NLPIR map technology system implementation knowledge of enterprise application service scenarios

Knowledge Mapping breakthrough achieved in the application of industry specific performance in knowledge representation, knowledge acquisition and knowledge application three levels. In the knowledge representation level, industry knowledge map applications breadth, depth and size both traditional knowledge and patterns differ. Mapping knowledge in some of the following typical enterprise service scenarios can produce results than expected:
Supply chain optimization: the process of production of goods usually want to buy a variety of raw materials, auxiliary materials and semi-finished products, how centralized procurement, how to find inexpensive suppliers, how to keep abreast of suppliers, rely on knowledge of the non-standard map technology-based and unstructured data analysis techniques. For example, the automatic collection and comparison of each raw materials prices and sales in various electricity providers and channels automatically collect and compare the bidding documents or the most successful supplier in the industry to find the best price for a product and services and even the different plants in different ERP systems category of raw materials combined to centralized procurement.
Financial Legal: financial, tax and legal and other related aspects of business, as it relates to handling a large number of professional documents, and can play an important role in the calculation process has a very high accuracy requirements, knowledge and cognitive maps. For example, the difference between the contract and the customer's own contract templates for quick return ratio, and stressed the importance of the change of; rapid statistics on sales data and inventory systems with digital check, calculate rebates to channel agents to do timely rebate; the contents of business contracts in the financial system to quickly create a corresponding financial records, automatically fill in the relevant items and attach evidence; automatically collect tax in line with government policy documents to find businesses and support policies.
KGB (Knowledge Graph Builder) knowledge map engine is a self-developed knowledge map construction and inference engine, based on the foundation of Chinese lexical analysis on the use of grammar KGB achieve real-time and efficient knowledge generation, various types of knowledge can be extracted from unstructured text and to achieve the specified extracted from the table content. KGB and can define different actions, such as extraction operation, and can be all kinds of custom handlers. Using the knowledge map engine can extract the KGB to offer detailed information about the product, to facilitate the next step of data mining and map construction.
KGB knowledge map to achieve cross-cutting scalable.
Knowledge Mapping plants have a common map construction engine. Knowledge extraction, knowledge associated with the quality of the verification process does not rely on specific business knowledge, combined with the knowledge map construction needs of the user, the user can quickly build the knowledge map of the field.
KGB knowledge map enables intelligent knowledge quality verification
intelligent verification and validation of knowledge on a variety of errors and conflict mapping knowledge processing plants, and the knowledge base for real-time automatic updates to ensure the accuracy of the knowledge map.
KGB man-machine combination of knowledge map service
mapping knowledge-processing man-machine composition: 90% + 10% artificial machine, only need to provide corpus, you can quickly get corresponding knowledge map construction results.
KGB is now in the insurance knowledge map text knowledge extraction, knowledge professional contract, safety evaluation report, bidding documents knowledge extraction and verification, as well as listed companies on the market and other industries to carry out data analysis applications.