CN117648975A - Oil-gas enterprise knowledge graph construction method based on petroleum business model - Google Patents
Oil-gas enterprise knowledge graph construction method based on petroleum business model Download PDFInfo
- Publication number
- CN117648975A CN117648975A CN202311750857.0A CN202311750857A CN117648975A CN 117648975 A CN117648975 A CN 117648975A CN 202311750857 A CN202311750857 A CN 202311750857A CN 117648975 A CN117648975 A CN 117648975A
- Authority
- CN
- China
- Prior art keywords
- service
- business
- data
- knowledge graph
- petroleum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000003208 petroleum Substances 0.000 title claims abstract description 50
- 238000010276 construction Methods 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 claims abstract description 34
- 238000000605 extraction Methods 0.000 claims abstract description 26
- 230000005477 standard model Effects 0.000 claims abstract description 8
- 230000004927 fusion Effects 0.000 claims description 14
- 230000000694 effects Effects 0.000 claims description 10
- 230000008520 organization Effects 0.000 claims description 5
- 238000004519 manufacturing process Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 239000000463 material Substances 0.000 claims description 3
- 239000013589 supplement Substances 0.000 claims description 3
- 238000009960 carding Methods 0.000 claims description 2
- 238000007499 fusion processing Methods 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 4
- 230000006870 function Effects 0.000 description 15
- 238000010586 diagram Methods 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000003245 working effect Effects 0.000 description 1
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a knowledge graph construction method of an oil-gas enterprise based on a petroleum business model, which belongs to the technical field of knowledge graph construction and comprises a knowledge graph construction method consisting of petroleum business coordinate system construction, petroleum business standard model construction, petroleum industry KG0 knowledge graph entity extraction and petroleum industry KG1 knowledge graph entity extraction, wherein the coordinate system is established according to five minimum business units of objects, businesses, work, business processes and professions. The knowledge graph construction method based on the oilfield business model has the advantages that the universality of oilfield business and the confidentiality of data of each oilfield company are considered, the KG0 graph can be suitable for the application of the whole oil and gas industry, the construction of each oilfield company is quickened, the KG1 graph is implemented in each oilfield function, the data safety is ensured, and the content of specific oilfield work development and various data results after the work can be comprehensively known through the hierarchical knowledge graph.
Description
Technical Field
The invention relates to the technical field of knowledge graph construction, in particular to an oil and gas enterprise knowledge graph construction method based on a petroleum business model.
Background
Knowledge graph is a structured knowledge representation that aims to organize and integrate large amounts of information for more efficient retrieval and analysis. Knowledge maps typically contain a large number of entities (e.g., people, places, things) and attributes (characteristics of entities) that are interconnected by various relationships. The method comprises the following steps: data collection, data preprocessing, entity identification and classification, relation extraction, ontology construction, knowledge fusion, knowledge storage and the like. The most core steps are entity identification, classification and relation extraction, and in the process, the entity identification and classification can be carried out by using methods such as machine learning, deep learning and the like. And then extracting and summarizing the ontology model of the knowledge graph from bottom to top according to the classification of the entity.
The existing knowledge graph construction method has the following defects: the general knowledge graph construction uses machine learning and deep learning algorithms in the knowledge extraction link, and the algorithms need a large amount of data for model training, thus being suitable for the situation that a large amount of text data is stored in the Internet. However, for industrial application in petroleum industry, data are mastered inside each oilfield company, and no corpus data are used for model training. In addition, in the industrial production process of the petroleum industry, a large amount of generated data is structured data designed according to a specified structure, and is not suitable for the extraction method of document data; the method and the mode for constructing the knowledge graph aiming at the oil and gas industry are relatively deficient, and therefore, the oil and gas enterprise knowledge graph construction method based on the petroleum business model is provided.
