Triple Pattern Fragments: a Low-cost Knowledge Graph Interface for the Web

Introducing the Knowledge Graph: things, not strings

We hope this will give you a more complete picture of your interest, provide smarter search results, and pique your curiosity.

Santiago on Twitter / X

Knowledge graphs for representing information are unbeatable.After this, you will never build a RAG system without knowledge graphs.It will take you five lines of code to build a knowledge graph with your data.I recorded a video to show you how you can do this.I used… pic.twitter.com/1jQ6Gejczq— Santiago (@svpino) September 26, 2025

Knowledge Graphs

In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

Termboard - Knowledge Graphs Made Simple

Finally, knowledge graphs anyone can build.

GraphRAG: A Practitioner's Guide to 6 Advanced Architectural Patterns

Beyond basic graph retrieval: six production-oriented architectures for combining semantic search, knowledge graphs, and LLM reasoning.

Orrery, a Knowledge Graph That Adapts to Its Corpus

Orrery is our open-source knowledge graph that gets deeper as you feed it: domains that hit critical mass earn their own custom extraction specs, agents get an API, and humans get a galaxy. The whole …

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora

In the current era of information abundance, transforming vast amounts of unstructured data into structured, machine-readable knowledge remains one of the most significant challenges in artificial intelligence. Knowledge Graphs (KGs) have emerged as the cornerstone technology for this transformation Zhao et al. (2024), providing the semantic backbone for applications ranging from search engines and question answering Wu et al. (2024); Chen et al. (2024c); Zong et al. (2024); Sun et al. (2024b) to recommendation systems Lyu et al. (2024) and complex reasoning tasks Li et al. (2024b). Yet despite their critical importance, current KG construction approaches remain hampered by an inherent paradox: they require predefined schemas created by domain experts, which fundamentally limits their scalability, adaptability, and domain coverage.

Semantics and complexity of SPARQL

SPARQL is the standard language for querying RDF data. In this article, we address systematically the formal study of the database aspects of SPARQL, concentrating in its graph pattern matching facility. We provide a compositional semantics for the core part of SPARQL, and study the complexity of the evaluation of several fragments of the language. Among other complexity results, we show that the evaluation of general SPARQL patterns is PSPACE-complete. We identify a large class of SPARQL patterns, defined by imposing a simple and natural syntactic restriction, where the query evaluation problem can be solved more efficiently. This restriction gives rise to the class of well-designed patterns. We show that the evaluation problem is coNP-complete for well-designed patterns. Moreover, we provide several rewriting rules for well-designed patterns whose application may have a considerable impact in the cost of evaluating SPARQL queries.

Common Knowledge Machines

From Community Notes to Community Posts

Getting started - Docs

Discourse Graphs are a tool and ecosystem for collaborative knowledge synthesis, enabling researchers to map ideas and arguments in a modular, composable graph format.