a Sensemaking AI series Semantic Web Course

Reading list

The annotated bibliography for the Sensemaking Semantic Web curriculum. Books, papers, W3C specs, and active practitioner voices. Organized by role, with one or two sentences of context per entry.

Last updated: Pre-launch, 5/23/26 · Companion files: tools.md · public-kgs.md

Contents

Required books

The curriculum repeatedly points to these. Acquire at least the first one before Module 1.

Allemang, Hendler & Gandon — Semantic Web for the Working Ontologist (3rd ed., ACM Books, 2020)

The canonical practitioner text. Allemang has spent two decades helping enterprises deploy this stack; the book is shaped by what he’s seen go wrong. The 3rd edition was substantially updated for the modern KG era.

If I only buy one book, I may buy this one.

DuCharme — Learning SPARQL (2nd ed., O’Reilly, 2013)

Slightly older but the conceptual content has not aged. DuCharme writes with unusual clarity for a technical book. The companion site provides every example as downloadable files so I can practice in my own SPARQL client.

The blog is worth following independently — DuCharme posts thoughtful pieces on SPARQL patterns and the LLM + KG intersection.

Open-access books

These are free, high-quality, and worth bookmarking even if not exactly read cover-to-cover.

Hogan, Blomqvist, Cochez, d’Amato et al. — Knowledge Graphs (Synthesis Lectures, Springer, 2021)

Eighteen of the world’s top KG researchers, single coherent treatment. Excellent companion to Allemang because it covers property graphs and embedding-based methods alongside the semantic web stack — giving us a unified vocabulary across both traditions.

Used for Module 1 (sections 3-4), Module 2 (section 5), Module 3 (section 6), and optionally Module 4 (sections 7-10).

Heath & Bizer — Linked Data: Evolving the Web into a Global Data Space

Shorter than the others. The four Linked Data principles, URI design considerations, and the history of how the linked-data vision developed. Read once for context; I may not return to it constantly.

Labra Gayo, Prud’hommeaux, Boneva & Kontokostas — Validating RDF Data (2018)

The open-access book on SHACL and ShEx. Useful when Module 3’s SHACL work gets serious. Both validation languages are covered; SHACL is what the curriculum uses.

W3C specifications

The W3C specs are surprisingly readable — don’t skip them just because they’re specs. The Primers are deliberately written for newcomers; the spec documents themselves are reference material.

RDF foundations

Querying

Schemas and ontologies

Constraints and provenance

Vocabularies I’ll reuse

Key papers

Worth reading for context on the modern KG + LLM intersection.

Edge, Trinh, Cheng, Bradley, Chao, Mody, Truitt, Larson — From Local to Global: A Graph RAG Approach to Query-Focused Summarization (Microsoft, 2024)

The canonical reference for the GraphRAG pattern: build a knowledge graph from text, summarize communities, use both for retrieval. Important reading before Module 4’s capstone.

Berners-Lee — Linked Data design note

The original four-rule articulation of Linked Data principles from 2006. One page. Worth reading once for historical context.

Berners-Lee, Hendler & Lassila — The Semantic Web (Scientific American, May 2001)

The original vision article. Now mostly of historical interest, but useful for understanding the gap between what was promised and what materialized. Helps to sound senior when discussing why the field hasn’t conquered the web the way it was supposed to.

Practitioner voices

The semantic web field is small enough that following a handful of practitioners gives good coverage of what’s happening.

Bob DuCharme

Author of Learning SPARQL. Active blog with consistently thoughtful posts on SPARQL patterns, KG + LLM integration, and the practical mechanics of working with this stack.

Dean Allemang

Co-author of Semantic Web for the Working Ontologist. Co-maintains the Financial Industry Business Ontology (FIBO). Writes on LinkedIn and at workingontologist.org.

Juan Sequeda

Chief Scientist at data.world. Writes accessibly about the LLM + KG intersection. Particularly interesting work on benchmarking LLM accuracy with vs. without knowledge graph augmentation.

Kurt Cagle

Opinionated, prolific, occasionally Forbes. Useful for the take-the-temperature-of-the-field perspective.

Reading order by module

Pre-week

Module 1 — Foundations

Required:

Optional:

Module 2 — Modeling

Required:

Optional:

Module 3 — Reasoning at the edge ★

Required:

Optional:

Module 4 — Shipping

Required:

Optional:

Syllabus · README · Resources: tools.md · public-kgs.md