Knowledge-Based Systems and How APIs Can Drive Their Adoption

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A Knowledge Based System (KBS) is an AI system that attempts to capture the knowledge of human experts to aid in decision making. The Object Management Group (OMG), an international technology standards group, recently released Beta 1.0 of its specification of Application Programming Interfaces for Knowledge Platforms. A consistent interface between client applications, knowledge resources and platforms such as editing tools or repositories and crawl engines is described in the new API. According to experts, developers will be able to integrate KBS into the enterprise architecture, ensuring that business plans are defined effectively using technology.

Advantages of KBS

KBS offers various advantages over traditional IT systems. They provide good documentation while intelligently handling huge amounts of unstructured data. Rules-based expert systems were the first KBS. A KBS can help users make better decisions by enabling them to operate at higher levels of knowledge, productivity and consistency. Likewise, KBS is also useful when knowledge is not available or when data needs to be effectively saved for future use. It also provides a single platform for the integration of knowledge at scale. Finally, by using the stored data, a KBS is able to generate new knowledge.

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Components of KBS

  • Knowledge Base: The accumulation, transfer and translation of expert problem solving skills and / or documented knowledge sources into a computer program to develop or increase the knowledge base is known as acquisition knowledge.
  • Inference engine: It works as an interpreter, analyzing and processing rules. Its mission is to match the antecedents of user responses and rules of shooting.
  • Knowledge Acquisition: The accumulation, transfer and translation of problem solving skills from experts and / or documented knowledge sources into a computer program for the purpose of developing or increasing the knowledge base is known as the name of knowledge acquisition.

The roles of an inference engine

An inference engine is used as a reasoning system in KBS. In many ways, inference engines were the origin of today’s personal computing, as they provided access to expert knowledge and solutions to problems. In order to analyze and process new data, inference engines provide logical rules based on existing knowledge bases. These engines can handle large amounts of data in real time, giving users access to the most recent data. Inference engines can be used to categorize data or to update data as it is analyzed. SL5 Object and CLIPS are the most widely used technologies for building KBS.
Likewise, OMG Application Programming Interfaces provide a uniform abstraction layer for developers to simplify the access, manipulation and assembly of knowledge artifacts, as well as their deployment and processing using analyzes. It allows developers to design knowledge graphs and incorporate them into larger AI-based business applications. Rather than replacing existing knowledge standards, the specification complements and links them. KBS can be used in various situations. This OMG API, in particular, could be an initiative of KBS. More notable achievements in KBS will soon be brought by Indian researchers and startups. Many more will be released in the near future.


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Dr Nivash Jeevanandam

Nivash holds a doctorate in information technology. He worked as a research associate in a university and as a development engineer in the computer industry. He is passionate about data science and machine learning.


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