With the explosion of ChatGPT out of the circle
Artificial intelligence once again ushered in a small climax of development
So as the mainstream technology in the previous search field,
What is the future of the knowledge map?
In fact, ChatGPT is not "universal". As a black box model, ChatGPT is difficult to verify whether the generated knowledge is accurate. Moreover, ChatGPT performs reasoning through a probabilistic model, which also makes it difficult to truly implement in fields that require high knowledge accuracy.
In contrast, Knowledge Graph, as a data model for describing complex knowledge, is essentially a semantic network, and its main purpose is to describe various entities and the relationship between entities in the real world. This also determines its unique advantages in terms of knowledge interpretability and accuracy.
Today, let's dig deeper into the knowledge map:
The knowledge map was first applied in the search field, which can be traced back to around 2012, mainly to solve the user experience problems of search engines.
for example:
In the traditional search mode, users can only see a few webpage links involving keywords, but with the technical support of the knowledge graph, the user will first see the relevant answer on the search interface. The returned sentence is to analyze the search results through the knowledge graph owned.
In the traditional search mode, after VS knowledge map is added
In addition to the search field, knowledge graphs are now also used in intelligent recommendation, intelligent question answering, and decision-making platforms. In recent years, knowledge graphs have begun to be applied in the financial field, gradually becoming the main means of risk control and anti-fraud in the financial field, and continue to expand to other businesses.
So, how is the knowledge graph constructed?
What are the reliable knowledge graph platforms in the current financial market?
The construction process of the knowledge graph includes three parts: graph design (designing the entity-relationship network), graph construction (importing data into the entity-relationship network) and graph fusion (requiring data fusion for data from different sources). Knowledge reasoning and analysis algorithms can be used for more in-depth data mining work.
However, when implementing and applying knowledge graphs, the following difficulties are often encountered:
▪️ Multiple data types
▪️ Map design is difficult
▪️ The cost of graph construction and update is high
▪️ Map application is difficult
CLP Jinxin: Don’t panic, I will make a move!
CLP Jinxin Whale Map Knowledge Graph Platform is a one-stop knowledge graph construction and service platform, which is specially designed for the broad business needs in the financial field, and can provide the whole process from text data labeling, knowledge extraction, knowledge fusion, graph storage and graph analysis ability.
Compared with other conventional knowledge graph platforms in the financial field, the Whale Map knowledge graph platform:
01 Provide knowledge map construction and service platform
The one-stop construction platform supports users to simply and quickly build various business knowledge graphs to realize business value. The platform provides full-process capabilities from source data management, map schema design, map construction (knowledge extraction, knowledge fusion, etc.), map storage, map management, and map visualization and analysis. Various business graphs. Based on this platform, relevant function enhancement development can be carried out according to the usage needs of customers' actual scenarios.
02 Map construction is simple and flexible
Provides a variety of construction methods, including mapping construction, extraction construction, etc. The model of linkage construction can effectively reduce the workload of architecture by more than 25%.
03 Strong natural language processing capabilities
Through active learning and other methods, the amount of text data labeling can be reduced by more than 30%. A variety of data extraction methods and knowledge fusion solutions are embedded in the platform, and 18 kinds of NLP algorithms are built in. Based on its efficient base model, it can effectively support business domain switching.
04 Excellent analytical capabilities to effectively support business needs
The platform is embedded with 30+ analysis algorithms in five categories, which can be applied to more than ten scenarios. Its built-in analysis algorithm can cover most of the commonly used business scenarios at present, including group faction identification, risk event transmission, commodity correlation, guarantee chain identification, graph question-and-answer reasoning, etc. In addition, it can also provide algorithm extension and customization services according to actual business needs, and can adapt to complex and demanding application scenarios.
In addition, the Whale Map Knowledge Graph can also handle billions of data and relationships, using expert-designed graphs and embedded graph analysis algorithms, allowing users to complete relationship analysis with just one click. The platform deploys the overall service framework in the customer environment, follows the principle of high availability, and covers functions such as a complete log system, abnormal monitoring and alarming, policy recovery, and cluster disaster recovery .
At present, the Whale Map knowledge graph has been applied in several scenarios, such as corporate risk control, risk transmission analysis, and discovery of invisible capital transaction relationships. It has provided stable and reliable services for financial institutions and successfully helped customers achieve business improvement.
At the just-concluded 2023 WAIC World Artificial Intelligence Conference, China Electronics Financial Trust's "Bank Intelligent Audit Application Based on Knowledge Graph" was also selected as the "General Artificial Intelligence Innovation Application Case Collection" .
In the future, CLP Jinxin Whale Map products will follow industry standards
Extend existing analysis and extraction algorithm capabilities
Establish a bank-wide knowledge map application platform
Promoting the digital transformation of the financial industry
