein.

Methods and apparatus to transform attribute data about assets in a source system data model into attribute data about the same assets in a target system data model. The first step is to extract the necessary attribute data from attribute data collected about inventory assets of a business entity needed to populate the attributes in objects representing those inventory assets in a target system data model. Transformation rules are written which are designed to make all conversions necessary in semantics, units of measure, etc. to transform the source system attribute data into attribute data for the target system which has the proper data format. These transformation rules are executed on a computer on the extracted attribute data and the transformed attribute data is stored in an ER model. In the preferred embodiment, the transformation rules are object-oriented in that transformation rules for subtypes can be inherited from their parent types or classes. An export adapter which is capable of invoking the application programmatic interface of the target system CMDB is then used to export the transformed attribute data stored in the ER model to the target system CMDB. A heuristic method to create self-consistent data blocks without exceeding a maximum size limit involves loading instances of entity types and all related instances in the order of decreasing connectivity metric.

The invention relates to an entity relationship joint extraction method and system based on an attention mechanism. The method comprises the following steps of: converting an entity marked in trainingdata and a triple of a relationship into a form that each word corresponds to a predefined type of tag; Mapping each word in the sentences of the training data into a corresponding word vector, inputting the word vectors into a neural network model based on an attention mechanism, and performing training through a back propagation algorithm to obtain a label prediction model; And inputting the sentences needing to be subjected to entity relationship extraction into the trained label prediction model, predicting a label corresponding to each word, and obtaining entity relationship triples existing in the sentences according to the corresponding relationship between the labels and the words in the triples. The system comprises a preprocessing module, a model training module and a result processing module. According to the method, by more effectively utilizing the key information in the sentences, the joint extraction performance of the relational entities is improved, and the method hasgood practicability.

The invention relates to the field of natural language processing, in particular to an entity relationship extracting system based on a deep neural network. Texts to be processed are input into the system, and the system achieves automatic entity relationship judgement and output; the system inputs word property incorporating characteristics into a convolutional neural network, wherein the convolutional neural network completes automatic characteristic extraction of information of words, word properties and entity positions with respect to extracting relationship and performs automatic classification of the entity relationship. Manual characteristic extraction is not needed, and the prediction efficiency and accuracy are higher. The system provides an automatic entity relationship extracting tool.

The present invention relates to an entity relationship recognition method and apparatus. The method comprises obtaining a statement sequence from a target text in a corpus, and performing named entity recognition and dependency grammar marker on the statement sequence to obtain a marked text sentence; matching and retrieving the marked text sentence on basis of an entity relationship seed to obtain a training example; replacing the entity relationship seed word in the training example with predetermined identification, processing the training example after replacement combined with the named entity recognition and the dependency grammar marker, and generating a candidate rule; fuzzifying the candidate rule to obtain fuzzy rules; determining whether the fuzzy rules comprise a new rule; and retrieving the corpus according to the fuzzy rules to obtain a seed set when the fuzzy rules comprise the new rule, and using the obtained seed set as an entity relationship recognition result. Manual participation can be effectively reduced, dependence on the calibrated corpus is reduced, a new entity relationship can be found timely, and the entity relationship recognition method and apparatus are self-adaptive to entity relationship mining in different fields.

A method of data analysis is enabled by receiving raw data records extracted from one or more data sources, and then generating from the data records an entity-relationship model. The entity-relationship model comprises more entity instances, and one or more relationships between those entity instances. Data analysis of the model is facilitated using one or more provenance links. A provenance link associates raw data records and one or more entity instances. Using a visual explorer that displays a set of entity instances and relationships from a selected entity-relationship model, a user can display details for an entity instance, and see relationships between and among entity instances. By virtue of the underlying linkage provided by the provenance links, the user can also display source records for an entity instance, and display entity instances for a source record. The technique facilitates Big Data analytics.

Open Chinese entity relationship extraction refers to, on the premise of not limiting a corpus field and a relationship category, automatic extraction of relationship information between entities from a Chinese corpus to obtain an entity relationship tuple. The present invention discloses a pattern self-learning based Chinese opening relationship extraction method. The method comprises the following three main steps of: firstly, based on an existing knowledge library, acquiring a high-quality entity relationship tuple and a corresponding sentence as a training corpus, and obtaining a dependent path mode between an entity and a relationship word by a pattern learning method proposed by the present invention; secondly, performing pre-processing of word segmentation, part-of-speech tagging, dependency analysis and the like on a to-be-extracted text, and performing entity relationship extraction by means of a relationship mode obtained by previous learning; and finally, performing quality evaluation on an entity relationship extracted automatically from the Chinese corpus by using a machine learning method, and obtaining the high-quality entity relationship tuple.

