3D graph and blood relationship visual display method based on knowledge graph

Abstract

The invention relates to the technical field of 3D graph and blood relationship visual display, in particular to a 3D graph and blood relationship visual display method based on a knowledge graph. According to the method, an online graph configuration and release platform is constructed and comprises a 3D graph visualization module, a 3D graph retrieval module and a 3D graph interaction module. According to the knowledge graph-based 3D graph and blood relationship visual display method, a 3D graph and blood relationship visual display assembly is created, corresponding data is presented in a three-dimensional mode through the visual effect of a 3D space, and a user can conveniently, visually and clearly know information.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D graphs and blood relationship visualization display, and in particular to a 3D graph and blood relationship visualization display method based on a knowledge graph. Background Art

[0002] Knowledge graphs began in the 1950s and have been roughly divided into three stages of development: the first stage (1955-1977) was the origin stage of knowledge graphs, during which citation network analysis began to become a common method for studying the development of contemporary science; the second stage (1977-2012) was the development stage of knowledge graphs, the semantic web developed rapidly, and the study of "knowledge ontology" began to become an important field of computer science. Knowledge graphs absorbed the concepts of semantic web and ontology in knowledge identification organization and expression, making knowledge easier to exchange, circulate and process between computers and between computers and people; the third stage (2012 to present) is the prosperous stage of knowledge graphs. In 2012, Google proposed Google Knowledge Graph, and the knowledge graph was officially named. Google improved the performance of its search engine through knowledge graph technology. With the vigorous development of artificial intelligence, key issues involved in knowledge graphs, such as knowledge extraction, representation, fusion, reasoning, question and answer, have been solved and breakthroughs have been made to a certain extent. Knowledge graphs have become a new hotspot in the field of knowledge services, attracting widespread attention from scholars and industry at home and abroad. Summary of the invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide a 3D graph and blood relationship visualization display method based on knowledge graph, to create a 3D graph and blood relationship visualization display component, to present the corresponding data in a three-dimensional form through the visual effect of 3D space, so as to facilitate users to understand the information intuitively and clearly.

[0004] To achieve the above-mentioned purpose, the present invention adopts the following technical solutions: constructing an online atlas configuration and publishing platform, including a 3D atlas visualization module, a 3D atlas retrieval module and a 3D atlas interaction module;

[0005] The 3D graph visualization module provides visualization services for 3D concept graphs, 3D data graphs, 3D table-level lineage, and 3D field lineage, and provides visualization services for massive data graphs in a three-dimensional presentation mode;

[0006] The 3D graph retrieval module provides convenient global search of graph nodes and data nodes based on the 3D graph, and can accurately locate and display the graph nodes searched by the user and the visual display of related associations and associated objects;

[0007] The 3D graph interaction module enriches the 3D graph interaction capability by providing 3D interaction services such as rotation, zooming in, zooming out, drilling, dragging, and other 3D interaction operation meanings.

[0008] Furthermore, semantic search technology using knowledge graphs can extract knowledge from structured, semi-structured and unstructured data through semantic annotation methods to capture the relationships between entities.

[0009] Furthermore, it includes knowledge extraction, knowledge fusion and semantic search components;

[0010] The knowledge extraction divides the data into structured, semi-structured and unstructured data according to different data formats, and uses different strategies and algorithms to extract knowledge for data in different formats;

[0011] The knowledge fusion, based on the knowledge extraction representation results, calculates the relationship between entities, constructs knowledge triples and builds a graph using a top-down construction method;

[0012] The semantic search uses key technologies of intent extraction, entity recognition, similarity judgment, and classification to perform semantic search.

[0013] Further, step 1: extracting ontology from data source, and conducting ontology concept learning and ontology relationship learning;

[0014] Step 2: Based on the above representation results, entity learning is performed through entity linking and entity filling;

[0015] Step 3: Based on the above entity learning representation results, construct a top-down knowledge graph.

[0016] The present invention has the following beneficial effects:

[0017] 1. The present invention constructs an online configuration and publishing capability for the graphic data structure of a configurable graph database, and provides a powerful online configuration and publishing capability for the construction and publishing of graphic data structures for three-dimensional graph visualization display needs. Users can dynamically define the data structures of nodes and edges through online configuration services according to business needs, and realize flexible storage and efficient query of graph data, so that users can complete the online management and maintenance tasks of complex graph data structures without in-depth understanding of the underlying technical details of the graph database, which greatly improves the efficiency, flexibility, ease of use and scalability of graph data management, and can better meet the needs and application scenarios of different users.

