This article proposes the DataBook, a design pattern that utilizes Markdown to bridge the gap between large-scale RDF knowledge graphs and small, ephemeral, task-specific semantic content. By combining YAML frontmatter for metadata, inline identifiers for addressability, and typed fenced code blocks for data payloads, DataBooks create self-describing and portable semantic artifacts. The authors argue that this approach allows for a microdatabase model where structured data can exist without the overhead of a full triple store.
Key points include:
The use of Markdown as a substrate for semantic infrastructure.
Defining the microdatabase for small-scale, non-indexed knowledge work.
Inverting the LLM role to act as a transformation engine within a DataBook pipeline.
Implementing provenance through process stamps in YAML metadata.
Managing complex dependencies via manifest DataBooks and build graphs.
Supporting secure data transfer through designed-in encryption profiles.
Key points include:
The use of Markdown as a substrate for semantic infrastructure.
Defining the microdatabase for small-scale, non-indexed knowledge work.
Inverting the LLM role to act as a transformation engine within a DataBook pipeline.
Implementing provenance through process stamps in YAML metadata.
Managing complex dependencies via manifest DataBooks and build graphs.
Supporting secure data transfer through designed-in encryption profiles.
Dr. Ora Lassila is a Principal Graph Technologist at AWS, working within the Amazon Neptune team with a primary focus on knowledge graphs. Throughout his extensive career, he has held significant roles, including Managing Director at State Street and positions at Nokia Research Center and HERE. A recognized pioneer in his field, he co-authored the original W3C RDF specification and the seminal article on the Semantic Web. His professional expertise covers AI, ontologies, the Semantic Web, RDF, and Knowledge Representation. In addition to his technical contributions, he is an enthusiast of aviation photography and scale modeling, even applying knowledge graph technologies to manage his aviation photography business, So Many Aircraft.
This article explores how to represent sentences as graphs, moving beyond traditional semantic modeling to a more natural-language oriented approach using reification and context graphs. It demonstrates how to translate sentences into RDF, Turtle, Open Cypher, and JSON-LD, highlighting the benefits of reification for capturing nuanced information and creating cleaner, more intuitive knowledge representations.
An exploration of SHACL 1.2 UI and its potential for creating forms and views, drawing parallels to the earlier XForms technology. The article discusses the benefits of declarative UI generation, dynamic properties, and security features.
The article explores SHACL 1.2 UI as a powerful, declarative approach to building forms and views for RDF data, drawing parallels to the earlier (and ultimately unsuccessful) XForms standard. The author argues that SHACL 1.2 UI offers benefits like consistent data presentation, automated form generation, dynamic property computation, and enhanced security, potentially revolutionizing how we interact with data on the web. While current tooling is limited, existing DASH-compatible tools can be adapted, and the author envisions a future where data itself dictates its presentation, reducing the need for costly and inconsistent manual form creation.
The article explores SHACL 1.2 UI as a powerful, declarative approach to building forms and views for RDF data, drawing parallels to the earlier (and ultimately unsuccessful) XForms standard. The author argues that SHACL 1.2 UI offers benefits like consistent data presentation, automated form generation, dynamic property computation, and enhanced security, potentially revolutionizing how we interact with data on the web. While current tooling is limited, existing DASH-compatible tools can be adapted, and the author envisions a future where data itself dictates its presentation, reducing the need for costly and inconsistent manual form creation.
An exploration of the role of an ontologist, covering skills, tasks, differences from taxonomists, training resources, and the future of the field.
This article details a step-by-step guide on building a knowledge graph from plain text using an LLM-powered pipeline. It covers concepts like Subject-Predicate-Object triples, text chunking, and LLM prompting to extract structured information.
The article explores how Retrieval-Augmented Generation (RAG) and knowledge graphs can be used together to break down data silos and enable more accurate, context-aware, and insightful AI systems.
python ./ontology_viz.py -o test.dot test.ttl -O ontology.ttl
