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Video Library

Semantic Arts (SemArts)

ESTES PARK GROUP:
We will be hosting Jessica Talisman, Senior Information Architect at Adobe this May for Semantic Arts, Inc.'s Estes Park Group, a non-salesy monthly virtual community gathering where guest speakers are invited to share lessons learned from their work developing data-centric architectures.

This session will be co-moderated by Steve Case and Joaquin Melara.

ABOUT THE SESSION:
"Change Management through The Ontology Pipeline"

Organized, structured, and semi-structured semantic knowledge systems are finally being recognized as a core foundation for performant LLM and AI systems. Yet technologists— who are not accustomed to thinking through a holistic socio-technical perspective— often dismiss semantic knowledge systems as overly simplistic and flat, consisting of little more than text labels or annotations.

Business stakeholders may view the investment in developing semantic knowledge graphs as labor-intensive, complex, and a luxury rather than a competitive advantage. This leads organizations to deploy watered-down, off-the-shelf taxonomies or ontologies that only partially meet domain or business needs.

However, a semantic knowledge-graph system is essential for all subsequent AI development— data products, machine-learning algorithms, generative AI/LLM implementations, and more. You can’t build a skyscraper on sand—and you can’t build enterprise AI on top of bad data.

KEY TOPICS:
- The growing recognition of semantic knowledge systems as foundational for high-performance LLM and AI.
- Technologists’ misconceptions—viewing semantic systems as simplistic, label-only artifacts.
- Business stakeholders’ skepticism—seeing knowledge-graph investments as costly, complex, and nonessential.
- The necessity of robust semantic knowledge graphs as the data foundation for all AI initiatives.

LINK TO ARTICLE:
https://moderndata101.substack.com/p/the-ontology-pipeline

ABOUT JESSICA TALISMAN:
Jessica Talisman has dedicated her career to exploring the dynamics of information and knowledge, and how information flows across systems. Her experiences range from historical contexts to educational frameworks and extends to the realms of artificial intelligence and its application within enterprise knowledge systems. She is an active contributor in codifying knowledge management techniques, founded in the discipline of library science. As a librarian, she evangelizes the importance of high-quality data as a prerequisite to rendering truthful and reliable AI systems. Currently, she is a Senior Information Architect at Adobe, building semantic knowledge graphs for content and context setting. She believes in the importance of bridging the gaps between library science and data management, to enable robust knowledge management systems that can support innovation in AI.

Change Management Through The Ontology Pipeline with Jessica Talisman

Working with ontologies is the only way to overlay a common meaning and structure onto your data infrastructure and AI systems. However, when ontologies are modeled, there can be two extreme levels of abstraction that prevent the immediate realization of value and maintainability.

1- DOMAIN-SPECIFIC ONTOLOGIES: In the first case, an ontology is developed around very low-level, concrete domains—say, for example, project management. As such, these ontologies are very precise in defining what projects, tasks, resources, and other pertinent entities/relationships are within the project realm. However, they do not take into consideration a global context of how project management is related to HR, Finance, or Accounting. Therefore, extending these ontologies beyond one or two domains may prove to be cumbersome and confusing without some common terminology between domains.

2- HIGH-LEVEL ONTOLOGIES: The considerations above lead to the use of an upper ontology to mitigate the localized expression of common concepts. However, there are upper ontologies that are so abstract and distant from the data that they are hard to understand and communicate and therefore do not readily act as an effective bridge between domains.

At Semantic Arts, Inc. we have developed an open-source upper ontology called gist that meets the Goldilocks level of abstraction, where you can begin with a common model of the world using easily understood concepts that you can easily extend into localized domains.

Join us (Rebecca Younes, Dylan Abney, and our other ontologies alongside Joaquin Melara & Steve Case) on March 20th in the gist Forum to ask any questions about best practices for working with the upper ontology.

About gist Forum:
We maintain an active gist community where practitioners and users of gist come together to discuss the gist model, implementation best practices, and evolution. Virtual meetings take place the third Thursday of each month.

About gist:
gist represents the fundamental concepts and relationships that exist for most business use cases and is specifically designed to be domain independent. This flexibility allows gist to be applied to a wide spectrum of domains and facilitates both interoperability and integration of knowledge. We have designed gist for clarity and completeness to cover nearly all the concepts that exist in real-world ontology development.

gist is an upper-level ontology containing a minimal set of concepts required for most business applications. It has been developed and maintained by Semantic Arts since 2012. The gist ontology is free to use and is distributed under the Creative Commons CC BY 4.0 license. You can use it as you see fit for any purpose, as long as you give us attribution and keep it in the gist namespace.

gist Forum: March 20th 2025

ESTES PARK GROUP:
We will be hosting Jesse Anderson, Managing Director at Big Data Institute this June for Semantic Arts, Inc.'s Estes Park Group, a non-salesy monthly virtual community gathering where guest speakers are invited to share lessons learned from their work developing data-centric architectures.

