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Organize and structure enterprise data into an ontology based representation

One of the common business challenges of enterprises today is related to retention and reuse of enterprise knowledge. There is a definitive need for processes, methodologies and tools specifically built for representing and building enterprise knowledge to enable re-use by various business applications. Nia knowledge provides capabilities to build, consume and reuse enterprise knowledge across various business domains and functions within an organization. Nia Knowledge module of the Nia AI platform helps enterprises to organize information into an ontology-based knowledge base so that additional knowledge can be inferred and queried . The taxonomy is standardized, and content is organized for re-usability across various business domains within the enterprise.

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Based on the information sources of the problem domain and the expected inferences to be drawn from the knowledgebase, knowledge engineers along with domain experts can define the concepts, relationships and any applicable axioms required to represent the domain knowledge in the applicable context. Custom parsers based on the Nia Knowledge Adapter Framework can be built to ingest the data based on domain schema from the information sources. Nia knowledge also provides various capabilities related with consumption of knowledge such as answering queries through chatbot interfaces, browsing, search and visualization.

  • Re-use of knowledge across multiple contexts
  • Modular approach of knowledge modeling
  • Visualization of model and graph query results
  • Interactive query support with natural language interface

Typical Industry Challenges

  • Retaining and reusing SME knowledge across enterprise and business units
  • Rapidly evolving technologies that replace existing systems do not capture the domain knowledge aspects
  • Extremely difficult to decode, maintain and enhance knowledge base stored in legacy systems
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How Nia Knowledge Can Help

  • Enhance productivity by retrieving relevant and accurate information from the knowledge base and reduce communication overheads
  • Retain expert knowledge by representing them using various ontology-based knowledge modeling constructs