Organizations are under pressure to anticipate the next layer of the technology stack while still making sense of the systems they already run. In AI programs, that challenge often becomes more difficult when metadata, taxonomies and ontologies are treated as optional documentation rather than core foundations. This session explains why these structures provide the scaffolding and guardrails AI systems need to produce useful, reliable and context-aware outputs.
The session will cover the foundational layers of AI implementation, where they sit within enterprise ecosystems and how they interact with existing data, content and application environments. It will examine different types of metadata, the role of taxonomies in organizing meaning and the value of ontologies in representing concepts, relationships and business context.
Attendees will also learn why AI does not remove the need for metadata discipline. Instead, well-managed semantic foundations help guide AI, reduce ambiguity and improve retrieval, classification and reasoning. The session will close with practical guidance on keeping these guardrails current as business language, systems and AI use cases evolve.
Organizations are looking for practical ways to unlock more value from their enterprise data while preparing for an AI-driven future. This session explores how Progress Data Platform makes data more accessible, actionable, and AI-ready through a combination of intelligent automation and AI-powered capabilities.
Using real-world scenarios, the session demonstrates how workflow automation streamlines data-driven processes, orchestrates enrichment and integration across systems, and reduces the effort required to operationalize data at scale. It also introduces the vision for Model Context Protocol (MCP) in MarkLogic, illustrating how AI agents and applications can securely interact with enterprise data using natural language and how these capabilities can be extended through custom tools and integrations.
The session highlights solutions available today alongside a preview of upcoming innovations designed to simplify development, improve operational efficiency, and expand access to data-driven insights. Together, these capabilities showcase how Progress Data Platform is evolving to accelerate AI adoption, enable self-service intelligence, and help organizations turn enterprise data into measurable business outcomes.
Decision automation is entering a new phase, where organizations need decisions that are not only consistent and explainable, but also better informed by trusted enterprise data and easier to evolve as business needs change. Progress Corticon provides deterministic, rules-based decision automation, while the Progress Data Platform supplies the governed data foundation, policy context, knowledge models, semantic enrichment, traceability and secure access patterns needed to support richer and more auditable decision services.
This session will explore how PDP leverages the Progress Corticon technology to strengthen established decisioning use cases such as eligibility, claims, compliance, routing, pricing, case handling and operational prioritization. It will also look at how agentic AI can help accelerate the creation and evolution of decision projects by assisting with rules, tests, documentation and supporting project artifacts. Rather than replacing rules-based systems, AI can reduce the effort required to build, update and maintain them, while business rules continue to provide the governance, repeatability and control required for critical decisions.
Attendees will leave with a practical understanding of how governed data, explainable rules and agentic AI can work together to modernize decision automation. The session will clarify the role of each capability: Corticon remains focused on trusted decision logic, PDP provides the data and knowledge fabric needed to operationalize better decisions, and agentic AI helps make decision automation faster, more approachable and easier to maintain without sacrificing trust, transparency or compliance.
A taxonomy cannot be judged only by its structure at launch. Its real value appears when users search, browse and interact with content in production. This session focuses on how to measure whether a taxonomy is genuinely improving findability after it has been deployed.
The session will move beyond coverage and depth metrics to examine runtime signals that reveal how users experience search. Attendees will learn how to use click-through rate, zero-result rate, reformulation rate, ranking position and relevance-judgment benchmarks to assess whether search quality is improving. Query logs will be treated as a diagnostic tool for identifying failing searches and turning them into actionable taxonomy improvements.
The session will also show how explicit and implicit feedback can be interpreted, how root causes can be traced from a failing query to a taxonomy change and how results can be remeasured after each update. Participants will leave with a practical measurement framework, a findability scorecard and a repeatable improvement loop for proving taxonomy value over time.
No prior Semaphore experience required Data is only as valuable as your ability to understand what it means not just what it says. In this hands-on session, you'll discover how organizations can bring order, meaning, and intelligence to complex enterprise data through the power of semantic modeling.
Whether you're a data architect designing enterprise knowledge structures, a taxonomist building controlled vocabularies, or a business user who simply needs to make sense of how your organization's concepts connect this session is built for you.
We'll start with a plain-language introduction to what semantic modeling is and why it matters in an AI-driven world. No prior knowledge of ontologies, taxonomies, or knowledge graphs required, just a willingness to think about data differently.
From there, you'll get hands-on, working through a guided scenario that takes you inside the process of mapping relationships between concepts, seeing firsthand how Semaphore helps you define not just what your data is, but what it means and how it connects.
By the end of the session, you'll understand how semantic models become the intelligence layer that makes AI applications more accurate, more trustworthy, and more aligned with how your business thinks.
Data is only as valuable as your ability to understand what it means not just what it says. In this hands-on session, you'll discover how organizations can bring order, meaning, and intelligence to complex enterprise data through the power of semantic modeling.
Whether you're a data architect designing enterprise knowledge structures, a taxonomist building controlled vocabularies, or a business user who simply needs to make sense of how your organization's concepts connect this session is built for you.
We'll start with a plain-language introduction to what semantic modeling is and why it matters in an AI-driven world. No prior knowledge of ontologies, taxonomies, or knowledge graphs required, just a willingness to think about data differently.
From there, you'll get hands-on, working through a guided scenario that takes you inside the process of mapping relationships between concepts, seeing firsthand how Semaphore helps you define not just what your data is, but what it means and how it connects.
By the end of the session, you'll understand how semantic models become the intelligence layer that makes AI applications more accurate, more trustworthy, and more aligned with how your business thinks.
No prior Semaphore experience required Data is only as valuable as your ability to understand what it means not just what it says. In this hands-on session, you'll discover how organizations can bring order, meaning, and intelligence to complex enterprise data through the power of semantic modeling.
Whether you're a data architect designing enterprise knowledge structures, a taxonomist building controlled vocabularies, or a business user who simply needs to make sense of how your organization's concepts connect this session is built for you.
We'll start with a plain-language introduction to what semantic modeling is and why it matters in an AI-driven world. No prior knowledge of ontologies, taxonomies, or knowledge graphs required, just a willingness to think about data differently.
From there, you'll get hands-on, working through a guided scenario that takes you inside the process of mapping relationships between concepts, seeing firsthand how Semaphore helps you define not just what your data is, but what it means and how it connects.
By the end of the session, you'll understand how semantic models become the intelligence layer that makes AI applications more accurate, more trustworthy, and more aligned with how your business thinks.
Data is only as valuable as your ability to understand what it means not just what it says. In this hands-on session, you'll discover how organizations can bring order, meaning, and intelligence to complex enterprise data through the power of semantic modeling.
Whether you're a data architect designing enterprise knowledge structures, a taxonomist building controlled vocabularies, or a business user who simply needs to make sense of how your organization's concepts connect this session is built for you.
We'll start with a plain-language introduction to what semantic modeling is and why it matters in an AI-driven world. No prior knowledge of ontologies, taxonomies, or knowledge graphs required, just a willingness to think about data differently.
From there, you'll get hands-on, working through a guided scenario that takes you inside the process of mapping relationships between concepts, seeing firsthand how Semaphore helps you define not just what your data is, but what it means and how it connects.
By the end of the session, you'll understand how semantic models become the intelligence layer that makes AI applications more accurate, more trustworthy, and more aligned with how your business thinks.