Disclosure of Invention
The invention aims to solve the xxx problem in the prior art, and provides an oil-gas enterprise knowledge graph construction method based on a petroleum business model.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the oil and gas enterprise knowledge graph construction method based on the petroleum business model comprises a knowledge graph construction method consisting of petroleum business coordinate system construction, petroleum business standard model construction, petroleum industry KG0 knowledge graph entity extraction and petroleum industry KG1 knowledge graph entity extraction;
the method comprises the following steps:
establishing a coordinate system according to five minimum service units of an object, a service, a work, a service process and a specialty;
carrying out service carding design from five bodies of an object, service, work, specialty and service process;
defining the relation between five business description entities from top to bottom to obtain the business relation among the entities;
generating a theoretical minimum service unit according to the intersection point of the established service ontology relation; the actual business work is combined, different attributes are given to the theoretical business nodes, and the theoretical business nodes are associated with the actual business work;
selecting a designated service node, and creating a unified description structure of various service resources generated under the service node to form a standard data dictionary;
establishing a relation among different dimensionalities of the business activity through a business activity dimensionality relation management tool, and establishing a hierarchy relation of the dimensionality through a tree structure of the dimensionality;
extracting data entities, data attributes, labels and data relations from structured and unstructured data according to the data organization structure of the oil gas knowledge graph;
dividing a large number of data sets into smaller, easier to manage and analyze subsets, and dividing the structured data table into dimensions defined by business model entities;
acquiring the blood-edge relation of a business entity from the input and output results of the minimum business node or acquiring the blood-edge relation of entity data from the CR relation of the data item;
splitting according to chapter or title, content type and user rules, dividing a document into a plurality of smaller parts or paragraphs, and then organizing and marking the paragraphs according to categories, topics or other criteria;
for the standard of unstructured data generated by non-actual production services such as papers, news, conference materials and the like, the NLP technology is adopted to standard the data;
describing and accessing professional software data in various forms by using a unified model, providing conversion capability among data formats, dimension units and various space coordinates in the process of accessing and outputting, and opening the data to the outside in the form of a service interface;
the calling component platform applies the service list interface related to the function description, establishes the service catalog of the service function tool, and supplements the service resource description label related to the service function. Part of software also needs to develop a data exchange interface in a targeted manner;
and carrying out knowledge fusion, namely carrying out ontology and entity fusion on independent knowledge maps constructed by each department to form a unified, coordinated, richer and comprehensive map knowledge base.
Preferably, the object, the service, the work, the process, the specialty, the minimum service unit and the service resource form an oil and gas industry body.
Preferably, the extraction of the structured data is different from the extraction of object entities from texts required for constructing a knowledge graph by Internet, most of object instances in the petroleum industry are in corresponding structured databases, and all extraction technologies are mainly mapping extraction of structured data tables.
Preferably, the relationship between every two of the five service description entities is defined from top to bottom, and the service relationship between the entities is obtained in one mode, and in the other mode, the service resource is marked from top to bottom and then is led into the system, and the relationship between the minimum service units is calculated in the background.
Preferably, the knowledge fusion includes, but is not limited to, ontology alignment, entity fusion, attribute fusion, relationship integration.
Preferably, the invoking component platform applies a service list interface related to the function description, and part of software also needs to develop a data exchange interface in a targeted manner in the process of establishing a service directory of the business function tool.
Compared with the prior art, the invention has the beneficial effects that:
the knowledge graph construction method based on the oilfield business model takes the universality of oilfield business and the confidentiality of data of each oilfield company into consideration, and the KG0 graph can be suitable for the application of the whole oil and gas industry, so that the construction of each oilfield company is quickened, the KG1 graph is implemented in the interior of each oilfield function, and the data security is ensured.
Drawings
FIG. 1 is a flow chart of steps of a knowledge graph construction method of an oil and gas enterprise based on a petroleum business model;
FIG. 2 is a diagram showing the relationship among knowledge graph, KG0 and KG1 in the knowledge graph construction method of oil and gas enterprises based on the petroleum business model;
FIG. 3 is a schematic diagram of a petroleum business coordinate system of a knowledge graph construction method of an oil and gas enterprise based on a petroleum business model;
FIG. 4 is a schematic diagram of the relationship between service resources and minimum service units in the oil and gas enterprise knowledge graph construction method based on the petroleum service model;
FIG. 5 shows the detailed contents of objects, businesses, works, professions and businesses in the oil and gas enterprise knowledge graph construction method based on the petroleum business model;
fig. 6 is a data organization chart of the oil and gas enterprise knowledge graph construction method based on the petroleum business model.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present invention are included in the protection scope of the present invention.
