239results about "Unstructured textual data retrieval" patented technology

A method, an apparatus, and a computer-readable storage medium for generating a document graph. A plurality of electronic documents is received. Each electronic document has a predetermined document type. A machine learning model is selected from the plurality of machine learning models based on the predetermined document type. The selected machine learning model is instructed to extract a plurality of document portions from each electronic document in the plurality of electronic documents in accordance with the predetermined document type. A relationship between two or more document portions is defined based on the content of each document portion, and the document portions are associated based on the relationship. A graph structure having a plurality of nodes is generated. Each node includes at least one document portion. Each node is connected to another node in accordance with the relationship between document portions included in the nodes. The graph structure is stored.

The invention discloses a resume analysis method and system based on a dynamic semantic network, and relates to the technical field of data information processing. The method comprises inputting a resume; according to the file type of the resume, multi-mode resume analysis engine processing is carried out, and information extraction is carried out; constructing a dynamic semantic network according to the extracted information; and outputting structured data based on the stored knowledge graph in combination with an industry self-adaptive mechanism to complete resume analysis. The resume analysis system is used for realizing the resume analysis method. According to the resume analysis method and system, the effects of high resume analysis accuracy, high processing efficiency and high industry adaptability can be achieved.

A method for monitoring and / or controlling a production of at least one chemical product, the method comprising: providing chemical product data related to a production and / or a processing of the chemical product, providing a plurality of chemical product data templates indicative of a structure associated with at least a part of the chemical product data, selecting at least one chemical product data template by determining at least one chemical product data template corresponds to at least a part of the chemical product data, generating contextualized chemical product data by merging at least a part of the chemical product data and the at least one selected chemical product data template, providing the contextualized chemical product data to a data-driven model for generating environmental property data related to an environmental property associated with processing and / or producing the chemical product, wherein the data-driven model is configured to provide the environmental property data in response to being provided by the contextualized chemical product data, providing the environmental property data for monitoring and / or controlling processing and / or a production of the at least one chemical product.

The invention relates to the technical field of passenger service, and discloses a construction method and device of a dynamic extension word bank, equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous original knowledge data related to civil aviation passenger service, and converting the multi-source heterogeneous original knowledge data into a standard format text; processing the unstructured data in the standard format text based on the adaptive word segmentation model and the stop word list to obtain a plurality of candidate words; performing multi-dimensional corpus feature quantitative analysis on each candidate word to determine candidate keywords; and performing similarity calculation based on the entity word segmentation and the candidate keywords, determining effective candidate keywords, and adding the effective candidate keywords into the dynamic expansion word bank. By means of the method and device, the technical problems that in the prior art, a word bank used by a civil aviation service system is usually based on a static vocabulary or depends on manual compiling and updating, the static word bank cannot be updated in real time, the characteristics of the civil aviation field cannot be flexibly handled, and the maintenance cost is high due to manual updating and maintenance are solved.

The invention provides a self-adaptive transformation and migration method and system for an internal website, and relates to the technical field of website migration. Comprising the following steps: constructing a hierarchical credential adaptation architecture, establishing a dynamic authority-data mapping mechanism, realizing knowledge base-data migration linkage and deploying an adaptive security protection chain. The method is used for realizing collaborative improvement of compatibility, security and knowledge service efficiency of an internal website in a credential environment through hierarchical credential adaptation, dynamic permission and data association, knowledge base linkage and self-adaptive security protection; besides, through cooperative work of the hardware driving adaptation module and the software interface conversion module, automatic identification of hardware features and interface features, precise analysis of adaptation requirements and dynamic generation of configuration files are realized. The compatibility of an internal website to different models of servers, autonomous controllable components and software interfaces in a credential environment is remarkably improved, the adaptation period is shortened, and stable operation of the system is guaranteed.

The invention discloses a data error correction method, a data error correction device and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: firstly, converting first text data in a first document to be subjected to error correction into structured second text data retaining a content association relationship, and identifying a target error type of the second text data through semantic analysis; and then, calling a target error correction engine integrated with the industry knowledge base to realize accurate identification and correction of industry exclusive errors. In this way, the problems that a general error correction tool is low in precision, not suitable for industry characteristics and free of compliance basis are solved. And finally, converting the error correction result into a revision mark supported by the first document, and mapping the revision mark to the first document, so that accurate positioning and display of the error correction result in the original first document can be realized, a user can be helped to quickly focus on a core risk, and the document checking efficiency can be improved while the error correction precision of the document is ensured.