This session will be co-moderated by Steve Case and Joaquin Melara.

ABOUT THE SESSION:
"Building a Delivery Team for Data-Centric Transformation"

Building a delivery team is a critical step in achieving success with data-centric transformation. Most big data projects fail not because of flawed technology, but due to poor management and misaligned team structures. Success requires a clear understanding of the three essential teams—data science, data engineering, and operations—and how each contributes to the overall project. These teams must be properly resourced, clearly defined, and able to work together efficiently to avoid common pitfalls and underperformance. Making use of data is a team sport; it takes a diverse set of skills, roles, and collaboration across the organization to drive meaningful results and cost-effective outcomes.

KEY TOPICS:
- Clarity in change management
- The importance of developing cohesive teams
- Managing team structures

ABOUT JESSE ANDERSON:
Jesse Anderson is a Data Engineer, Creative Engineer, and the Managing Director of the Big Data Institute. From nimble startups to established Fortune 100 companies, Jesse mentors organizations around the world on Big Data, leveraging cutting-edge technologies such as Apache Kafka, Hadoop, and Spark.

A true innovator in the field, Jesse’s groundbreaking teaching methods have earned him recognition from industry leaders and coverage in renowned publications including The Wall Street Journal and Harvard Business Review.

Building a Delivery Team for Data-Centric Transformation with Jesse Anderson

"gist and Data-Centric Architecture Beyond the Enterprise"

Join us (Rebecca Younes and Colton Glasgow alongside Joaquin Melara & Steve Case) on June 19th in the gist Forum to learn how gist is being applied beyond the enterprise.

Treasury of Lives maintains a digital native biographic encyclopedia of Tibet, Inner Asia, and the Himalaya. They were the recipients of a three-year NEH grant to transition their relationally-based website to Linked Open Data to enhance data sharing capabilities. While gist is described as a business-focused upper ontology, Rebecca Younes and Colton Glasgow will tell the story of leveraging gist to build an ontology of Tibetan Buddhism and Semantic Arts' DCA low-code/no-code platform to build a custom data curation platform on top of the resulting knowledge graph.

Why ontologies?:
Working with ontologies is the only way to overlay a common meaning and structure onto your data infrastructure and AI systems. However, when ontologies are modeled, there can be two extreme levels of abstraction that prevent the immediate realization of value and maintainability.

1- DOMAIN-SPECIFIC ONTOLOGIES: In the first case, an ontology is developed around very low-level, concrete domains—say, for example, project management. As such, these ontologies are very precise in defining what projects, tasks, resources, and other pertinent entities/relationships are within the project realm. However, they do not take into consideration a global context of how project management is related to HR, Finance, or Accounting. Therefore, extending these ontologies beyond one or two domains may prove to be cumbersome and confusing without some common terminology between domains.

2- HIGH-LEVEL ONTOLOGIES: The considerations above lead to the use of an upper ontology to mitigate the localized expression of common concepts. However, there are upper ontologies that are so abstract and distant from the data that they are hard to understand and communicate and therefore do not readily act as an effective bridge between domains.

At Semantic Arts, Inc. we have developed an open-source upper ontology called gist that meets the Goldilocks level of abstraction, where you can begin with a common model of the world using easily understood concepts that you can easily extend into localized domains.

About gist Forum:
We maintain an active gist community where practitioners and users of gist come together to discuss the gist model, implementation best practices, and evolution. Virtual meetings take place the third Thursday of each month.

About gist:
gist represents the fundamental concepts and relationships that exist for most business use cases and is specifically designed to be domain independent. This flexibility allows gist to be applied to a wide spectrum of domains and facilitates both interoperability and integration of knowledge. We have designed gist for clarity and completeness to cover nearly all the concepts that exist in real-world ontology development.

gist is an upper-level ontology containing a minimal set of concepts required for most business applications. It has been developed and maintained by Semantic Arts since 2012. The gist ontology is free to use and is distributed under the Creative Commons CC BY 4.0 license. You can use it as you see fit for any purpose, as long as you give us attribution and keep it in the gist namespace.

gist Forum: gist and Data-Centric Architecture Beyond the Enterprise

The Business Case for Managing Meaning

Michael Atkin has spent over four decades as a scribe and analyst for data management – from the foundations of the information industry to the adoption of semantic technology. In this presentation he summarizes what he’s observed, including the business rationale for data management, the mistakes companies make and why all roads lead to the adoption of semantic standards and graph capability.