1-6, an oil and gas enterprise knowledge graph construction method based on an oil service model comprises a knowledge graph construction method consisting of oil service coordinate system construction, oil service standard model construction, oil industry KG0 knowledge graph entity extraction and oil industry KG1 knowledge graph entity extraction;
the petroleum service coordinate system is built, a coordinate axis is required to be defined firstly, the petroleum data coordinate axis is a multi-dimensional (> 4-dimensional) quasi-space coordinate axis due to the complexity of petroleum service, each coordinate axis has a specific professional meaning, the combination of all coordinate axes forms a high-dimensional space coordinate point, and the coordinate point corresponds to a specific petroleum service data node;
the petroleum business is developed according to certain business standards and management rules, any one business is designed in advance, and corresponding to the development, any business data is also designed in advance. The petroleum business standard model is to convert the structure definition, relation, metadata and the like of the existing business functions in the petroleum industry into business data which can be stored and identified by a computer system, combine the business functions, the business function structures and a petroleum coordinate system to form the relation among the data, and the standard model is to define the storage structures of various resources, wherein the main resources of the standard model comprise: business data items, business data sets, case resources, method resources, standard specifications, business functions, business components, technical components, expert resources, business roles, and the like.
Extracting KG0 knowledge graph entity in petroleum industry: KG0 expresses the oil gas business work content and the business resource content related to the work content, namely the content constructed by the petroleum business standard model. The working content, the working relation and which data are generated or used in the whole oil gas service can be completely known through the hierarchy, and the extraction of KG0 entity data only needs to extract the service modeling content from the model management tool of the company and convert the service modeling content into data which can be stored in the graph database.
Extracting KG1 knowledge graph entity in petroleum industry: different from the construction of the knowledge graph of the Internet, the data of each oil field in the petroleum industry are secret data, the extraction of entity data is required in the oil field intranet, and the concepts of KG0 and KG1 are provided, so that the knowledge graph can be maximally suitable for the problem of secret oil field data.
The body of the KG1 knowledge graph can be regarded as KG0, and the data of oil field companies are extracted to the graph data according to the standard defined by KG 0; KG1 is holographic data record of actual business in a region, and the content of specific work development of an oil field and various data results after the work can be comprehensively known through the hierarchical knowledge graph.
The method specifically comprises the following steps:
the coordinate system is established according to five minimum service units of an object, a service, a work, a service process and a specialty, resources of the oil and gas industry are generated in the minimum service units, and a service model of the oil and gas industry can be abstracted into: service activity and service resources, and realizing the association of each service resource through a minimum service unit, thereby constructing a map network;
the method comprises the steps of carrying out service combing design from five bodies of an object, service, work, specialty and service process, developing and describing service activities from five aspects, wherein the object of the activities, the service related to the activities, the implemented work, the professional and work processes are generated in the service production process, and all used informationizable contents are service resources of a knowledge graph; the object, service, work, process, specialty, minimum service unit and service resource form an oil and gas industry body, and the detailed content refers to fig. 5, which is specifically as follows:
in the partitioning of objects: dividing according to work organization units or work objects in the business;
in the division of traffic: dividing oil and gas exploration and development according to the time dimension;
in the division of work: dividing the time nodes according to the working property;
in the division of business processes: flow nodes for business work subdivision, namely specific steps of work;
in the professional division: according to the technical method, the oil-gas geology major is divided into equal dimensions.
