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Semantic Knowledge for Artificial Intelligence

The Progress® Semaphore™ semantic platform helps you leverage your data for generative AI, fueling explainable, relevant and trustworthy outcomes.

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Why Semantic Knowledge Is So Important for a Trustworthy Enterprise AI

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Data in Context Drives AI Initiatives

Artificial Intelligence (AI), particularly generative AI and machine learning, has become a priority for businesses due to its ability to interpret data and enable smarter operations by leveraging large language models (LLMs) in critical applications and systems. However, these data-human interactions need to be regularly monitored to reduce hallucinations and data biases and facilitate more accurate output. Merging generative AI with semantic technologies and knowledge graphs can deliver value to digital ecosystems by applying human insight and context to data at a machine scale.

Conversational Agents

Document Q&A Systems

Semantic Enterprise Search

Content Generation

Semantic Retrieval Augmented Generation

The effectiveness of generative AI depends on the data it uses to generate results. Semantic Retrieval Augmented Generation (RAG) allows users to augment prompts by incorporating enterprise data, guiding the model to generate contextually relevant responses and reducing hallucinations and data biases for more precise AI outputs. Prepopulating generative AI's short-term memory with semantically relevant enterprise knowledge can enhance the results of AI systems, providing context, meaning and insight into the data used by AI.

Multi-Model Database Store and Query Documents​ 1 1 Ingest content as-is into a multi-model database, curate and harmonize it into the relevant model required for both the LLM and other downstream applications, making it extensible for future applications and services. 2 Semantically tag your private content using classification based on ontologies, taxonomies, entity and fact extraction— all within a semantic platform that helps you turn your content into a knowledge graph of your data. 3 Using a semantic platform, tag your queries against this knowledge graph and identify relevant content within your data, relating to the subject and context of the queries. 4 Create a prompt using our Augmented Prompt Generation approach, including a hybrid search against the semantic knowledge graph, increasing relevancy and accuracy for the user.​ 5 Pass this prompt to the generative AI and get an answer that is then re- validated against the knowledge graph and the reference documents to promote accuracy. Semantic Platform Knowledge Capture Generative AI 4 5 2 3 User Queries Ingest content as-is into a multi-model database, curate and harmonize it into the relevant model required for both the LLM and other downstream applications, making it extensible for future applications and services. 1 Ingest Content Semantically tag your private content using classification based on ontologies, taxonomies, entity and fact extraction—all within a semantic platform that helps you turn your content into a knowledge graph of your data. 2 Multi-Model Database Enterprise Private Documents Using a semantic platform, tag your queries against this knowledge graph and identify relevant content within your data, relating to the subject and context of the queries. 3 Semantic Platform Knowledge Capture Generate a prompt from relevant content, the semantic knowledge graph and the user’s query. 4 User Queries Pass this prompt to the generative AI and get an answer that is then re-validated against the knowledge graph and the reference documents to promote accuracy. 5 Generative AI

Business Benefits of RAG:

  • Achieves consistent results, facilitating trust in the generative AI
  • Improves accuracy, with unbiased data results and fewer hallucinations
  • Enhances results with the ability to change out generative AI models quickly and inexpensively
  • Enables human understanding, auditability and operational insights
  • Supports intuitive prompt creation and response understanding

Technical Benefits of RAG:

  • Feeds in semantically relevant enterprise data
  • Supports natural language feeding of the generative AI’s prompt, making it human readable
  • Functions independently of the generative AI model being used
  • Has tunable, use-case dependent relevancy
  • Adheres to enterprise standards for governance, lineage and provenance

Webinar

Leveraging Generative AI for Semantic Knowledge Modeling

Building knowledge models can be a daunting task. Combining generative AI with a human in the loop, using the powerful Semaphore technology, can make you more productive in the creation, enrichment and management of your semantic models.

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How Semaphore Supports Your Business

Progress Semaphore is a semantic platform that supports knowledge-centric architectures, enabling companies to use enterprise knowledge more effectively through data quality enhancement, data enrichment, data governance and knowledge management practices. These are critical for enabling AI models to deliver quality results.

Knowledge Governance

Semaphore is moderated and instructed by business users. SMEs are responsible for the management, development and maintenance of knowledge models over their lifetime. The models represent relevant knowledge in the language and vocabulary used by the business to provide qualified contextual data.

Data
Quality

Semantic metadata—data that is harmonized, enriched and extracted—provides a holistic view of all enterprise information, structured and unstructured, internal and external to the organization. It improves data quality, reduces noise and results in a higher precision of prediction, which is important since the quality of information affects outcomes.

Auditable  Outcomes

Semaphore provides a transparent approach for harmonizing information differences. As the semantic platform evaluates the information and makes decisions, the knowledge model and metadata provide a clear and explicit picture of the information used and actions taken in the decision-making process.

Repeatable  Outcomes

Semaphore provides precise, complete and consistent results. By leveraging a knowledge model and rule-based classification and fact extraction, information is processed consistently and decisions and outcomes are repeatable, transparent and fact-based.

Webinar

Human Insight at a Machine Scale with Graph Technologies

Modern enterprises are data-driven, and generative AI technologies need secure access to that enterprise data in a form that is sufficiently rich, real-time and accurate to deliver more trustworthy and quality results. Combine the semantic capabilities of Semaphore with the Progress® MarkLogic® platform for greater insights and more accurate, trustworthy answers.

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Related Resources

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Unleash the Power of Generative AI Systems in Your Enterprise
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Trustworthy AI with Semaphore

Semaphore can help you realize the potential of your data and get high-quality and trustworthy generative AI results. Get in touch to learn how we can help support your data needs.

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