AnTuo Knowledge Graph Platform

The TRS “AnTuo” Knowledge Graph Platform is a universal domain big data knowledge graph product. It is based on dynamic ontology and knowledge graph theory, with high throughput and millisecond-level response as the design baseline. It can easily construct knowledge graphs that computers can understand within various industry domains. It integrates, correlates, annotates, and knowledgeizes massive, heterogeneous data sources, forming large-scale knowledge graphs covering objects, attributes, relationships, events, documents, and multimedia within the domain.

The platform provides intelligent analysis and operations for knowledge graphs with hundreds of billions of nodes. It includes rich and powerful knowledge browsing, exploration, and management tools, achieving goals such as knowledge construction management, semantic retrieval, intelligent text extraction, intelligent question answering, intelligent recommendation, graph relationship analysis, geographic spatial analysis, and knowledge management, thereby improving the level of business analysis intelligence.

Simultaneously, it supports collaborative applications among different institutions, functions, and regions, providing external knowledge graph application interfaces and supporting the use of third-party extension applications, effectively supporting more general and specific domain application scenarios.

Product Advantages

Flexible Data Model Construction

The knowledge graph platform offers a flexible and adaptable data model, enabling quick adaptation to various business scenarios without the need for independent development or configuration. This flexibility is crucial for driving innovation in the application of domain knowledge within business fields.

Knowledge Construction Based on Graphs

It can meet the integration of resources around business goals, such as data integration, adding data sources, and adapting to changes in data structure, enabling targeted knowledge processing and easily coping with the expansion and evolution of knowledge structures.

Text Analysis Based on NLP

Utilize NLP technology to intelligently analyze documents, supporting the automatic extraction of objects, their attributes, and relationships from documents. This process combines manual confirmation and annotation to refine knowledge objects.

Knowledge Computation and Reasoning Coverage

The platform is equipped with high-performance intelligent algorithms such as entity alignment, frequent subgraph mining, deep knowledge exploration, and knowledge inference. It also supports user-customized configuration of graph mining models.

Product Functions

Knowledge Graph Management

Supports the construction and management of knowledge graphs, including ontology management, knowledge construction, knowledge construction engines, and knowledge graph repository statistics; supports unified management of ontologies; supports the integration of different types of data to improve the speed of business data access and knowledge base construction.

Knowledge Computation and Services

Provides functionalities such as text mining engine, map parsing engine, computational model management, tag calculation engine, image analysis engine, and knowledge graph services on top of the knowledge graph repository.

Knowledge Graph Application Management

Provides rich and powerful knowledge management tools, including search configuration, encyclopedia management, image comparison, map parsing, and strategy expansion functionalities.

Knowledge Graph Search Application

Provides knowledge exploration and traversal, including knowledge graph-based search, semantic retrieval, full-text search, object profile image retrieval, encyclopedia browsing, and editing.

Knowledge Graph Analysis Application

Provides analysis applications based on knowledge graphs, including self-service statistical analysis, graph correlation analysis, map statistical analysis, thematic model analysis, and collaborative work analysis.

Application Scenario

Public Safety Domain

Open Source Intelligence Domain

Intelligent Customer Service Domain

Network Defense Domain

Financial and Insurance Services Domain

Anti-Financial Crime Domain

Medical Knowledge Graph Domain

Water Resources Industry Domain

For the public safety sector, a unified knowledge processing platform is established for massive heterogeneous data from various sources within the security department. This platform enables normalized data correlation mining and analysis. Through knowledge semantic retrieval, relevant individuals and objects can be quickly and accurately identified. Additionally, with functionalities like graph analysis and geographic spatial analysis, the platform visualizes complete chains of evidence, significantly enhancing the efficiency of investigation and case solving in the security department.

In the open-source intelligence domain, knowledge graph technology is utilized to integrate open-source intelligence data. This facilitates the construction of structured knowledge networks related to target individuals, organizations, events, etc., thereby forming chains of intelligence object knowledge. Such integration provides technical support for the long-term management of intelligence operations.

In the intelligent customer service domain, leveraging the data correlation capabilities of knowledge graphs effectively addresses the diversity of customer inquiries, improving the accuracy of responses to informal queries such as greetings and colloquial language. With the support of knowledge graph technology, artificial intelligence algorithms such as machine learning are applied, enabling chatbots to continuously learn from public inquiries. This allows them to address unknown questions and enhance intelligent guidance services, facilitating the self-evolution of chatbots.

In the field of network defense, heterogeneous network security data is transformed into a knowledge graph, enabling the establishment of a comprehensive security monitoring profile. This facilitates behavior analysis of virus propagation, application associations, personnel activities, and terminal devices, thereby achieving comprehensive and multidimensional analysis of threat origins. This approach allows for accurate identification of attack sources and paths, ensuring network security.

In the financial and insurance services domain, constructing chain-like and circular customer group views and generating relationship graphs such as corporate investment relationships, guarantee relationships, fund transactions, and industry status relationships. This provides commercial banks with precise and reliable marketing and risk control bases, thereby empowering smart finance.

In the field of anti-fraud and anti-money laundering in finance, through the analysis of massive transaction data, potential money laundering patterns are explored, discovered, modeled, matched, cross-referenced, and verified. This reveals the true nature of fraudulent and money laundering behaviors, greatly enhancing the efficiency of anti-fraud departments.

In the medical domain, doctors can explore and research family medical history networks, query similar cases, medical treatment history cases, and reference treatment plans. Patients can search for symptoms related to diseases, possible diseases corresponding to symptoms, treatment drugs, doctors, similar cases, etc. Health and disease control departments can analyze epidemic trends and trace the origins of imported infectious diseases.

In the water resources domain, support is provided for comprehensive knowledge research and management, offering functions such as knowledge modeling, knowledge construction, visual analysis, expert decision-making, and knowledge reasoning throughout the entire cycle of water resources business. By constructing a knowledge graph in the water resources domain, users in the water resources industry are assisted in making intelligent decisions and precise management. This service covers areas such as water resource planning, water resource management, and hydraulic engineering, helping to improve work efficiency and decision quality.