[0018] 2. The present invention constructs a massive data graph visualization capability based on 3D real-time dynamic rendering technology, realizes the real-time automatic rendering layout display of massive data graph nodes in three-dimensional space, provides friendly three-dimensional graph interaction capabilities, and balances the boundary between display effect and performance of massive three-dimensional graph display services, realizing efficient three-dimensional graph display and full-link blood relationship display. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0021] Build an online graph configuration and publishing platform, including 3D graph visualization module, 3D graph retrieval module and 3D graph interaction module;

[0022] The 3D graph visualization module provides visualization services for 3D concept graphs, 3D data graphs, 3D table-level lineage, and 3D field lineage, and provides visualization services for massive data graphs in a three-dimensional presentation mode;

[0023] The 3D graph retrieval module provides convenient global search of graph nodes and data nodes based on the 3D graph, and can accurately locate and display the graph nodes searched by the user and the visual display of related associations and associated objects;

[0024] The 3D graph interaction module enriches the 3D graph interaction capability by providing 3D interaction services such as rotation, zooming in, zooming out, drilling, dragging, and other 3D interaction operation meanings.

[0025] Furthermore, semantic search technology using knowledge graphs can extract knowledge from structured, semi-structured and unstructured data through semantic annotation methods to capture the relationships between entities.

[0026] Furthermore, it includes knowledge extraction, knowledge fusion and semantic search components;

[0027] The knowledge extraction divides the data into structured, semi-structured and unstructured data according to different data formats, and uses different strategies and algorithms to extract knowledge for data in different formats;

[0028] The knowledge fusion, based on the knowledge extraction representation results, calculates the relationship between entities, constructs knowledge triples and builds a graph using a top-down construction method;

[0029] The semantic search uses key technologies of intent extraction, entity recognition, similarity judgment, and classification to perform semantic search.

[0030] Further, step 1: extracting ontology from data source, and conducting ontology concept learning and ontology relationship learning;

[0031] Step 2: Based on the above representation results, entity learning is performed through entity linking and entity filling;

[0032] Step 3: Based on the above entity learning representation results, construct a top-down knowledge graph.

[0033] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0034] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0035] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0036] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0037] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A 3D graph and blood relationship visualization display method based on knowledge graph, characterized in that: The following steps are involved: Build an online graph configuration and publishing platform, including 3D graph visualization module, 3D graph retrieval module and 3D graph interaction module; The 3D graph visualization module provides visualization services for 3D concept graphs, 3D data graphs, 3D table-level lineage, and 3D field lineage, and provides visualization services for massive data graphs in a three-dimensional presentation mode; The 3D graph retrieval module provides convenient global search of graph nodes and data nodes based on the 3D graph, and can accurately locate and display the graph nodes searched by the user and the visual display of related associations and associated objects; The 3D graph interaction module enriches the 3D graph interaction capability by providing 3D interaction services such as rotation, zooming in, zooming out, drilling, dragging and the like.

2. According to claim 1, a 3D graph and blood relationship visualization display method based on knowledge graph, characterized in that: The 3D atlas visualization module is as follows: Semantic search technology using knowledge graphs extracts knowledge from structured, semi-structured, and unstructured data through semantic annotation methods to capture the relationships between entities.

3. According to claim 2, a 3D graph and blood relationship visualization display method based on knowledge graph is characterized in that: The semantic search technology of the knowledge graph is specifically as follows: including knowledge extraction, knowledge fusion and semantic search; The knowledge extraction divides the data into structured, semi-structured and unstructured data according to different data formats, and uses different strategies and algorithms to extract knowledge for data in different formats; The knowledge fusion, based on the knowledge extraction representation results, calculates the relationship between entities, constructs knowledge triples and builds a graph using a top-down construction method; The semantic search uses key technologies of intent extraction, entity recognition, similarity judgment, and classification to perform semantic search.

4. According to claim 3, a 3D graph and blood relationship visualization display method based on knowledge graph is characterized in that: According to the results of knowledge extraction, the relationship between entities is calculated, knowledge triples are constructed, and a top-down construction method is used to build a graph, as follows: Step 1: Extract the ontology from the data source and learn the ontology concepts and ontology relationships; Step 2: Based on the above representation results, entity learning is performed through entity linking and entity filling; Step 3: Based on the above entity learning representation results, construct a top-down knowledge graph.

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