Defining the relation between every two of the five service description entities from top to bottom to obtain the service relation among the entities, or leading the service resource marked service entities into the system from top to bottom, and calculating the relation among the minimum service units by the background;
generating a theoretical minimum service unit according to the intersection point of the established service ontology relation; combining with actual service work, giving different attributes to theoretical service nodes, associating with the actual service work, selecting designated service nodes, and creating a unified description structure of various service resources generated under the service nodes to form a standard data dictionary;
in the process, unified business data modeling and model management tools are required for building the oil gas knowledge graph, including but not limited to business dimension management, minimum business unit management, data item management and business data set management;
establishing a relation among different dimensionalities of the business activity through a business activity dimensionality relation management tool, and establishing a hierarchy relation of the dimensionality through a tree structure of the dimensionality;
extracting data entities, data attributes, labels and data relations from structured and unstructured data according to the data organization structure of the oil gas knowledge graph, and referring to fig. 6 specifically;
dividing a large number of data sets into smaller subsets which are easier to manage and analyze, dividing a structured data table into dimensions defined by business model entities, and unlike the fact that the Internet needs to extract object entities from texts when constructing a knowledge graph, most of object instances in the petroleum industry are in corresponding structured databases, and all extraction technologies mainly are mapping extraction of the structured data table;
to segment the structured data table into dimensions defined by business model entities, a large number of data sets need to be segmented into smaller, more manageable and analyzable subsets, which can be created based on specific criteria or conditions;
acquiring the blood-edge relation of a business entity from the input and output results of the minimum business node or acquiring the blood-edge relation of entity data from the CR relation of the data item;
splitting according to chapter or title, content type and user rules, dividing a document into a plurality of smaller parts or paragraphs, and then organizing and marking the paragraphs according to categories, topics or other criteria;
for the standard of unstructured data generated by non-actual production services such as papers, news, conference materials and the like, the NLP technology is adopted to standard the data;
describing and accessing professional software data in various forms by using a unified model, providing conversion capability among data formats, dimension units and various space coordinates in the process of accessing and outputting, and opening the data to the outside in the form of a service interface;
the calling component platform applies a service list interface related to function description, establishes a service catalog of a service function tool, supplements a service resource description label related to the service function, and part of software also needs to develop a data exchange interface in a targeted manner;
and carrying out knowledge fusion, namely carrying out ontology and entity fusion on independent knowledge maps constructed by each department to form a unified, coordinated, richer and comprehensive map knowledge base, wherein the knowledge fusion process comprises, but is not limited to, ontology alignment, entity fusion, attribute fusion and relationship integration.
The extraction of the structured data is different from the fact that the Internet is used for constructing a knowledge graph, object entities need to be extracted from texts, most of object instances in the petroleum industry are in corresponding structured databases, and all extraction technologies are mainly used for mapping and extracting structured data tables.
And the relation between every two of the five service description entities is defined from top to bottom, one mode is to obtain the service relation between every two entities, and the other mode is to guide the service resource to the system after marking the service entity from top to bottom, and the relation between the minimum service units is calculated by the background.