The Business Case for Data-Centric: Challenges, Fixes, and Semantic Standards with Michael Atkin

ESTES PARK GROUP:
We will be hosting Chris Harding, CEO at Lacibus this April for Semantic Arts, Inc.'s Estes Park Group, a non-salesy monthly virtual community gathering where guest speakers are invited to share lessons learned from their work developing data-centric architectures.

This session will be co-moderated by Steve Case and Joaquin Melara.

ABOUT THE SESSION:
"Knowledge-Centric Architecture"

Knowledge graphs have emerged as a fundamental technology. Combined with large language models, they are driving the next generation of artificial intelligence. They should not be considered as transient artifacts, used for one purpose and then thrown away. Rather, they should be carefully curated, used repeatedly, and improved over time. They are a key asset and, following Data-Centric principles, they should be at the center of the applications.

KEY TOPICS:
- How to build Knowledge Graphs from text
- The difference between factual and conceptual knowledge
- How people can work with AI to curate a body of knowledge
- How to put the knowledge graph at the center of a microservices
architecture

ABOUT CHRIS HARDING:
Chris Harding, founder and principal of Lacibus, brings years of IT industry expertise to helping organizations unlock the potential of natural language processing (NLP). His background spans software engineering, team leadership, consulting, and standards development, consistently at the forefront of technical innovation. At Lacibus, Chris focuses on deploying practical NLP solutions to improve content management and knowledge organization, enabling clients to achieve new capabilities and enhanced efficiency.

Knowledge Centric Architecture with Chris Harding

ESTES PARK GROUP:
Welcome to Semantic Arts' Estes Park Group, a non-salesy monthly virtual community gathering where guest speakers are invited to share lessons learned from their work developing data-centric architectures.

Feel free to skip to 11:08 for the presentation: https://youtu.be/Sqi98tJNdkQ?feature=shared&t=668

ABOUT THE SESSION:
“How to Move Beyond a Monolithic Knowledge Graph to a Distributed Knowledge Mesh”

Creating an Enterprise Knowledge Graph is a complex challenge that involves the entire organization. A top-down approach, where a central team of information architects designs the ontology and then one or more data steward teams link it to the managed data assets, simply doesn’t scale. Centralized modeling teams quickly become a bottleneck, leaving most new data assets disconnected from the knowledge graph turning it into a cathedral in the desert.

In this talk, we’ll delve into the knowledge mesh, a new distributed, sustainable, and value-driven approach to building an enterprise knowledge graph incrementally, one data product at a time.

KEY TOPICS:
- Managing data as a product (data mesh)
- Managing knowledge as a product (knowledge mesh)
- Mapping data to knowledge (technical perspective)
- Mapping data to knowledge (organizational perspective)

ABOUT ANDREA GIOIA:
Andrea Gioia is a technical manager with a proven track record of over 15 years leading cross-functional teams on complex data projects. His career began with BI and DWH projects, and he has since embraced the advancements in big data, IoT, cloud, data governance, and AI. Andrea possesses a deep passion for technology and data and believes that the increasing centrality of data will drive significant advancements in data management, creating exciting opportunities for the future.

How to Move Beyond a Monolithic Knowledge Graph to a Distributed Knowledge Mesh with Andrea Gioia

Join us for a Semantic Arts, Inc. Estes Park Group community webinar featuring Paco Nathan, Principal Developer Relations Engineer at Senzing.

Paco will discuss how to leverage entity resolution to construct knowledge graphs for anti-fraud applications.

The event will be co-hosted by Steve Case and Joaquin Melara.

ESTES PARK GROUP:
Semantic Arts, Inc.'s Estes Park Group, is a non-salesy monthly virtual community gathering where guest speakers are invited to share lessons learned from their work developing data-centric architectures.

ABOUT THE SESSION:
"Catching Bad Guys: Leveraging Entity Resolution to Construct Graphs for Anti-Fraud" will demonstrate how to use graph technology to identify key players in financial fraud.

This session will use open datasets and an anti-fraud data model to illustrate how entity resolution merges data, creates entities and relations, and constructs a knowledge graph.

You'll learn how this approach enhances data quality and supports trustworthy AI applications, while also enabling evidence-based decision-making and auditing.

We'll explore design patterns for integrating an entity resolution service into data engineering practices and recommend community resources for further learning about investigative graphs and open data practices.

Catching Bad Guys: Leveraging Entity Resolution to Construct Graphs for Anti-Fraud with Paco Nathan

7- Developing Data-Centric Architecture using gist - Open QA

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Contact Us

Semantic Arts, Inc.
123 N College Avenue
Suite 218
Fort Collins, CO 80524

Email:
[email protected]

Phone:
(970) 490-2224

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