Semantic Data Conference 2025

This week I attended the second annual conference “SemanticData: Taxonomy, Ontology, and Knowledge Graphs,” hosted by Henry Stewart (HS) Events and co-located with the HS DAM (Digital Asset Management) conference. I found this conference to be very worthwhile to attend, even without presenting, for its networking opportunities and ideas shared. As a one-day one-track-only conference, it had only 12 speakers, so I was not a speaker again this year, as I was last year, in order to let others speak.

Ideas of Semantics

Semantic data means enriching data with meaning from controlled vocabularies, especially taxonomies, and with meaningful relationships and specific attributes, provided by ontologies. Taxonomies and ontologies are referred to then as “semantic models.” A knowledge graph is a semantic model plus all of the connected data, which is stored in a graph database.

How “semantics” was discussed was up to each speaker. Jessica Talisman gave an overview of semantic models in what she describes as the "semantic pipeline.” In his talk on information ethics, Gary Carlson stayed high-level, stating “Semantics is about moving information from one place to another.” By contrast, Ashleigh Faith focused on the practical application of semantic tags to benefit AI. In his keynote, Ahren Lehnart spoke of the need to trust semantic models and concluded by focusing on the people, listing what “semantic professionals” do, including driving semantic adoptions within an organization, engaging with subject matter experts, seeking out and staying involved in AI projects, targeting high-risk semantic cases, and designing transparency into semantic models.

Turning to practice, Melissa Knudtson Monsalve explained the adoption of “just enough semantics” as a solution for organizations facing challenges of implementing semantic models. The conference also had some interesting case studies. Laura Rodriguez spoke about taxonomy governance strategies undertaken at HealthStream. Tracy Forzaglia explained the use of taxonomy and tagging at Scholastic. Mindy Carner explained the implementation of the DITA structured content standard in conjunction with a controlled vocabulary to manage and deliver Help Center content at LinkedIn. Finally, Dr. Robert Sanderson explained and demonstrated Yale’s LUX Collections Discovery utilizing a cultural heritage ontology and knowledge graph.

Comparisons with Semantic Data 2024

I had blogged about the first conference, Semantic Data 2024, last year. The format was the same: Individual half-hour presentations, the first as a “keynote”, a participant discussion activity, and a panel discussion moderated by the chair. By comparison, the conference was larger this year, up from about 50 attendees to about 70, making the room quite full. Aside from the chair and two of the sponsors, all but one of the speakers were also different this year from last. 

Madi Weland Solomon was again the conference chair and moderator, and Factor and Datavid were again sponsors with sponsored talks that were not promotional. Gary Carlson of Factor presented on the importance of data quality in semantic architecture, and Tim Padilla of Datavid presented on the AI-readiness of enterprise data. Progress Software was a new sponsor, but instead of a sponsored talk, Jim Morris of Progress spoke on the closing panel.

Panel: Solomon, Morris, Sanderson, and Faith

The theme of AI (especially generative AI and LLMs) was somewhat more prominent in the conference this year, taken up in almost half of the sessions. Ashleigh Faith’s talk, “How Semantic Tags Benefit AI,” was especially practical and informative. AI was woven through Ahren Lehnart’s opening keynote, when he discussed semantic trends and predictions. Tracy Forzaglia’s case study was about tagging with AI. Finally, the closing panel discussion had a focus on AI this time even in its title “Semantic Architects vs. AI: Who Curates the Future?” In fact, the conference could be title: “Semantic Data: Taxonomy, Ontology, Knowledge Graphs, and AI.” The importance of “human in the loop” with regard to AI and semantic automation was emphasized.

The “roundtable” group discussion members addressed questions of their organization’s semantic maturity, important changes in the past year, and what topics they would like to have addressed next year. This proved to be a popular session, although the large number of attendees required more time than allotted, and the room did not have tables. Perhaps a larger room or two tracks will be needed next year. I hope to participate next fall, if my schedule allows. Meanwhile, those of you in Europe may attend Semantic Data Europe on June 25, 2026, in London.