Creating a Knowledge Graph

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Which can be used in a variety of ways, for example in Text mining in medical articles, related keywords will be searched to bring knowledge to create further research, or in the AI ​​field, knowledge graphs will be used. Instead of the product part, for example, shirts are related to polo shirts. There is a relationship with the arm and color by sending information so that customers can actually search for information about related products or use applications on various websites in the form of Q&A to be smarter and support information more quickly. The main elements that we use in conjunction with Knowledge Graph analysis in actual use consist of 4 parts: Input data: Data that is imported for analysis. It can be Strutured, Unstructured, image data, or Streaming data. LLM: This part will help in doing Knowledge Extraction or cutting words before analyzing their connections with a knowledge graph. Knowledge Graph.


Used to analyze the underlying relationships among features Whatsapp Number List tokenized in the LLM process. Semantic Insights: is the process of finding relationships in meaning. (not just feature mapping) within each feature, the results will be displayed in the form of Q&A or BI Dashboard, etc. Understanding Knowledge Graph: The theory behind Social network analytics source:  Nik would like to give an example of data that can be used to create a good Knowledge graph, that is, structured data or Relational database. But if the data is in the form of Unstructured data, such as images and VDO, it must be It's even more difficult to prepare information, friends.Which can be used in a variety of ways, for example in Text mining in medical articles, related keywords will be searched to bring knowledge to create further research, or in the AI ​​field, knowledge graphs will be used. Instead of the product part, for example, shirts are related to polo shirts. There is a relationship with the arm and color by sending information so that customers can actually search for information about related products or use applications on various websites in the form of Q&A to be smarter and support information more quickly.



The main elements that we use in conjunction with Knowledge Graph analysis in actual use consist of 4 parts: Input data: Data that is imported for analysis. It can be Strutured, Unstructured, image data, or Streaming data. LLM: This part will help in doing Knowledge Extraction or cutting words before analyzing their connections with a knowledge graph. Knowledge Graph: Used to analyze the underlying relationships among features tokenized in the LLM process. Semantic Insights: is the process of finding relationships in meaning. (not just feature mapping) within each feature, the results will be displayed in the form of Q&A or BI Dashboard, etc. Understanding Knowledge Graph: The theory behind Social network analytics source:  Nik would like to give an example of data that can be used to create a good Knowledge graph, that is, structured data or Relational database. But if the data is in the form of Unstructured data, such as images and VDO, it must be It's even more difficult to prepare information, friends.