Schema.org: Evolution of Structured Data on the Web

By Ramanathan V. Guha, D. Brickley, Steve Macbeth. Queue, 2015

455 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Croissant: A Metadata Format for ML-Ready Datasets (2024, unknown): “Croissant is organized around the following four layers: 1 { "@type": "sc:Dataset", 2 "name": "PASS", 3 "dct:conformsTo":"http://mlcommons. org/croissant/1.0",”
Transfer Learning for Neural Semantic Parsing (2017, methodology): “While the EviMRL is aligned with an internal schema speciﬁc to the knowledge base (KB), the AlexaMRL is aligned with an RDF-based open-source ontology (Guha et al., 2016).”
Explaining Trained Neural Networks with Semantic Web Technologies: First Steps (2017, methodology): “able, and that the amount of such structured data on the Web is in fact constantly growing [5,18]. Prominent examples of large-scale datasets include Wikidata [22] and data coming from the schema.org [10] eort which is driven by major Web search engine providers. We will utilize this available data ”