And the calling component platform applies a service list interface related to function description, and part of software also needs to develop a data exchange interface in a targeted manner in the process of establishing a service catalog of the business function tool.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (6)
1. The oil and gas enterprise knowledge graph construction method based on the petroleum business model is characterized by comprising a knowledge graph construction method consisting of petroleum business coordinate system construction, petroleum business standard model construction, petroleum industry KG0 knowledge graph entity extraction and petroleum industry KG1 knowledge graph entity extraction;
the method comprises the following steps:
establishing a coordinate system according to five minimum service units of an object, a service, a work, a service process and a specialty;
carrying out service carding design from five bodies of an object, service, work, specialty and service process;
defining the relation between five business description entities from top to bottom to obtain the business relation among the entities;
generating a theoretical minimum service unit according to the intersection point of the established service ontology relation; the actual business work is combined, different attributes are given to the theoretical business nodes, and the theoretical business nodes are associated with the actual business work;
selecting a designated service node, and creating a unified description structure of various service resources generated under the service node to form a standard data dictionary;
establishing a relation among different dimensionalities of the business activity through a business activity dimensionality relation management tool, and establishing a hierarchy relation of the dimensionality through a tree structure of the dimensionality;
extracting data entities, data attributes, labels and data relations from structured and unstructured data according to the data organization structure of the oil gas knowledge graph;
dividing a large number of data sets into smaller, easier to manage and analyze subsets, and dividing the structured data table into dimensions defined by business model entities;
acquiring the blood-edge relation of a business entity from the input and output results of the minimum business node or acquiring the blood-edge relation of entity data from the CR relation of the data item;
splitting according to chapter or title, content type and user rules, dividing a document into a plurality of smaller parts or paragraphs, and then organizing and marking the paragraphs according to categories, topics or other criteria;
for the standard of unstructured data generated by non-actual production services such as papers, news, conference materials and the like, the NLP technology is adopted to standard the data;
describing and accessing professional software data in various forms by using a unified model, providing conversion capability among data formats, dimension units and various space coordinates in the process of accessing and outputting, and opening the data to the outside in the form of a service interface;
the calling component platform applies a service list interface related to function description, establishes a service catalog of a service function tool, supplements a service resource description label related to the service function, and part of software also needs to develop a data exchange interface in a targeted manner;
and carrying out knowledge fusion, namely carrying out ontology and entity fusion on independent knowledge maps constructed by each department to form a unified, coordinated, richer and comprehensive map knowledge base.
2. The oil and gas enterprise knowledge graph construction method based on the petroleum business model according to claim 1, wherein the object, the business, the work, the process, the specialty, the minimum business unit and the business resource form an oil and gas industry body.
3. The method for constructing oil and gas enterprise knowledge graph based on petroleum business model according to claim 1, wherein the extraction of the structured data is different from the extraction of object entities from text required for constructing knowledge graph by internet, most of object instances in petroleum industry are in corresponding structured database, and all extraction technologies are mainly mapping extraction of structured data table.
4. The method for constructing the oil-gas enterprise knowledge graph based on the petroleum service model according to claim 1, wherein the method is characterized in that the relationship between five service description entities is defined from top to bottom, the service relationship between the entities is obtained in one mode, the service resource is marked from top to bottom and then is led into the system, and the relationship between the minimum service units is calculated in the background.
5. The method for constructing the oil and gas enterprise knowledge graph based on the petroleum business model according to claim 1, wherein the knowledge fusion process comprises, but is not limited to, ontology alignment, entity fusion, attribute fusion and relationship integration.
