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Saltlux Technology

Graph DataBase

Advanced technology that combines knowledge graph with artificial intelligence
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Reasoning ability

Multiple data models processing function

High quality

Ultra-large volume graph data maintenance function

Saltlux Technology - Dữ liệu đồ thị Graph DB

Insight

Graph data analysis function

Prediction ability

Multiple data models support function and data diagnosis, prediction, analysis function

Saltlux Technology - graphDB
saltlux technology

Graph DataBase Technology

Automatically converts correlations between data

Create and store big data as knowledge graph structures

Integrated management, analysis, and direct application of big data

Enables the generation, management, and intelligent analysis (prediction and reasoning) of the best knowledge graph data

Also includes data integration, risk management, content recommendation, data opening, large-scale reasoning, and selective use of add-on packages

main features

Graph Database provides all functions corresponding to the lifecycle of graph data, such as the conversion functions to convert data into graph data, graph data storage, graph data-based Geodata analysis, knowledge network analysis, graph data open functions, and visualization and operation management.

Converge and commercialize Asia's first Al and Graph Database technologies

Verification functionality of 108 ultra-large volume graph data processing

Powerful built-in commercial reasoning engine functionality

Powerful built-in knowledge extraction and conversion functionality

Guarantee stability according to experience in securing maximum Graph Database references and execution

why do we need graph database technology

Graph Database Technology selects and applies an optimized solution according to the business environment, which depends significantly on data inflow speed and data volume possessed by a company or an organization. Graph Database Technology is the best solution that creates a new market based on graph data. It finds a customer and raises the customer’s economic value by satisfying the customer’s needs and providing optimized services.

cơ sở đồ thị dữ liệu graph db database - saltlux technology

Our Graph Database engines

Discover Saltlux Technology's tools that apply Graph Database technology

SLT-GraphDB-công cụ chuyển đổi dữ liệu đồ thị
Graph DB Conversion Engine
This tool generates data corresponding to the knowledge graph, through mapping data sources (DBMS, CSV, RDF,...). It also provides structured data like RDB, through W3C's R2RML language, and RML language (internal data transformation rules). Moreover, the tool can convert and process user data into a virtual data view (Data View). Users can perform data transformation quickly and easily through the graph database conversion tool. Main functions:
  • Supports multiple formats for data conversion
  • Enhances and transforms extremely large graph data
  • Manages data transformation tools
See Technical Information
SLT-GraphDB-công cụ lưu trữ dữ liệu đồ thị
Graph DB Storing Engine
Graph Database storing engine provides RDF-based graph data storage and Property Graph storage, providing Property Graph storage and analysis through Apache server linkage TinkerPop and Gremlin. Graph data based on RDF is represented as triples, Property Graph is represented as Vertex, Edge and Verte, Edge can carry properties. Main functions:
  • Secure large-scale graph data storing management efficiency and data interoperability
  • Distinct analysis based on SPARQL query
  • Scalable through large distributed environments
  • Access easily through management tools
See Technical Information
SLT-graphDB-công cụ lý luận dữ liệu đồ thị
Graph DB Reasoning Engine
Graph Database reasoning engine allows transforming and storing new facts from transformed data through reasoning. In particular, this tool can provide schema axiom and instance axiom for high-low-level reasoning and relational reasoning between concepts in the data model. Main functions:
  • Embedded axiom and rule-based reasoning
  • Support various knowledge expression languages
  • Graph data augmentation and verification
See Technical Information
SLT-graphDB-công cụ phân tích dữ liệu đồ thị
Graph DB Analysis Engine
Graph database analysis engine acts as a knowledge finder, including a knowledge graph generator for external information (social media, email, knowledge management systems, etc.). It generates graph index for knowledge network analysis, knowledge network analyzer by user, by unit time and by topic, topic network analyzer for topic analysis of contents distributed on the network, interested topic tendency analyzer by unit time, and knowledge retriever. Main functions:
  • Robust built-in graph data analysis
  • Large-scale graph data analysis performance
  • Extended graph data analysis
See Technical Information

graph database technology for saltlux inc. customers

Discover Graph Database technology-based products that Salltux Inc. developed for major customers around the world.

Though “people's safety” is the government's main administration strategy, it is difficult to prevent persistent crimes in the long run with only temporary human resources and budget investment (increasing public safety manpower, CCTV installation, campaign reinforcement, etc.). Crime prevention activities are carried out by each government agency.
An intelligent system can strengthen the preemptive prevention-based criminal justice system by establishing a scientific crime analysis system. It can effectively prevent crime by analyzing a crime in detail, sharing information, and cooperating with relevant authorities. Also, it creates a close joint cooperation system between people, the government, and regions to educate, investigate, manage post-offenders, and enforce victims’ and assailants’ normal rehabilitation within society. To solve the big problems of these two points of view, a scientific crime analysis-based system and crime graph data-based intelligent crime analysis system are established, along with the basis of a crime prevention cooperation system.
 
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Project Information
  1. Establish a scientific crime analysis base system: 
    - Establish structured and unstructured data convergence infrastructure for five violent crimes (murder, sexual assault, robbery, arson, death resulting from bodily injury through assault)
    - Text analysis of approximately 800 semi-structured and unstructured documents
    - Design various scenarios that support crime analysis
    - Analyze criminal evidence, such as voice, video, or image
    - Technical methodology research (external POC execution)
  2. Establish an intelligent crime analysis knowledge base: 
    - Establish base materials for deep crime analysis
    - Establish a concept dictionary and crime classification system for deep crime analysis
    - Set up machine learning data and a knowledge expression system to automate in-depth crime analysis
  3. Prepare the basis for a crime prevention cooperation system between relevant authorities:
    - Identify the required information for relevant authorities to establish crime prevention
    - Prepare a linkage base method with relevant authorities
Applied technologies and solutions
  • Apply Graph Database for graph data generation, storing, reasoning, crime dictionary, and information.
  • Apply big data storing and the search engine (DISCOVERY) for the intelligent semantic search service, unstructured analysis engine (TMS), and cognitive analysis engine (CAS) for collected unstructured data analysis.
  • Apply OpenSource Apache SPARK, STORM, and KAFKA in the intelligent crime prevention analysis system.
Validity of product selection
1. Ontology-based real-time analysis (a data model represents a domain that is used to make inferences about objects in that domain and the relationships between them) and the importance of relationship visualization between objects
2. The importance of visualization of scientific investigations, criminal policies, and crime prevention-related analysis results
3. The importance of crime taxonomy configuration specializing in Korean crimes

Customer Happiness Center and all business branches of the NongHyup Bank have improved their knowledge management system to manage and share business knowledge needed for customer consultation. Functions such as knowledge generation, management, and search are improved to increase convenience. When knowledge contents are generated, they are managed by particular business fields and properties. Later, these contents will be stored after being converted into a Knowledge-Graph based knowledge base. This converts natural language content into knowledge data in a machine-readable format. AI-based automatic knowledge extraction is also applied as part of the process.

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Project Information

The knowledge management system was formerly designed to generate, store, manage the business knowledge for customer consultation. It is now used to share knowledge between bank consultants and/or branch employees. However, the demand for AI-based services is continuously increasing as bank customers now use many different digital channels. As a result, it is crucial to establish and manage the AI-based knowledge data separately for AI services. The previous knowledge contents and AI-based knowledge data had different shapes and structures, making it difficult to maintain the same information if they were managed separately. Duplicated knowledge generation, management, and use would cause inconvenience to users in many ways. The aim is to improve the convenience of managing and sharing consultation knowledge and information between employees. This can be done by reforming the outdated 10-year knowledge management system into an intelligent one that meets the latest AI technology trends. Users also can improve management efficiency and knowledge utilization by creating and managing knowledge data in AI-based services.

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Applied technologies and solutions
1. Knowledge graph-based knowledge data generation and management
An intelligent knowledge management system not only generates knowledge content but also generates and manages knowledge data in a machine-readable format, aligning with the knowledge graph structure. This knowledge base can be used for various AI-based services.
 
2. Automatic knowledge extraction from unstructured text
Knowledge contents are prepared as sentences or paragraphs in a format (TEXT or HTML) that is easily understood by users. KENT (Knowledge Extraction from Natural Language Text) technology automatically extracts and converts knowledge data from those unstructured texts based on a learned model.
Main accomplishments
1. Linkage between NongHyup Bank and NongHyup Card's intelligent knowledge management system and the Q&A system
Intelligent knowledge management systems, which were established separately for NongHyup Bank and NongHyup Card to manage separate contents and knowledge sharing. Also, an environment to deliver knowledge data to employees is created through the linkage between the AI Q&A system used by the consultation advisers in the Call Center.
 
2. Automatic knowledge extraction technology commercialization
Automatic knowledge extraction is a technical field that needs a lot of R&D effort. Various measures, including language processing and machine learning, were studied to improve quality and obtain satisfactory results in the extraction of required knowledge from the contents from NongHyup Bank. It can be considered a successful case of automatic knowledge extraction application to an actual service.
Project Information

Aiming at “the development of AI-based personalized knowledge consultation services for HACCP intelligent information services”, a knowledge base based on close HACCP certified safe food is established using raw materials and nutritional content data. It also provides a theme-based personalized safe food recommendation service.

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Applied technologies and solutions
1. Q&A Manager: Provide user query processing and interface (Open API)
2. KBQA: FBQA, TBQA, and Plugin
FBQA: Fact-based query processing
TBQA: Template-based query processing, a complicated or complex query processing
Plugin: Query processing for real-time changed data, such as weather, news and world time, etc.
3. IRQA: Find other similar questions through text analysis and provide rated answers
4. KB (Ontology): Open-domain knowledge base for query processing (OWL ontology: RDF + Axioms)
5. NLU: Natural language query analysis and pattern recognition
6. NLG: Create natural language queries to obtain query processing results
7. Analysis dictionary management: Manage resources for query analysis and query patterns
8. Q&A management: KB management, query testing, Q&A evaluation, KBQA analysis dictionary management
Validity of Product Selection
1. Use the repository for storing a knowledge graph
2. Use the machine learning solution to extract knowledge from unstructured documents
Main accomplishments
1. Provide theme-based customized food information to users
2. Increase access to information vulnerable people and improve the right to know about food safety
3. Save time and costs to obtain information regarding intelligent safe food services
4. Expected cost savings in terms of civil consultation and technical support of intelligent HACCP consultation service
Project Information

Establish an information channel to link various business information owned by other government departments and public institutions, based on the intellectual property rights information owned by Korean Intellectual Property Office.

  1. Link business information owned by government departments and public institutions.
  2. Converge intellectual property rights information owned by Korean Intellectual Property Office and business information, and provide customized information.
  3. Provide customized reports based on established intellectual property rights information and business information.
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Applied technologies and solutions
1. Extract text mining keyword
2. Trend analysis and TopN analysis
3. Knowledge graph application for correlation analysis
Validity of Product Selection
1. Use the repository to store the knowledge graph
2. Use the machine learning solution to extract knowledge from unstructured documents
3. Text mining and time series analysis technology solution for trend analysis
 
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Main accomplishments
1. Open more than five million cases of domestic and overseas patent information
2. Provide three million DB linkage information patent analysis cases from thirteen institutions
3. Provide analysis service for each product type
4. Provide a customized analysis report for companies
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