A method is provided. The method is executed by an extraction engine implemented as a computer program within a computing environment. The extraction engine executes action and task mining on unstructured data. The method includes receiving a communication including unstructured data defining an action and automatically processing the communication by utilizing at least one generative artificial intelligence (AI) model to extract details of the action being performed in the unstructured data. The method includes automatically converting the action into an activity or a task of a process associated with the communication.

Rows of first and second tables that share common values for one or more designated key fields can be considered partner rows to facilitate computer-based comparison of the tables. Responsive to a user request to compare the first and second tables, which designates field(s) common to both data tables as key fields and field(s) common to both data tables as comparison fields, a matches table is generated which includes the key field(s), comparison field(s), and a source field whose value indicates the originating table of the data in the row. For each set of partner rows, the matches table is populated with data from the partner rows. The data in the matches table is handled, and a results table is populated with the results of the handling of the data in the matches table and with data from any unpartnered rows in the first and second tables.

A Multimodal Search System (MSS) is designed to enable accurate search and retrieval across different modalities of data, including images, videos, and text. The MSS leverages multimodal and domain-specific models to extract and represent features and information from multimodal data, enabling seamless querying using text-based search queries. The MSS integrates multiple pipelines, including feature vector-based and tag-based approaches, to ensure robust and scalable search capabilities. The MSS addresses current multimodal-search challenges by leveraging a hybrid approach that combines multimodal embeddings, text embeddings, and semantic tagging to enable cross-modal searching. Additionally, for visual data, both tagging and embedding techniques are integrated within a single modality, ensuring a comprehensive and accurate representation. By assembling multiple retrieval strategies, the solution harnesses the strengths of each, resulting in a balanced and effective search system.

Provided is an information processing system that has a different configuration from conventional configurations. An information processing system (1) comprises: an interface unit (11) that receives input from a user (U); a local language model (LM1); a remote language model (LM2) that is different from the local language model (LM1); and a selection unit (12) that selects, from among at least one of the local language model (LM1) and the remote language model (LM2), a language model that should be used for generating a response to a question that the user (U) has inputted to the interface unit (11).

The invention relates to the technical field of AI large models, in particular to a large model resume screening system and method for the human industry, and the system consists of a working post analysis module, a candidate resume analysis module, a matching degree analysis module and a personal information module. The working post analysis module is used for deeply analyzing and extracting key requirements of working posts; the candidate resume analysis module is used for comprehensively analyzing the uploaded candidate resumes and extracting key information; the matching degree analysis module is used for carrying out accurate matching degree analysis in combination with working post requirements and candidate information and giving a comprehensive ranking; the personal information module is used for the user to manage personal information and account setting; the large model resume screening system and method for the human industry have the beneficial effects that efficient screening and processing of a large number of resumes are achieved through an automation technology, so that the working pressure of a human resource department is remarkably reduced, and the screening efficiency is greatly improved.

The invention provides a text data parallel semantic deduplication method, system and device and a medium, and belongs to the technical field of text processing. The method comprises the steps of collecting large-scale text data to be subjected to duplicate removal, performing preprocessing to generate sample data, performing word segmentation and word frequency statistics by using a BERT word list, and calculating a word frequency vector of each piece of sample data; using a pre-trained BERT model to extract semantic features of the sample data, calculating word semantic weights of words in the sample data according to the word frequency vectors, and calculating similarity of the sample data based on the semantic features and a simhash algorithm; and according to the semantic similarity of the sample data, maintaining a pre-deletion dictionary and a global deduplication graph, and executing a deletion operation of the sample data based on the global deduplication graph to form a deduplicated data set. By combining the BERT model, the simhash algorithm and the global deduplication graph, the efficiency and accuracy of parallel semantic deduplication of large-scale text data are effectively improved.

A computer implemented method for synchronizing a first database with a second database. The method comprises a replication method comprising determining that a first change in the first database fulfils a defined tolerance range in response to detecting that the first change indicates a value of at least one selected attribute of the set of attributes, and determining that a second change in the first database does not fulfil the defined tolerance range in response to detecting that the second change indicates a value of at least one selected attribute of the set of attributes. The replication method further comprises preventing replication of the first change to the second database in response to determining that the first change fulfils the tolerance range, and replicating the second change to the second database in response to determining that the second change does not fulfil the tolerance range.

The invention discloses a slope stability evaluation method based on a large language model and an intelligent prediction model. The method specifically comprises the steps of establishing a slope training database and a large language model training database; constructing a slope stability prediction artificial intelligence model and a slope field large language model; slope parameter extraction is carried out on the data uploaded by the user through a slope field big language model, and whether the extracted slope parameters meet the input requirements of a slope stability prediction artificial intelligence model or not is judged; if the slope parameters in the data uploaded by the user are incomplete, the slope field big language model feeds back the slope parameters needing to be supplemented to the user; after the users are supplemented, the slope field big language model is extracted again and judged again until it is monitored that the users have uploaded all slope parameters; the slope field big language model sends the complete slope parameters into the trained slope stability prediction artificial intelligence model, and the slope stability prediction artificial intelligence model outputs the stability coefficient of the slope.

A method, according to one approach, includes: causing a received query to be translated from a first language to a second language. The method also includes generating potential answers for the translated query, and extracting features from the translated query. The method also includes causing the extracted features to be converted into feature vectors. The method also includes causing the feature vectors to be compared against existing vectors in a knowledge base that correspond to past question-answer pairs. The method also includes causing the potential answers to be ranked based at least in part on an outcome of comparing the feature vectors against the existing vectors in the knowledge base. Furthermore, the method includes causing a final answer to be generated based at least in part on the ranked potential answers.

An entity resolution method and system may obtain historical entity data from one or more databases to generate an entity matrix based on a clustering of the historical entity data. The entity resolution method and system may partition the entity matrix into one or more disjointed groups of records based on multi-pass blocking of the attributes associated with the one or more entities and cluster the one or more disjointed groups of records based on one or more similarity metrics between each of the one or more disjointed groups of records. The entity resolution method and system may generate an entity graph for the one or more entities based on the clustering and may create an entity index for an entity based on the entity graph. Ultimately, the entity resolution method and system resolves an entity query of a requestor as matching the entity.

The present invention discloses a method and system for integrating public data resources based on artificial intelligence, and relates to the technical field of data resource integration. The method for integrating public data resources based on artificial intelligence obtains public resource data, and the public resource data includes text data and picture data; screens the public resource data to obtain text data to be classified and picture data to be classified; integrates and classifies the public resource data based on an integrated classification model stored in a database and then stores the integrated classification model, and the integrated classification model includes a text data integrated classification model and a picture data integrated classification model, which solves the problem that the amount of public data is huge, the data is scattered and difficult to manage.

Migrating computing systems to zero trust compliant architectures using templates that are constructed from multimodal unstructured sources. The unstructured input data is converted to structured data using a trained machine learning model. The structured data output by the model may be reviewed. If errors are found, a revision script can be revised or refined and the unstructured input data may be re-ingested. When the structured data is suitable, the structured data is parsed and stored as templates in a template database. A database of zero trust approved hardware and / or software may be generated. Migration operations may be performed using the templates in the template database and / or the information in the hardware and software database.

A real-time cloud pipeline system (100) for ingesting, transforming, and indexing real estate data from multiple heterogeneous sources, comprising: (a) an ingestion module configured to receive data from structured and unstructured real estate sources; b) a transformation module configured to normalise, enrich and map data to a unified schema; (c) an indexing engine configured to extract metadata and structure content for searchable storage; and d) a cloud-based orchestration module that manages data flow, processing, and system scaling.

A method for labeling data points is provided, comprising: performing a labeling operation on a target data set, the target data set comprising a plurality of data points, each data point representing a business instance, the labeling operation comprising: dividing the target data set into a plurality of first subsets; for each of the first subsets: receiving user input for specifying a tag for at least one data point in the first subset, the tag being used to describe the situation of the business instance represented by the data point; determining whether the similarity between the tag and a tag previously specified for at least one data point in the target data set meets a preset condition; in response to determining that the preset condition is not met, re-performing the labeling operation with the first subset as the target data set; in response to determining that the preset condition is met, setting a tag associated with the tag previously specified for at least one data point in the target data set for each data point in the first subset.

The application discloses a data error correction method and device and a storage medium, and relates to the technical field of data processing. First, the first text data in the first document to be corrected is converted into structured second text data that retains content association, and the target error type of the second text data is identified through semantic analysis. Then, a target error correction engine integrated with an industry knowledge base is called to realize accurate identification and correction of industry-specific errors. In this way, the problem of low accuracy of general error correction tools, inadaptation to industry characteristics and lack of compliance basis is solved. Finally, the error correction result is converted into revision marks supported by the first document, and the revision marks are mapped to the first document, accurate positioning and display of the error correction result in the original first document can be realized, and the user can be quickly focused on the core risk, the error correction accuracy of the document is ensured, and the auditing efficiency of the document is improved.