6. The method for building the oil and gas enterprise knowledge graph based on the petroleum service model according to claim 1, wherein the invoking component platform applies the service list interface related to the function description, and part of software also needs to develop the data exchange interface in a targeted manner in the process of building the service catalog of the service function tool.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311750857.0A CN117648975A (en) | 2023-12-19 | 2023-12-19 | Oil-gas enterprise knowledge graph construction method based on petroleum business model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311750857.0A CN117648975A (en) | 2023-12-19 | 2023-12-19 | Oil-gas enterprise knowledge graph construction method based on petroleum business model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117648975A true CN117648975A (en) | 2024-03-05 |
Family
ID=90049425
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311750857.0A Pending CN117648975A (en) | 2023-12-19 | 2023-12-19 | Oil-gas enterprise knowledge graph construction method based on petroleum business model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117648975A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112862302A (en) * | 2021-02-03 | 2021-05-28 | 北京侏罗纪软件股份有限公司 | Petroleum data model modeling method and tool |
| CN113312494A (en) * | 2021-05-28 | 2021-08-27 | 中国电力科学研究院有限公司 | Vertical domain knowledge graph construction method, system, equipment and storage medium |
| CN113590835A (en) * | 2021-07-28 | 2021-11-02 | 上海致景信息科技有限公司 | Method and device for establishing knowledge graph of textile industry data and processor |
| CN115545558A (en) * | 2022-11-07 | 2022-12-30 | 中冶赛迪技术研究中心有限公司 | Method, device, machine readable medium and equipment for obtaining risk identification model |
| US20230214949A1 (en) * | 2021-12-30 | 2023-07-06 | FiscalNote, Inc. | Generating issue graphs for analyzing policymaker and organizational interconnectedness |
-
2023
- 2023-12-19 CN CN202311750857.0A patent/CN117648975A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112862302A (en) * | 2021-02-03 | 2021-05-28 | 北京侏罗纪软件股份有限公司 | Petroleum data model modeling method and tool |
| CN113312494A (en) * | 2021-05-28 | 2021-08-27 | 中国电力科学研究院有限公司 | Vertical domain knowledge graph construction method, system, equipment and storage medium |
| CN113590835A (en) * | 2021-07-28 | 2021-11-02 | 上海致景信息科技有限公司 | Method and device for establishing knowledge graph of textile industry data and processor |
| US20230214949A1 (en) * | 2021-12-30 | 2023-07-06 | FiscalNote, Inc. | Generating issue graphs for analyzing policymaker and organizational interconnectedness |
| CN115545558A (en) * | 2022-11-07 | 2022-12-30 | 中冶赛迪技术研究中心有限公司 | Method, device, machine readable medium and equipment for obtaining risk identification model |
Non-Patent Citations (2)
| Title |
|---|
| SHIYUE WANG 等: "Enriching BERT With Knowledge Graph Embedding For Industry Classification", 《SPRINGER LINK》, 2 December 2021 (2021-12-02) * |
| 李婷玉 等: "基于图卷积神经网络的石油数据资产知识图谱实体对齐方法", 《东北石油大学学报》, 30 June 2023 (2023-06-30) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7849049B2 (en) | Schema and ETL tools for structured and unstructured data | |
| EP1899855B1 (en) | System and method of making unstructured data available to structured data analysis tools | |
| JP2923552B2 (en) | Method of constructing organization activity database, input method of analysis sheet used for it, and organization activity management system | |
| CN113987212A (en) | Knowledge graph construction method for process data in numerical control machining field | |
| US20070011183A1 (en) | Analysis and transformation tools for structured and unstructured data | |
| de Vasconcelos et al. | An organisational memory information system using ontologies | |
| González et al. | Considering unstructured data for OLAP: a feasibility study using a systematic review | |
| CN117648975A (en) | Oil-gas enterprise knowledge graph construction method based on petroleum business model | |
| Burita et al. | K-gate ontology driven knowledge based system for decision support | |
| CN115934969A (en) | Construction method of immovable cultural relic risk assessment knowledge graph | |
| Sen et al. | Toward developing data warehousing process standards: An ontology-based review of existing methodologies | |
| Gujral et al. | Knowledge Graphs: Connecting Information over the Semantic Web | |
| Prasad et al. | Text analytics to data warehousing | |
| CN115687623B (en) | Industrial digital twin data space construction method and system | |
| Ni | An Intelligent Retrieval Algorithm for Digital Literature Promotion Information Based on TRS Information Retrieval | |
| Mastella et al. | Semantic exploitation of persistent metadata in engineering models: application to geological models | |
| Leshcheva et al. | Towards a method of ontology population from heterogeneous sources of structured data | |
| Harvilahti | Tradition Archives and the Challenges of the Digital World: From Exclusive Rules Towards Networks and Contexts | |
| Wang et al. | A Study of the Impact of Integrating Artificial Intelligence with the Archival Profession under Data Mining | |
| Nyberg et al. | Linking data for industrial knowledge management—a case study | |
| Huang et al. | Design and Implementation of Digital Platform Based on Standard Knowledge Map in Oil and Gas Exploration and Development Field | |
| Hui | Research on Construction of Armament Knowledge Graph by Protégé | |
| Faltin et al. | Concept for human-machine interfaces for resilient data extraction from digital twins | |
| Zhou et al. | A Knowledge Base of Shale Gas Play and Its Application on EUR Prediction by Integrating Knowledge Graph and Automated Machine Learning Techniques | |
| Baker et al. | NoSQL Databases and Big Data Query Processing: Comparative Analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |