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Call Us400-6240-800
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Email Usbusiness@percent.cn
Product Overview
PERCENT Knowledge Graph Construction System is a one-stop knowledge graph construction tool for structured, semi-structured and unstructured data. It provides business modeling, knowledge extraction, knowledge fusion, knowledge storage and other knowledge graph lifecycle management functions to help customers quickly build industry knowledge graph, deeply mine the correlation value between data, and promote the intelligent upgrade of business.
Product Advantages
One-stop Knowledge Graph Construction
The integrated knowledge graph platform supports the rapid ontology design, knowledge extraction and knowledge mapping of the whole process of knowledge graph to realize the construction of entity graph and text knowledge graph.
Comprehensive Functions
It supports rich information extraction and knowledge fusion schemes. The knowledge map can be updated in full and increments at any time to ensure the reliability and timeliness of knowledge.
Low Threshold and High Performance
The product supports flexible deployment modes, including single-node deployment and cluster deployment. The single-node deployment mode supports the 10-million-level vertex graph, and the cluster deployment mode supports linear performance expansion.
One-stop Knowledge Graph Construction
The integrated knowledge graph platform supports the rapid ontology design, knowledge extraction and knowledge mapping of the whole process of knowledge graph to realize the construction of entity graph and text knowledge graph.
Comprehensive Functions
It supports rich information extraction and knowledge fusion schemes. The knowledge map can be updated in full and increments at any time to ensure the reliability and timeliness of knowledge.
Low Threshold and High Performance
The product supports flexible deployment modes, including single-node deployment and cluster deployment. The single-node deployment mode supports the 10-million-level vertex graph, and the cluster deployment mode supports linear performance expansion.
One-stop Knowledge Graph Construction
The integrated knowledge graph platform supports the rapid ontology design, knowledge extraction and knowledge mapping of the whole process of knowledge graph to realize the construction of entity graph and text knowledge graph.
Comprehensive Functions
It supports rich information extraction and knowledge fusion schemes. The knowledge map can be updated in full and increments at any time to ensure the reliability and timeliness of knowledge.
Low Threshold and High Performance
The product supports flexible deployment modes, including single-node deployment and cluster deployment. The single-node deployment mode supports the 10-million-level vertex graph, and the cluster deployment mode supports linear performance expansion.
Product Functions
Ontology Modeling
It supports the visualization construction of knowledge graph ontology, realizes the definition and management of entity, attribute and relationship in a graphical way. Based on the constructed ontology model, it completes the semantic annotation of knowledge and realizes the connection management of meta-knowledge and ontology to provide the foundation for the generation of knowledge graph.
Knowledge Extraction
For structured and semi-structured data, the configuration interface is provided to achieve automatic knowledge extraction based on data source configuration; for unstructured text data, the RTE is automatically completed through algorithm model based on text recognition technology and industry standard word library.
Knowledge Fusion
It supports entity alignment, normalization, conflict disambiguation and other knowledge fusion for heterogeneous knowledge from multiple sources. The knowledge mapping between knowledge and ontology is completed through configuration, and the map instantiation is quickly realized. With the ability to assess knowledge quality, it can discover and correct wrong knowledge, expired knowledge, etc. timely to ensure knowledge quality.
Knowledge Storage
Multiple knowledge storage solutions are provided based on service scenarios, including multi-mode storage, distributed storage, and mixed storage to provide flexibility, diversity, and expansibility of knowledge storage.
Knowledge Services
It provides knowledge graph query service interface, which facilitates direct use of knowledge graph application; it provides visual services and supports the man-machine interactive way of graph analysis mining; it supports online empowerment of business experts, transforming professional experience into entity, relationship and other graph information to enrich the knowledge graph system.
Product Functions
Ontology Modeling
It supports the visualization construction of knowledge graph ontology, realizes the definition and management of entity, attribute and relationship in a graphical way. Based on the constructed ontology model, it completes the semantic annotation of knowledge and realizes the connection management of meta-knowledge and ontology to provide the foundation for the generation of knowledge graph.