The invention discloses a substation secondary equipment operation safety protection method and device and electronic equipment. The method comprises the steps that text information contained in a work ticket is recognized, and a text recognition result is obtained; based on the text recognition result, constructing a text database matched with the work ticket identifier of the work ticket; based on the data reading logic corresponding to the work ticket identifier, device control parameters of the secondary equipment operation safety protection device are determined from the text database, the protection device comprises at least one suspension assembly, the position of the suspension assembly corresponds to the position of a protection screen in a transformer substation, and the suspension assembly is used for isolating the protection screen in an unfolded state; and controlling the working state of the suspension assembly based on the device control parameters. According to the invention, the technical problem that the operation safety protection cost of the secondary equipment of the transformer substation is relatively high in the prior art is solved.

The asset management program causes the computer to function as the following modules: an acquisition unit (102) that acquires operation history information indicating the operation history of file assets in the engineering design tool (400) from an operation log file generated in the engineering design tool (400); and an extraction unit (103) that extracts summary information indicating a summary of the content of changes to the file assets from the operation history information. Further, the asset management program causes the computer to function in such a manner that the extraction unit (103) stores the summary information in a database.

To provide an information processing device configured to improve the efficiency in confirming whether the content of a target document matches required conditions or not.SOLUTION: An information processing device comprises: acquisition means for acquiring a first document and a second document; analysis means for extracting, from the first document, contents to be confirmed and generating a task for each content; information extraction means for extracting attribute information indicating a feature of the first document; selection means for selecting candidate documents from among one or more second documents on the basis of the attribute information; search means for searching the candidate documents for a condition pertaining to the content to be confirmed by the tasks; processing means for sequentially inputting, to a trained model, the tasks generated by the analysis means, and causing the trained model to answer whether the content to be confirmed by the input tasks matches the condition searched for from the candidate document; and output means for outputting the result processed by the processing means.SELECTED DRAWING: Figure 3

A system for searching contents within a database of textual contents is described. Said contents and the corresponding databases may be of any kind such as movie titles, music titles, song titles, scientific terms, medical titles / terms, titles formed of one or more sequences of symbols, a list of telephone numbers, a contact list, etc. Upon providing one or more keyword, the search system provides a list one or more of corresponding contents to a user. According to one aspect, after selecting a presented content, by the user, a process corresponding to selected content is executed by a processor.

The invention discloses an intelligent test paper composition method and device, and belongs to the technical field of image or video recognition or understanding. The intelligent test paper composition method comprises the following steps: collecting original data of various questions, and carrying out standardization processing on the collected data to form a standard question bank; wherein the standardization processing comprises adding applicable teaching materials, applicable grades, question types, first-level knowledge point labels, second-level knowledge point labels, difficulty coefficients and score information to the original data; pre-de-duplication processing is carried out on questions in a standard question bank, then the standard question bank is divided into a plurality of first-level question banks according to first-level knowledge point labels, and the first-level question banks are divided into a plurality of second-level question banks according to second-level knowledge point labels; and screening questions from the standard question bank according to set test paper composition information. And when the repeated questions belong to the second-level question bank related to the core knowledge point, replacing the repeated questions with another question in the same second-level question bank, thereby ensuring the accuracy of the test paper composition again.

Provided are computer-implemented systems and methods for connecting to plurality of systems of record. This includes receiving a user request from the user at a user device, transmitting the user request via a network, receiving a first output based on the user request, determining one or more actions, based on the one or more intent signals, each of the one or more intent signals identifying a user intent in the user request, determining a candidate system of record associated with the one or more actions, transmitting an automated request based on the one or more intent signals and the one or more actions, receiving a second output based at least on the one or more intent signals and the one or more actions, the second output comprising a response to the user request, determining a user response to the user request based on the second output.

A cluster of mobile devices is provided for performing parallel computation of network connectivity through distribution of subprocesses to a plurality of the mobile devices. Network nodes representing computation devices are connected via paths comprising links between nodes. The connectivity of such nodes may be used to determine potential interactions between the nodes. Connectivity values may be based on the frequency or type of interactions between community members. A server may distribute subprocesses for computation of connectivity to various mobile devices in the cluster of devices and receive the results of the subprocesses. Identification of electronic interaction between two or more computation devices in the network community may result in connectivity values being adjusted upwards or downwards depending on the type of interaction between the computation devices, the content of the interaction, and / or the result of the interaction. Connectivity values may represent alignment, reputation, degree of trust, competence, or compatibility.