Product Overview

TRS Knowledge Graph Platform is a general-purpose domain-specific big data knowledge graph product. Based on the theories of dynamic ontology and knowledge graph, and with massive throughput and second-level response as its design baseline, it can easily construct machine-understandable knowledge graphs in industry-specific domains. It integrates, associates, annotates, and intellectualizes massive multi-source heterogeneous data, forming a large-scale knowledge graph that covers objects, attributes, relationships, events, documents, and multimedia within the domain. The platform provides intelligent analysis and operation functions for knowledge graphs with ten-billion-level nodes. It has built-in rich and powerful knowledge browsing, exploration, and management tools to realize knowledge construction and management, knowledge semantic retrieval, intelligent text extraction, intelligent question answering, intelligent recommendation, graph relationship analysis, geospatial analysis, and knowledge management. These functions aim to improve the intelligence level of business analysis. Meanwhile, the platform supports collaborative applications among users from different institutions, functional departments, and regions. It also provides external knowledge graph application interfaces to support third-party extended applications, effectively enabling more general and domain-specific application scenarios.

Core Functions

: Ontology Definition Supports dynamic ontology construction, allowing flexible modeling of domain-related information.

: Knowledge Construction Configuration Management Through a set of visual configuration interfaces, data from data sources is mapped to the ontology objects and attributes in the Knowledge Graph Database, and then stored in the database.

: Intelligent Retrieval Provides a retrieval service that closely resembles a question-answering format. It helps users quickly find object entity information and their associated relationships (such as people, events, and things) related to the analysis target in the retrieval think tank, providing support for business analysis and other application systems.

: Relationship Graph Analysis Utilizes visual analysis technologies and adopts neural network algorithms, clustering similarity algorithms, Bayesian networks, and regression analysis algorithms. It deeply explores the common elements and connections between knowledge objects, reveals in-depth relationships in data, and presents these relationships through various visual presentation methods, thereby providing the most intuitive, convenient, and efficient application display.

: Map Analysis With big data analysis results as data support, it realizes display and interactive analysis based on Geographic Information System (GIS) maps. It provides operations such as view manipulation, map marking, layer management, selection tools, attribute query, data collection map distribution, and map drilling. It also supports displaying data analysis results through color temperature maps, heat maps, and route maps.

: Encyclopedia Management Supports importing encyclopedia knowledge, and enables classified management, entry editing, and entry display of encyclopedia information in the resource library.

Product Advantages

Knowledge Graph Based on Dynamic Ontology The platform is designed based on a dynamic ontology architecture. It can address resource integration needs such as data integration, data source expansion, and data structure changes centered on business objectives. This enables goal-oriented knowledge processing and easily adapts to the expansion and evolution of knowledge structures.
Powered by Advanced LLM and NLP Technologies Leveraging Large Language Models (LLM), Natural Language Processing (NLP), and deep learning technologies, it automatically extracts objects, attributes, and relationships from documents to build multi-dimensional entity-relationship networks. This enables rapid data correlation analysis, early warning, and prediction. Meanwhile, it supports intelligent document parsing, keyword extraction, classification, and summarization, and enhances knowledge processing capabilities through integration with manual annotation.
Built-in Knowledge Mining and Reasoning Algorithms The platform comes with high-performance graph mining algorithms, including entity alignment, frequent subgraphs, in-depth knowledge exploration, tightness centrality, and knowledge reasoning. It also supports users in customizing and configuring graph mining models.
Internal Knowledge Sharing and Collaborative Analysis The platform supports collaborative sharing among multiple users across different locations and departments. It enables intelligence transmission and knowledge sharing between relevant organizations, eliminating information silos and improving overall operational efficiency. Additionally, it provides a comprehensive knowledge permission management mechanism to ensure data security and prevent unauthorized access and tampering.
Scalable and Highly Available Service Architecture Adopting a scalable and extensible distributed big data framework, the platform effectively addresses pain points such as high performance, high availability, and high concurrency in massive data access. This ensures the robust operation of the knowledge graph under the pressure of massive data.
Compliance with Information System Security Protection Requirements The platform employs measures such as encrypted transmission, atomic-level control, and authorized interface access. These effectively safeguard knowledge content security, interface security, deployment security, and transmission security, aligning with national information system security protection standards.
Multi-Tenant Space Support TRS Knowledge Graph Platform supports multi-tenant functionality under a unified ontology to achieve data isolation between tenants. By deploying multiple instances of the TRS Knowledge Graph application, it can support multi-tenancy with different ontologies under unified storage.
Support for Various Domestic Adaptations The platform adopts an open hybrid data architecture. It can be adapted to big data components from domestic cloud service providers such as Huawei and Alibaba, and also supports a wide range of domestic information technology innovation (ITI) hardware environments.

Application Scenarios

Public Security Field Open-Source Intelligence (OSINT) Field Financial Field Industrial Development Field

For massive multi-source heterogeneous data of public security management departments, the platform conducts unified knowledge-based processing to form a normalized data association mining and analysis platform. Through knowledge semantic retrieval, it can quickly and accurately locate relevant personnel and items. Then, by leveraging graph analysis and geospatial analysis functions, it visually presents complete evidence chains in real time, effectively improving the efficiency of investigation and case-solving for public security departments.