Knowledge Extraction
For structured and semi-structured data, the configuration interface is provided to achieve automatic knowledge extraction based on data source configuration; for unstructured text data, the RTE is automatically completed through algorithm model based on text recognition technology and industry standard word library.
Knowledge Fusion
It supports entity alignment, normalization, conflict disambiguation and other knowledge fusion for heterogeneous knowledge from multiple sources. The knowledge mapping between knowledge and ontology is completed through configuration, and the map instantiation is quickly realized. With the ability to assess knowledge quality, it can discover and correct wrong knowledge, expired knowledge, etc. timely to ensure knowledge quality.
Knowledge Storage
Multiple knowledge storage solutions are provided based on service scenarios, including multi-mode storage, distributed storage, and mixed storage to provide flexibility, diversity, and expansibility of knowledge storage.
Knowledge Services
It provides knowledge graph query service interface, which facilitates direct use of knowledge graph application; it provides visual services and supports the man-machine interactive way of graph analysis mining; it supports online empowerment of business experts, transforming professional experience into entity, relationship and other graph information to enrich the knowledge graph system.
Product Functions
Ontology Modeling
It supports the visualization construction of knowledge graph ontology, realizes the definition and management of entity, attribute and relationship in a graphical way. Based on the constructed ontology model, it completes the semantic annotation of knowledge and realizes the connection management of meta-knowledge and ontology to provide the foundation for the generation of knowledge graph.
Knowledge Extraction
For structured and semi-structured data, the configuration interface is provided to achieve automatic knowledge extraction based on data source configuration; for unstructured text data, the RTE is automatically completed through algorithm model based on text recognition technology and industry standard word library.
Knowledge Fusion
It supports entity alignment, normalization, conflict disambiguation and other knowledge fusion for heterogeneous knowledge from multiple sources. The knowledge mapping between knowledge and ontology is completed through configuration, and the map instantiation is quickly realized. With the ability to assess knowledge quality, it can discover and correct wrong knowledge, expired knowledge, etc. timely to ensure knowledge quality.
Knowledge Storage
Multiple knowledge storage solutions are provided based on service scenarios, including multi-mode storage, distributed storage, and mixed storage to provide flexibility, diversity, and expansibility of knowledge storage.
Knowledge Services
It provides knowledge graph query service interface, which facilitates direct use of knowledge graph application; it provides visual services and supports the man-machine interactive way of graph analysis mining; it supports online empowerment of business experts, transforming professional experience into entity, relationship and other graph information to enrich the knowledge graph system.
Application Scenario
Based on graph database knowledges and combined with AI technology, it provides fast retrieval, relevancy ranking and intelligent information filtering to accurately understand users’ intentions and quickly give target object results.
Through knowledge graph query reasoning, combined with entity recognition link, multi-round dialogue, language generation and other related technologies, the entity and relationship of user questions are identified, and the intention of user questions is understood, mapping into knowledge graph query language. Next, natural statements are generated and returned to the user.
Combined with the entities in the knowledge graph and the relationship structure between entities, real-time personalized recommendation of the full link can be realized. According to the browsing history and preferences of users, real-time feedback can be given to the target objects that meet the preferences of users in the search results.
Based on graph database knowledges and combined with AI technology, it provides fast retrieval, relevancy ranking and intelligent information filtering to accurately understand users’ intentions and quickly give target object results.
Through knowledge graph query reasoning, combined with entity recognition link, multi-round dialogue, language generation and other related technologies, the entity and relationship of user questions are identified, and the intention of user questions is understood, mapping into knowledge graph query language. Next, natural statements are generated and returned to the user.
Combined with the entities in the knowledge graph and the relationship structure between entities, real-time personalized recommendation of the full link can be realized. According to the browsing history and preferences of users, real-time feedback can be given to the target objects that meet the preferences of users in the search results.
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