“Everywhere one seeks to produce meaning, to make the world signify, to render it visible. We are not, however, in danger of lacking meaning; quite the contrary, we are gorged with meaning and it is killing us.” – Jean Baudrillard, Simulacra and Simulation

I attended the annual Henry Stewart Semantic Data New York Conference for its third year on October 1st. Like last year, I’m using my monthly blog to perform a thematic roundup of the talks at the conference.

While of course the focused topic across much of the conference was AI, there has been a gradual shift from the uncertainty and lack of clarity brought about by rapid change and development to an acceptance that AI is here to stay. More practical use and evidence over the past several years has underscored the need for semantic practices paired with AI work of all kinds. Despite an ongoing lack of certainty as AI continues to rapidly evolve and find its place in the enterprise, there are more practical activities to create foundational semantic bedrock for AI endeavors.

Cross-System Alignment

One of the foundational principles of semantic data is to create a common language and domain model across all content. Many of the presenters emphasized how essential this is in the era of AI, particularly when it comes to aligning truth across systems in a semantic layer. Regardless of the system’s purpose, data, content type, or format, using taxonomies and ontologies to rationalize information across systems is essential for source of truth information for AI. A benefit of cross-system alignment is a cross-system and cross-source knowledge graph. The semantic layer of meaning created in taxonomies and ontologies paired with the things themselves containing data and knowledge allows humans and AI agents to find information grounded by semantic models.

A common theme across the conversations was how little has changed even as so much has changed. The rapid rise of AI is mind-blowing. Still, organizations suffer from poorly managed legacy data and systems requiring reconciliation through semantic practices to be AI-ready. Speakers covered multiple, disjointed taxonomies or metadata schemas, “islands” of differently governed information, and fragmented system landscapes that may perform in isolation but could not deliver as an organizational knowledge base for AI initiatives.

As always, cross-system alignment requires people working together across teams and functions to build common taxonomies and ontologies, integrate systems where possible, and automate processes that can be automated. While a semantic layer can provide a common truth, the passage of metadata and data across systems is still a common use case, so integrating systems and aligning to a common source of truth is essential.

Another common message that has not changed with AI is the use of semantic standards. Many of the speakers were using open standards like W3C’s RDF, SKOS, OWL, SPARQL, and SHACL, and other metadata and content representation standards. Interoperable standards is yet another foundational layer allowing organizations to move from AI proof of concepts to production-ready releases.

Deterministic Semantics

Semantic data practitioners agree using taxonomies and ontologies provides a foundational model for all types of data and content across the organization. Semantics are also key for preventing “hallucinations” in large language model (LLM) chatbots. When an LLM hallucinates, it’s not so much making up an answer out of thin air as providing an answer based on probability. At some point, if the LLM has very little contextual information, the most probable answer it provides is likely to be far off the mark. LLMs are great at text summarization, identifying and extracting new concepts, and automatically suggesting classification metadata, but they’re not very good at answering questions involving domains not covered or crossing between multiple documents or datasets to arrive at an answer for more complex questions.

In contrast, taxonomies and ontologies are deterministic. They are constructed at a slower pace and represent encoded truths in the domains they cover. If the data is not in the graph, then that information has no coverage. If the data is in the graph, then it can be contextually true if it is modeled correctly. Using a hybrid approach of deterministic semantic models and probabilistic AI results in much higher answer accuracy. Strictly deterministic use cases are often when the data or content structure is very well known, regular, and homogenous. In these cases, deterministic models and classifications are repeatable and can have high confidence in accuracy.

Another consideration was the nature of the work and industry. Speakers and attendees represented a broad range of industries, including retail, pharmaceuticals, libraries and museums, financial, and music and entertainment. For some industries, there can be general annoyance or reputational damage if chatbots answer information incorrectly. In other highly regulated industries, the wrong answers could lead to serious legal and financial repercussions or even severe problems for consumers or product users. While no organization wants to have their customers not trust them, the severity of the breach of trust matters. Therefore, accuracy in chatbot responses and recommendations is essential.

Knowledge and Change Management

Despite the dominance of AI in the conversation, the role of people is still ever-present and important to making organizations function. No matter how much knowledge LLMs have, it is not the experiential tacit knowledge of humans. Also, there are knowledge processes simply not captured at all in documentation: what decision was made, who made it, why was it made, and what uncaptured tacit knowledge helped to shape and form the decision? Meeting notes may capture a lot of information, especially with AI note takers in virtual meetings, but there is still unspoken knowledge feeding into decision-making. Human imperfection also leads to challenges in converting tacit knowledge to explicit, and structured, information. People, with or without the help of chatbots, create inconsistent and incomplete knowledge documents. Thus, there remains inconsistent word choice and descriptions still needing to be rationalized to fit into the taxonomy and ontology semantic representations.

Many of the speakers hit on change management as an essential, if often the most difficult, aspect of semantic practices across an organization. Adopting new systems, new ways of handling data in graph format, and agreeing on terms and definitions is still one of the most difficult tasks of anyone working in semantic data.

“Trust” was a word and a theme appearing across presentations and panel discussions. Users need to trust the data so they can trust the output of AI. Users trust what they know, however, so arriving at consensus across terminology, lines of work, and systems is feasibly still a greater challenge than the technical implementation of semantic structures and technologies.