The invention discloses a knowledge graph construction method, a data processing device and a computer readable storage medium, which are applied to the field of knowledge relation construction. The method comprises the following steps of obtaining a graphic database comprising an entity definition, an entity relation definition and an attribute definition, and a medical treatment record of a user; extracting an entity, an entity relationship, an attribute and an attribute value pointed by the attribute in the medical treatment record; identifying an entity definition of an entity, an entity relationship definition of an entity relationship and an attribute definition of an attribute in the medical treatment record; and filling the entities, the entity relationship, the attributes and theattribute values in the medical treatment record into a graphic database to obtain a knowledge map. By establishing the knowledge graph, the complex and diverse association relationships between the data in the medical treatment record and the data can be extracted and stored in the form of the graphic database, so that a medical professional knowledge network is constructed, and the data acquisition efficiency is improved.

The invention discloses an entity relationship extracting method based on a deep neural network. The entity relationship extracting method comprises the following steps of: mapping each word or class keyword of a sentence to a word vector or a class vector respectively; carrying out characteristic extraction on the sentence according to the word vector and the class vector; and connecting extracted characteristics end to end and inputting into a whole-connection classification layer to obtain an extracted result. An entity relationship of a text is extracted by utilizing a common neural network and a convolutional neural network in a machine learning process, the accuracy and performance of the extraction of the entity relationship are improved and the artificial workload in the extraction of the entity relationship is simplified. The pre-trained word vector is utilized and the convergence rate and accuracy of the neural network are improved; and sentence characteristics and class characteristics are introduced and the convolutional neural network and the common neural network are used for extracting, so that the problem of long and short sentences is solved and the performance of the extraction of the entity relationship is improved.

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.

The invention relates to an information search method based on a metadata, which comprises the following steps during search: step 1, splitting searched words and sentences into key words; the splitting method is scanning the searched words and sentences from front to back, finding and then cutting the longest match in the word stock, continuing to find the longest match in the rest until splitting is finished; step 2, carrying out metadata match analysis on single key word according to the mapping relationship of er model of the metadata; extracting metadata explanation information based on match result and generating match information record of the key words; step 3, integrating the match information record of all the key words and outputting and presenting the results after optimization treatment. By adopting the information search method, the invention can acquire associated information from a plurality of data sources conveniently and overcome the search bottleneck of data warehousing. The invention supports Chinese search with simple operation, which is especially suitable for application in development and management and can improve the development efficiency of developers.

The invention discloses a relation extraction and knowledge graph construction method based on a deep learning model. The relation extraction and knowledge graph construction method specifically comprises the following steps of processing the corpus data into the annotation data by utilizing a data annotation platform; inputting the annotation data into the deep learning model for training and testing, and generating a relationship extraction model; inputting the to-be-extracted text data into the relationship extraction model, and extracting the entity relationship pairs of the to-be-extracted text data; and constructing a knowledge graph of the to-be-extracted text by utilizing a graph database. According to the method, the relationship extraction model is constructed based on a BERT model, a bidirectional long-short-term memory network model and a conditional random field algorithm, the entity relationship pairs can be accurately extracted, and the correctness of the entity relationship pairs entering the graph database is ensured by manually checking the extracted entity relationship pairs.

The invention discloses a judicial case knowledge graph construction method of a dependency syntactic analysis relation extraction model. The method comprises the following steps of firstly, converting an unstructured judgment document into the structured data through an information extraction technology, then performing word segmentation, part-of-speech tagging and named entity identification processing on the structured data, and then extracting an entity relationship triple through a dependency syntax analysis relationship extraction model; and finally, importing the data in the triple forminto a Neo4j graph database in batches, and constructing a judgment document knowledge graph by utilizing the Neo4j and performing visual display on the judgment document knowledge graph. The dependency syntactic analysis relation extraction model can effectively extract the relation between the entities, is suitable for different large-scale corpora, and has the better transplantation applicability. The judgment document knowledge graph is intuitive and clear, so that the user can conveniently and efficiently master the information, and the great convenience is provided for the judicial work.

The invention discloses a visual query tool for knowledge maps. The tool comprises a visual component library and a visual query system for the knowledge maps, wherein the visual component library provides an API (Application Programming Interface) to the outside; the visual query system uses components in the visual component library through calling the API; the visual component library comprisesstatic map components, single entity relationship diagram components and combined entity relationship diagram components; the visual query system comprises a map overall information displaying moduleand an entity relationship querying module; the overall information displaying module calls the static map components; and the entity relationship querying module calls the single entity relationshipdiagram components and the combined entity relationship diagram components. The tool disclosed by the invention has the advantages that the information contained in the knowledge map is graphically presented by using the visualization technology; and a knowledge map viewer can obtain the information contained in the knowledge map more quickly and more multidimensionally through operations such asquerying and the like in the visual system disclosed by the invention.

System and method for automatic entity relationship (ER) model generation for services as software is disclosed. ER model generation by automated knowledge acquisition is disclosed, and automation of knowledge generation process is disclosed. Information extraction process is automated and multilevel validation of information extraction process is provided. System comprises training module to train information extraction model, and knowledge generation module for population of ER model. Standard Operators are generated based on the ER model so generated (populated). Context aware entity extraction is implemented for the ER model generation. System and method leverages existing ER model to make the system self-learning and intelligent, and provides common platform for knowledge generation from different data sources comprising documents, database, website, web corpus, and blog.

The embodiment of the invention provides an entity relationship obtaining method and device. According to the embodiment of the invention, a text fragment containing every sample group is extracted from a webpage text according to at least one sample group in accord with appointed relationship, wherein every sample group comprises two sample entities; data generalization processing is carried out to the text fragment containing every sample group so as to generate an entity relationship template; two target entities accord with the appointed relationship are extracted from the webpage text through the entity relationship template. According to the technical solution of the embodiment of the invention, the relationship between the entities can be automatically obtained; the obtaining efficiency of the relationship between the entities is improved; and the obtaining cost of the relationship between the entities is reduced.

The invention relates to a software project knowledge graph automatic construction method and system. The method comprises the steps of (1) analyzing original software resource data to obtain basic knowledge entities and entity relationships of a software project, and storing the basic knowledge entities and the entity relationships in a graph database in vertex and edge forms; 2) based on the existing basic knowledge entities and the entity relationships, establishing new relationships between the entities by adopting a knowledge abstraction method, and/or adding new basic knowledge entitiesand entity relationships into a knowledge graph, and storing the new basic knowledge entities and entity relationships in the graph database in the vertex and edge forms; and 3) selecting part or allof the basic knowledge entities and the entity relationships to form the knowledge graph of the software project. Each software resource data analysis method and each knowledge abstraction method areexistent in a plug-in form; and required plug-ins are selected and run to generate the knowledge graph of the software project. The problem of extraction and organization of domain-specific knowledgein multi-source heterogeneous software resources is solved; the application range is wide; and the expandability is high.

The invention relates to an online transaction fraud detection method based on an entity relationship, which is characterized in that the entity relationship is extracted according to transaction data, a relational network bipartite graph is constructed, and a heterogeneous network homogenization method based on node contraction and a neighborhood information aggregation promotion tree classification model mechanism based on ensemble learning and graph representation learning are provided. According to the method provided by the invention, from the perspective of practicability, the attentionis converted from the transaction node to the multi-order neighborhood information of the transaction in the relational network, and the potential association relationship between the transactions isfully considered, so that the possibility is provided for mining gang fraud. The gradient promotion model improves the fraud identification effect through continuously fitting the residual error of the model, and has a very good performance effect. Meanwhile, the method expands the ensemble learning from the application of the grid-type data to the application field of the graph data. Based on theabove aspects, a framework of the loan transaction fraud detection method is established, and technical support is provided for solving fraud transaction detection.

Entities are objects with feature values that can be thought of as vectors in N-space, where N is the number of features. Similarity between any two entities can be calculated as a distance between the two entity vectors. A similarity network can be drawn between a set of entities based on connecting two entities that are relatively near to each other in N-space. Binary relative neighborhood trees are a special type of entity relationship network, designed to be useful in visualizing the entity space. They have the intuitively simple property that the more typical entities occur at the top of the tree and the more unusual entities occur at the leaf nodes. By limiting the number of links to n+1 per node (one parent, n children), a regularized flat tree structure is created that is much easier to visualize and navigate at both a course and a fine level by domain experts.

The invention provides a data dictionary generation method and device. The method comprises the steps of according to a plurality of entity objects, attribute information of each of the plurality of entity objects, and entity relationships of the plurality of entity objects, generating a plurality of pieces of data dictionary format data corresponding to the plurality of entity objects; adding annotation information of the plurality of entity objects in the plurality of pieces of data dictionary format data, so as to form data dictionary specification definitions comprising the annotation information; and generating a data dictionary according to the plurality of pieces of data dictionary format data and the data dictionary specification definitions. Through adoption of the data dictionary generation method, the data dictionary is generated by use of the characteristic of the normalized data structure of a database, so that generation of the data dictionary is prevented from totally relying on the annotation information of the entity objects, the difficulty of generation of the data dictionary is also lowered, and the generation of the data dictionary is operable.

The invention relates to a traditional Chinese medicine electronic medical record entity relationship extraction method based on an entity relationship labeling strategy, and belongs to the technicalfield of computer natural language processing. According to the method, firstly, a method for entity relationship joint labeling based on a new labeling strategy is used for acquiring a required entity relationship corpus, then a Bi-lstm model is used for processing a labeled entity relationship, and finally, a crawler is used for complementing the incomplete entity relationship. The final model avoids the problems of mistake transmission, error accumulation and relationship classification inaccuracy which are caused by a traditional series connection method.

A method and system for preventing error during data replication is provided. A replication entity model is used to represent data in a source and data in a target. One or more of a logical model, a directed relationship model or a state model may be provided to prevent error. The method and system may be applied to data migration and data synchronisation. The system comprises a transformation engine and a replication engine, wherein the replication engine is adapted to instruct the transformation engine to replicate each replication entity in turn. This may be based on the order dictated by the one or more directed relationships in the directed relationship model. Replication of a replication entity by the transformation engine comprises replicating data within one or more selected data structures of the source in one or more selected data structures of the target, the selection being based on the mapping between the replication entity model data in the source and data in the target.